SeqAPASS: A Bioinformatics Tool for Predicting Cross-Species Chemical Susceptibility in Drug Development and Toxicology

Abstract

This article provides a comprehensive overview of the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, a web-based application developed by the U.S. Environmental Protection Agency. Designed for researchers, scientists, and drug development professionals, the content covers the foundational principles of using protein sequence and structural conservation to extrapolate toxicity data across species. It details the methodological workflow from sequence alignment to protein structure modeling, explores practical applications through documented case studies, addresses common troubleshooting and optimization strategies, and validates the tool's predictions against empirical data. The article aims to equip scientists with the knowledge to efficiently utilize SeqAPASS for prioritizing chemical testing, selecting relevant model species, and supporting safety assessments in the context of reduced animal testing and New Approach Methodologies (NAMs).

Understanding SeqAPASS: The Foundation of Cross-Species Susceptibility Prediction

Human and ecological hazard assessment of chemicals has traditionally relied on toxicity data generated from a limited number of laboratory model species. However, regulatory agencies face the formidable challenge of extrapolating these limited data to thousands of diverse species of potential concern, with documented differences in chemical sensitivities ranging from several-fold to over a thousand-fold [1]. This challenge, combined with decreasing testing resources, growing international interest in reducing animal testing, and increasing demands to evaluate chemicals more rapidly, has created a compelling need for innovative, scientifically-based approaches to extrapolate toxicological data across taxa [2] [3].

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool emerged as a direct response to these challenges. Developed by the U.S. Environmental Protection Agency (US EPA) and first released publicly in 2016, SeqAPASS is a fast, freely available, online screening tool that enables researchers and regulators to extrapolate toxicity information across species by evaluating protein structural similarities and differences [3] [4]. The tool operates on the fundamental principle that conservation of a molecular target across species can serve as a critical line-of-evidence for predicting relative intrinsic susceptibility to chemicals that interact with that target [4].

The regulatory landscape has been rapidly evolving to support such approaches. In 2019, the US EPA Administrator issued a directive to eliminate mammalian regulatory and research studies completely by 2035, with associated funds allocated to develop alternative methods [2]. Similarly, the European Union's REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) regulations were amended in 2017 to establish animal testing as a last resort for filling data gaps [2]. These regulatory shifts have accelerated the development and adoption of New Approach Methodologies (NAMs) that can provide efficient, cost-effective toxicity evaluation while reducing reliance on whole-animal testing [2] [5].

Table 1: Key Regulatory Drivers for SeqAPASS Adoption

Regulatory Driver	Implementing Body	Key Provision	Impact on Toxicology
EPA Directive 2035 [2]	U.S. Environmental Protection Agency	Eliminate mammalian regulatory studies by 2035	Accelerates development of computational alternatives to animal testing
REACH Amendment [2]	European Union	Established animal testing as last resort for data gaps	Promotes use of alternative methods for chemical safety assessment
Cosmetic Testing Bans [2]	Multiple governments (EU, others)	Banned marketing of cosmetics tested on animals	Drives innovation in non-animal testing approaches

SeqAPASS Tool Design and Analytical Framework

The SeqAPASS tool was designed with flexibility to accommodate varying degrees of protein characterization, acknowledging that available information about chemical-protein interactions and molecular targets themselves can differ substantially [4]. To address this variability, the tool employs a tiered analytical approach consisting of three sequential levels of evaluation, each providing additional evidence for screening-level assessments of probable cross-species susceptibility [4] [1].

Tiered Analytical Approach

Level 1: Primary Amino Acid Sequence Comparison The initial analysis level compares the entire primary amino acid sequence of a query protein from a known sensitive species to all species with available sequence information in the National Center for Biotechnology Information (NCBI) protein database, which contains over 153 million proteins representing more than 95,000 organisms [3] [5]. Using BLASTp algorithms, the tool calculates metrics for sequence similarity and performs ortholog detection, establishing a foundational assessment of potential susceptibility across taxonomic groups [5] [4].

Level 2: Functional Domain Evaluation The second analysis level provides greater resolution by examining sequence similarity within specific functional domains, such as ligand-binding domains, rather than the entire protein sequence [5] [4]. This approach recognizes that chemicals often interact with specific protein regions rather than the entire protein structure, offering more precise predictions of susceptibility that can distinguish differences among broader taxonomic groups [1].

Level 3: Critical Amino Acid Residue Comparison The most granular level of analysis compares individual amino acid residue positions identified as critical for protein conformation, chemical binding, or other key functions [4] [1]. This highest-resolution evaluation can detect species-specific differences in chemical susceptibility that might be masked in broader sequence comparisons, potentially explaining dramatic differences in sensitivity observed between closely related species [1].

SeqAPASS Three-Tiered Analytical Workflow

Technical Evolution and Capabilities

Since its initial release in 2016, SeqAPASS has undergone substantial evolution through regular version updates, each introducing enhanced capabilities and features. The tool's development has been characterized by responsive adaptation to user needs and technological advancements [5].

Table 2: SeqAPASS Version Evolution and Key Features [5]

Version	Release Date	Key Features and Enhancements
1.0	January 2016	Initial public release with Level 1 and Level 2 analyses
2.0	May 2017	Added Level 3 amino acid residue comparisons
3.0	March 2018	Integrated interactive data visualization capabilities
4.0	October 2019	Added interoperability with ECOTOX Knowledgebase
5.0	December 2020	Introduced customizable heat map visualization
6.0	September 2021	Implemented widget connecting to ECOTOX empirical data
8.0	Recent	Added protein structure generation across species

The tool's interoperability with other data resources significantly enhances its utility for comprehensive assessment. SeqAPASS integrates with the CompTox Chemicals Dashboard, allowing results from ToxCast assay targets to be extrapolated across species [3]. Additionally, the ECOTOX Knowledgebase widget enables users to rapidly connect sequence-based predictions of chemical susceptibility to existing curated empirical toxicity data for terrestrial and aquatic species [5] [6].

Application Notes: Case Studies in Cross-Species Extrapolation

Endocrine Disruption Assessment Across Vertebrates

The Endocrine Disruptor Screening Program (EDSP) faces the challenge of evaluating over 10,000 chemicals for potential effects on the endocrine system across diverse species. SeqAPASS has been employed to determine the degree to which data generated for chemical activation in mammalian systems can be translated to non-mammalian vertebrates, including fish, amphibians, and birds [3]. By comparing the conservation of the human estrogen receptor across these taxonomic groups, researchers obtained critical information to prioritize testing and assess both human health and ecological risks of estrogenic chemicals, demonstrating how mechanistic data can be strategically applied to focus limited testing resources [3].

Insecticide Specificity and Pollinator Protection

The decline in honey bee colonies has raised significant concerns about ecosystem health and agricultural productivity. SeqAPASS has been utilized to evaluate the potential chemical susceptibility of honey bees and other insect pollinators by examining the conservation of protein targets like the nicotinic acetylcholine receptor [3] [4]. In a complementary application, the tool has helped explain the species selectivity of molt-accelerating insecticides by comparing the ecdysone receptor (EcR) between target pest species and non-target organisms [3] [4]. These analyses demonstrated that the EcR is well conserved among arthropods but exhibits sufficient sequence variation in specific functional domains to enable the design of insecticides that selectively target pests while minimizing impacts on beneficial insects [4].

Susceptibility Predictions for Threatened and Endangered Species

Protecting threatened and endangered species from chemical exposures presents particular challenges, as traditional toxicity testing is rarely feasible with these populations. SeqAPASS provides a valuable approach to address this data gap by predicting protein target conservation and potential chemical susceptibility for species of conservation concern [3] [5]. The tool's data visualization features include specific options to highlight threatened and endangered species, enabling regulators to incorporate these considerations into chemical risk assessments even when empirical toxicity data are unavailable [5].

Cross-Species Extrapolation Concept

Experimental Protocols

Protocol 1: Level 1 and Level 2 Analysis

Objective: To perform primary amino acid sequence comparison (Level 1) and functional domain evaluation (Level 2) for cross-species susceptibility prediction [5] [7].

Methodology:

• Access and Authentication

  • Navigate to https://seqapass.epa.gov/seqapass using Chrome browser
  • Login to existing account or create new SeqAPASS account (enables storage, access, and customization of completed jobs) [5]

• Protein Identification

  • Click dropdown buttons under "Identify a Protein Target" to access external resources
  • Identify query protein and sensitive species through literature review or pre-existing data
  • Select either "By Species" or "By Accession" under "Compare Primary Amino Acid Sequences" [5]

• Level 1 Analysis Initiation

  • Enter protein accession number or select species and protein
  • Click "Request Run" to initiate query
  • Monitor job status under "SeqAPASS Run Status" tab [7]

• Level 1 Data Interpretation

  • Access results under "View SeqAPASS Reports" tab
  • Select "View Report" for web browser display
  • Choose between "Primary Report" (condensed) or "Full Report" (expanded) format
  • Species receive "Yes" or "No" susceptibility prediction based on protein conservation relative to query sequence [5] [7]

• Level 2 Analysis Development

  • Click plus sign next to "Level Two" header on Level One Query Protein Information page
  • Click "Select Domain" box to populate functional domains list from NCBI Conserved Domain Database
  • Select appropriate domain and click "Request Domain Run" [7]

• Level 2 Data Visualization

  • Click "Refresh Level Two and Three Runs" to populate level two data
  • Under "View Level Two Data," select completed domain accession
  • Click "View Level Two data" button to access results [5]

Protocol 2: Level 3 Critical Residue Analysis

Objective: To compare individual amino acid residue positions of importance for protein-chemical interaction across species [5] [1].

Methodology:

• Literature Review and Residue Identification

  • Conduct comprehensive literature review to identify critical amino acid residues
  • Utilize Reference Explorer tool (accessed via plus sign next to Reference Explorer)
  • Generate predefined Boolean string to query available literature
  • Click "Generate Google Scholar Link" or "Search Google Scholar" to automate literature search [5]

• Level 3 Analysis Setup

  • Navigate to Level One Query Protein Information page
  • Click plus sign next to "Level Three Header" to populate Level Three Query Menu
  • Select template sequence for alignment comparison
  • Enter user-defined run name for job identification [7]

• Taxonomic Selection

  • Select taxonomic group in "Choose Taxonomic Group" box
  • Repeat for all taxonomic groups requiring conservation evaluation
  • Select desired species for alignment to template sequence
  • Click "Request Residue Run" [5]

• Level 3 Data Compilation and Interpretation

  • Click "Refresh Level Two and Three Runs" to populate completed jobs
  • View data by individual taxonomic group or combine multiple groups
  • Click "Combine Level Three Data" for multi-group analysis
  • Select level three template as comparison basis [7]

• Amino Acid Position Alignment

  • Enter previously identified positions into text box (comma-separated) or select from residue list
  • Click "Copy to Residue list" to shuttle residues into selection box
  • Click "Update Report" to align sequences with specified positions [5]

Data Synthesis and Visualization

Heat Map Generation for Level 3 Data:

• Click plus sign next to "Visualization" header and select "Visualize Data"
• Click "Heat Map" on visualization information page
• Select taxonomic groups for display under "Controls"
• Customize display options under "Report Options," "Optional Selections," and "Heat Map Settings"
• Click "Download Heatmap" to export visualization [5] [7]

Decision Summary Report Compilation:

• Click "Push Level to DS Report" from Results or Data Visualization pages
• Access combined analysis across all levels in "DS Report" tab
• Download comprehensive summary for cross-level susceptibility comparison [5]

Research Reagent Solutions

Table 3: Essential Research Materials and Computational Tools for SeqAPASS Analysis

Resource Category	Specific Tool/Database	Function in Analysis	Access Point
Protein Databases	NCBI Protein Database	Provides 153+ million protein sequences across 95,000+ organisms for comparative analysis	https://www.ncbi.nlm.nih.gov/protein [3]
Computational Algorithms	BLASTp (Protein Basic Local Alignment Search Tool)	Calculates primary amino acid sequence similarity metrics for Level 1 analysis	Integrated in SeqAPASS [5]
Domain Identification	NCBI Conserved Domain Database	Identifies functional protein domains for Level 2 analysis	Integrated in SeqAPASS [5]
Toxicity Data Integration	ECOTOX Knowledgebase	Provides curated empirical toxicity data for comparison with sequence-based predictions	https://cfpub.epa.gov/ecotox/ [5]
Chemical Prioritization	CompTox Chemicals Dashboard	Supports identification of protein targets and chemical interactions	https://comptox.epa.gov/dashboard [3]

SeqAPASS represents a transformative approach in modern toxicology, effectively addressing the critical challenge of cross-species extrapolation through innovative bioinformatics. By leveraging publicly available protein sequence information and applying a tiered analytical framework, the tool enables rapid, cost-effective predictions of chemical susceptibility across broad taxonomic groups. Its development and ongoing refinement reflect the evolving regulatory landscape that increasingly prioritizes mechanistically-oriented, animal-free testing methodologies. As a freely available, web-based application, SeqAPASS provides both researchers and regulators with a powerful platform to support chemical prioritization, inform species selection for testing, and advance the application of the Adverse Outcome Pathway framework—ultimately contributing to more efficient and protective chemical safety assessments for both human health and ecological systems.

The fundamental principle underlying cross-species chemical susceptibility hinges on the degree of conservation of specific protein targets with which chemicals interact. A species' intrinsic susceptibility to a particular chemical is largely determined by the presence and functional conservation of these protein targets, which, when bound, can disrupt vital biological processes leading to adverse effects on survival, growth, and reproduction [3]. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool translates this principle into an actionable, computational method. It is a fast, freely available, online screening tool that enables researchers and regulators to extrapolate toxicity information from data-rich model species to thousands of other species for which toxicity data is limited or absent [5] [3]. By evaluating protein sequence and structural conservation, SeqAPASS provides a scientifically robust framework for predicting relative intrinsic chemical susceptibility across the tree of life.

Background and Scientific Principle

The Molecular Basis of Chemical Susceptibility

At its core, the interaction between a chemical and a biological system is often highly specific. Many chemicals, including pharmaceuticals and pesticides, exert their effects—both intended and unintended—by interacting with specific protein molecules, such as receptors, enzymes, or transporters. The presence of a specific protein target, coupled with a sufficient degree of structural compatibility at the interaction site, is a primary determinant of a chemical's effect in an organism [3]. Consequently, the diversity of responses observed across different species to the same chemical can frequently be traced back to differences in the amino acid sequences of these protein targets. Even a single amino acid substitution at a critical position within a chemical-binding pocket can dramatically alter the binding affinity and, hence, the species' susceptibility [5].

SeqAPASS: From Principle to Practice

The SeqAPASS tool operationalizes this principle by leveraging the vast and publicly available protein sequence information from the National Center for Biotechnology Information (NCBI) database, which contains information on over 153 million proteins representing more than 95,000 organisms [3]. The tool uses a tiered approach to evaluate protein conservation, moving from broad, sequence-level comparisons to more refined, structure-based analyses [5] [3]. This multi-level evaluation allows users to capitalize on existing knowledge about chemical-protein interactions, making the tool both flexible and powerful for cross-species extrapolation.

SeqAPASS Protocol: A Tiered Approach to Predicting Susceptibility

The following protocol details the application of the SeqAPASS tool for predicting cross-species chemical susceptibility, using a known sensitive species and its protein target as a starting point.

Research Reagent Solutions

Table 1: Essential Research Reagents and Computational Tools for SeqAPASS Analysis

Item Name	Function/Description	Source/Example
Query Protein Sequence	The amino acid sequence of the protein target from a known sensitive species. Serves as the reference for all comparisons.	Can be obtained as a FASTA file or NCBI Protein Accession from databases like NCBI Protein.
SeqAPASS Online Tool	The web-based platform that performs the multi-level computational analysis.	Freely accessible at https://seqapass.epa.gov/seqapass [5].
NCBI Protein Database	The comprehensive source of protein sequence data used by SeqAPASS for cross-species comparisons.	National Center for Biotechnology Information (NIH) [3].
Chemical of Interest	The specific compound for which susceptibility is being predicted. Understanding its mode of action is critical.	e.g., a pesticide, pharmaceutical, or environmental contaminant.
External Database Links	Resources to help identify the initial query protein and its critical residues.	Integrated within SeqAPASS; e.g., CompTox Chemicals Dashboard, AOP-Wiki [5].

Getting Started and Input

• Access: Navigate to the SeqAPASS website (https://seqapass.epa.gov/seqapass) using the Chrome web browser. Log in with an existing account or create a new one to save and manage jobs [5].
• Identify Query Protein: Prior to analysis, identify a protein target and a known sensitive species through literature review. SeqAPASS provides drop-down menus with links to external resources (e.g., CompTox Chemicals Dashboard) to aid in this initial step [5].
• Initiate Job: On the homepage, select "Request SeqAPASS Run" and input the query protein using its NCBI protein accession number or by uploading a FASTA-formatted sequence file.

Level 1 Analysis: Primary Amino Acid Sequence Comparison

The first level of analysis provides a broad screening of protein conservation.

• Principle: The entire primary amino acid sequence of the query protein is compared to sequences from all other species in the database using algorithms like Protein Basic Local Alignment Search Tool (BLASTp) [5].
• Protocol:
  • After job submission, the tool automatically runs the Level 1 analysis.
  • Results are displayed as a list of species with similar protein sequences, along with a prediction of their relative intrinsic susceptibility based on overall sequence similarity.
  • Utilize the interactive data visualization features, such as the taxonomic tree, to review predictions.
• Output Interpretation: A high degree of overall sequence similarity suggests the protein target is present and, therefore, the species may be susceptible. This level is useful for initial screening but has limited taxonomic resolution.

Level 2 Analysis: Functional Domain Conservation

This level refines the prediction by focusing on the specific regions of the protein essential for its function.

• Principle: Not all regions of a protein are equally important. Level 2 evaluates the conservation of known functional domains (e.g., ligand-binding domains) [5].
• Protocol:
  • From the Level 1 results page, proceed to Level 2 analysis.
  • The tool performs multiple sequence alignments focused on the conserved functional domains as defined by databases like NCBI's Conserved Domain Database.
  • Examine the alignment and the resulting susceptibility prediction, which is now based on domain conservation rather than the full sequence.
• Output Interpretation: Improved taxonomic resolution. A species may have a similar full-length sequence but a divergent functional domain (or vice versa), leading to a more accurate susceptibility prediction than Level 1 alone.

Level 3 Analysis: Critical Amino Acid Residue Comparison

The most precise level of analysis, Level 3, requires specific knowledge of the chemical-protein interaction.

• Principle: Direct chemical binding often depends on a handful of critical amino acid residues. Level 3 evaluates the conservation of these specific residues across species [5].
• Protocol:
  • Specify the amino acid positions and identities of the residues critical for chemical binding in the query sequence. This information is derived from experimental studies (e.g., X-ray crystallography, site-directed mutagenesis).
  • SeqAPASS will generate a customizable heat map visualization, showing the alignment of these critical residues across hundreds of species.
  • The tool provides an automatic susceptibility prediction based on the perfect conservation of all critical residues.
• Output Interpretation: Species exhibiting 100% conservation of all critical residues are predicted to be susceptible. Any variation at a critical residue may suggest reduced or absent susceptibility, providing the highest level of taxonomic resolution.

The following diagram illustrates the logical workflow and decision-making process across the three tiers of SeqAPASS analysis:

Data Synthesis and Visualization

SeqAPASS versions 5.0 and above include advanced data synthesis features [5].

• Customizable Heat Maps: For Level 3 results, generate and download publication-quality heat maps that visually summarize conservation of critical residues.
• ECOTOX Widget: On the Level 1 results page, use the integrated widget to select species and a chemical of interest. This passes the query to the ECOTOX Knowledgebase to retrieve existing empirical toxicity data for comparison with sequence-based predictions [5].
• Decision Summary Report: Generate a comprehensive, downloadable PDF report that synthesizes data tables and visualizations from all levels of the SeqAPASS evaluation into a single document for interpretation and reporting [5].

Application Notes and Case Studies

Case Study 1: Endocrine Disruption via the Estrogen Receptor

• Objective: To determine if data on chemical activation of the human estrogen receptor (ER) can be translated to non-mammalian species (e.g., fish, amphibians, birds) for ecological risk assessment [3].
• Method: The human ER alpha protein sequence was used as the query in a SeqAPASS analysis. Comparisons were carried out through Levels 1, 2, and 3, with Level 3 focusing on known critical residues for ligand binding.
• Outcome: The analysis revealed a high degree of ER conservation across all vertebrate classes, suggesting that chemicals known to activate the human ER are likely to pose a risk to fish, amphibians, and birds. This information helps prioritize chemicals for testing in the Endocrine Disruptor Screening Program [3].

Case Study 2: Evaluating Pesticide Specificity

• Objective: To assess why certain pesticides targeting the ecdysone receptor in pest insects (e.g., tobacco budworm) are not toxic to non-target species like honey bees and earthworms [3].
• Method: The ecdysone receptor protein from the tobacco budworm was used as the query. Level 3 analysis was critical for comparing the specific residues forming the pesticide-binding pocket.
• Outcome: The analysis showed that key residues in the binding pocket were not conserved in honey bees and earthworms, explaining the lack of susceptibility in these non-target species and confirming the pesticide's selective mechanism of action [3].

Table 2: Summary of SeqAPASS Applications in Toxicology

Research Area	Protein Target Example	SeqAPASS Utility
Endocrine Disruption	Estrogen Receptor, Androgen Receptor	Prioritize testing of chemicals across vertebrate and invertebrate species.
Pesticide Development & Ecotoxicology	Ecdysone Receptor, Nicotinic Acetylcholine Receptor	Understand selective toxicity and assess risks to non-target pollinators and insects.
Pharmaceutical Safety	Opioid Receptors, Transthyretin	Predict potential adverse drug reactions in humans and veterinary species.
Chemical Safety for Endangered Species	Various enzyme targets	Make informed decisions for species where empirical testing is not feasible.

Technical Diagrams and Workflows

The following diagram outlines the core computational workflow that occurs within the SeqAPASS tool after a user submits a job, illustrating how the backend data and algorithms interact to produce a result.

Discussion

Strengths and Limitations

The SeqAPASS tool represents a significant advancement in predictive toxicology and embodies the "3Rs" principle (Replace, Reduce, Refine) by minimizing reliance on whole-animal testing [5] [8] [9]. Its major strengths include its robustness, leveraging constantly updated public databases; its flexibility in accommodating different levels of prior knowledge; and its interoperability with other resources like the CompTox Chemicals Dashboard and ECOTOX Knowledgebase [5] [3].

However, users must be aware of its domain of applicability. SeqAPASS specifically evaluates intrinsic susceptibility based on protein target conservation. It does not directly address other critical factors governing toxic outcomes in whole organisms, such as ADME (Absorption, Distribution, Metabolism, and Excretion). A species may possess a conserved protein target but not be susceptible in practice due to differences in metabolism that rapidly detoxify the chemical, or due to an impermeable barrier preventing the chemical from reaching the target [8]. Therefore, SeqAPASS predictions are most powerful when used as a screening-level line of evidence within a broader weight-of-evidence assessment that considers additional toxicokinetic and physiological data.

The fundamental principle that protein target conservation determines chemical susceptibility provides a powerful lens through which to view cross-species extrapolation. The SeqAPASS tool effectively applies this principle, offering researchers and regulators a sophisticated, computationally-driven method to predict chemical susceptibility for thousands of species. Its tiered protocol allows for screening-level assessments to highly refined investigations, making it an indispensable resource in the modern toxicologist's toolkit for supporting chemical safety evaluations, prioritizing testing efforts, and protecting human health and the environment.

In toxicology, ecology, and drug development, a significant challenge arises from the stark disparity in available toxicity data across different species. For well-established model organisms such as humans, mice, rats, and zebrafish, a wealth of toxicological information exists. In contrast, for the vast majority of other plants and animals, toxicity data are extremely limited or non-existent [3]. This creates a critical data gap, hindering accurate risk assessments for pharmaceuticals, pesticides, and environmental contaminants across the full spectrum of biodiversity. Traditional whole-animal testing is not only resource-intensive and costly but is also ethically questionable, especially for threatened or endangered species. This reality has accelerated the paradigm shift towards computational predictive methods that can maximize the use of existing data from data-rich species to make reliable predictions about data-poor species [5].

The fundamental premise for bridging this gap is evolutionary conservation. The susceptibility of a species to a particular chemical is often determined by the presence and specific structure of proteins that interact with that chemical once it enters the body. If the protein target of a chemical is highly conserved across species, the susceptibility observed in a model organism can be extrapolated to others [4]. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, is a web-based application designed to operationalize this principle. It provides a fast, online screening tool that allows researchers and regulators to extrapolate toxicity information across thousands of species by evaluating the conservation of known protein targets [3] [10].

SeqAPASS is a publicly available, freely accessible online tool that leverages vast public repositories of protein sequence information to predict chemical susceptibility across species. Its core function is to evaluate the conservation of a known protein target—such as a receptor or enzyme—from a species with documented chemical sensitivity (the "query") across all other species with available protein sequence data in the National Center for Biotechnology Information (NCBI) database, which contains over 153 million proteins representing more than 95,000 organisms [3] [11].

The tool is designed with a flexible, tiered approach that accommodates varying degrees of available information about the chemical-protein interaction. This multi-level evaluation allows users to refine their assessments, moving from broad, screening-level predictions to more precise, high-resolution analyses [4] [5]. A key strength of SeqAPASS is its interoperability with other databases. It can be directly linked to the EPA's CompTox Chemicals Dashboard to help identify query proteins and to the ECOTOX Knowledgebase, allowing users to compare sequence-based susceptibility predictions with existing empirical toxicity data [11] [5]. Since its initial release in 2016, SeqAPASS has undergone continuous refinement, with annual version releases incorporating new features, updated data, and enhanced visualization capabilities based on active user feedback [5].

Table 1: Key Features and Capabilities of the SeqAPASS Tool

Feature	Description	Utility for Researchers
Data Source	NCBI protein database (massive and continuously updated)	Access to a comprehensive and current knowledge base for protein sequences.
Three-Level Analysis	Primary sequence, functional domain, and critical residue comparisons.	Provides flexibility to perform analyses with variable levels of prior knowledge.
Interoperability	Links to CompTox Chemicals Dashboard and ECOTOX Knowledgebase.	Facilitates query protein identification and validation of predictions with empirical data.
Data Visualization	Customizable box plots, heat maps, and summary reports.	Enables rapid interpretation of results and generation of publication-quality graphics.
Output	Downloadable data tables, visualizations, and a comprehensive summary report (.pdf).	Streamlines data synthesis and reporting for risk assessments and scientific publications.

The Tiered Analytical Workflow of SeqAPASS

The predictive power of SeqAPASS is rooted in its three-level analytical workflow, which progresses from a broad whole-protein comparison to a focused inspection of specific atomic interactions. This hierarchical structure ensures that the tool is both accessible for novice users and powerful enough for advanced research.

Level 1: Primary Amino Acid Sequence Comparison

The first and most fundamental level of analysis involves comparing the entire primary amino acid sequence of the query protein against all available protein sequences in the database. The tool uses a standalone version of the Protein Basic Local Alignment Search Tool (BLASTp) to perform this alignment [5]. It calculates a metric for overall sequence similarity and identifies potential orthologs—proteins in different species that evolved from a common ancestral gene and typically retain the same function. A high degree of sequence similarity at this level suggests that the protein target is present in the evaluated species and provides an initial, screening-level line of evidence for potential chemical susceptibility [4].

Level 2: Functional Domain Evaluation

The second level of analysis offers greater resolution by focusing on specific functional domains of the protein. Not all regions of a protein are equally important for its interaction with a chemical. For instance, a chemical may bind specifically to a ligand-binding domain (LBD) or an active site. Level 2 analysis evaluates sequence similarity specifically within these user-selected or predefined domains [3] [4]. This is particularly useful when the entire protein sequence is not well-conserved, but the critical functional domain is. A species may be deemed susceptible if its functional domain is highly similar to that of the sensitive query species, even if the overall protein sequence similarity is lower.

Level 3: Critical Amino Acid Residue Comparison

The third and most precise level of analysis investigates the conservation of individual amino acid residues known to be critical for the protein's interaction with the chemical. These residues may be involved in forming hydrogen bonds, engaging in hydrophobic interactions, or contributing to the overall three-dimensional structure of the binding pocket. Differences in a single critical residue can be enough to abolish chemical binding and confer resistance [4] [5]. Level 3 allows users to input the positions of these critical residues from the query sequence. SeqAPASS then generates a customizable heat map visualization showing the alignment of these specific residues across species of interest, providing a high-resolution prediction of susceptibility.

The following workflow diagram illustrates the logical progression through these three tiers of analysis within the SeqAPASS tool.

Application Notes and Protocols

This section provides a detailed, step-by-step protocol for using the SeqAPASS tool, from initial setup to data interpretation, followed by specific case studies demonstrating its practical application.

Step-by-Step Experimental Protocol

1. Getting Started and Account Creation

• Navigate to the official SeqAPASS website at https://seqapass.epa.gov/seqapass using the Chrome web browser for optimal compatibility [5].
• Create a user account or log in with an existing one. Account creation is required to run, store, access, and customize jobs.

2. Pre-Analysis: Identifying the Query Protein

• Prior to analysis, identify a protein of interest and a species known to be sensitive to the chemical in question through literature review or existing data.
• The SeqAPASS interface provides drop-down menus under "Identify a Protein Target" with links to external resources like the CompTox Chemicals Dashboard and AOP-Wiki to assist in this process [11] [5].

3. Developing and Running a Level 1 Query

• On the "Request SeqAPASS Run" page, select the sensitive species and input the protein sequence, either by its NCBI protein accession number or in FASTA format [5].
• Submit the job. The tool will mine the NCBI databases, run the BLASTp alignment, and present results on the "SeqAPASS Run Status" page.
• The Level 1 results page provides an interactive data visualization (e.g., a customizable box plot) showing the distribution of sequence similarity scores across taxonomic groups. A downloadable data table is also available.

4. Refining the Analysis: Level 2 and Level 3

• From the Level 1 results, proceed to Level 2 to evaluate specific functional domains. The tool will display conserved domains; the user selects the relevant one(s) for a more focused alignment and susceptibility prediction.
• For the highest resolution, initiate a Level 3 analysis. Input the specific amino acid residue positions from the query sequence that are critical for chemical binding. The tool will generate a heat map showing the alignment of these specific residues across taxa, providing a powerful visual tool for interpreting potential susceptibility [5].

5. Data Synthesis and Interpretation

• Utilize the Decision Summary Report feature to compile results from all analysis levels into a single, downloadable PDF document. This report is invaluable for documenting the assessment for regulatory submissions or publications [5].
• Use the integrated widget to pass selected species and a chemical of interest to the ECOTOX Knowledgebase to check for any existing empirical toxicity data that can corroborate the SeqAPASS predictions [11] [5].

Case Study 1: Predicting Pollinator Susceptibility to Neonicotinoid Insecticides

• Background: The decline of honey bee (Apis mellifera) colonies has been linked to chemical stressors, including neonicotinoid insecticides. These chemicals target the nicotinic acetylcholine receptors (nAChRs) in the insect nervous system [3].
• SeqAPASS Application: Researchers used the nAChR protein sequence from the honey bee, a known sensitive species, as the query in SeqAPASS.
• Findings: The tool predicted that many other insect pollinators and beneficial insects possessed the conserved protein target, indicating potential widespread susceptibility. Conversely, the analysis helped identify species that might be less susceptible due to differences in their receptor sequences, informing the selection of safer pest control options [3] [4].
• Impact: This application allowed for a rapid, screening-level assessment of the potential ecological impact of neonicotinoids beyond honey bees, guiding subsequent testing and regulatory decisions.

Case Study 2: Evaluating Cross-Species Susceptibility to Strobilurin Fungicides

• Background: Strobilurin fungicides inhibit mitochondrial respiration by binding to the cytochrome b protein complex. Understanding their effects on non-target species is crucial for environmental risk assessment [4].
• SeqAPASS Application: The cytochrome b protein from a sensitive fungal species was used as the query. The analysis was likely carried through to Level 3, examining critical residues known to be involved in fungicide binding.
• Findings: The tool successfully predicted susceptibility across a range of fungal species. More importantly, it demonstrated a lack of binding site conservation in non-target vertebrates and plants, providing a mechanistic explanation for the selective toxicity of strobilurins and confirming their relative safety for these groups [4].
• Impact: The analysis supported the registration and use of these fungicides by clearly delineating the taxonomic domain of applicability for their adverse effects.

Table 2: Summary of Key Case Studies Applying the SeqAPASS Tool

Case Study	Query Protein (Sensitive Species)	Chemical Class	Key Finding
Pollinator Risk	Nicotinic acetylcholine receptor (Honey bee)	Neonicotinoid insecticides	Predicted potential susceptibility in many other bee species and insects, informing ecological risk assessments [3].
Endocrine Disruption	Estrogen receptor (Human)	Estrogenic chemicals	Determined the degree to which mammalian estrogen receptor data can be translated to fish, amphibians, and birds for the Endocrine Disruptor Screening Program [3].
Insect Molting	Ecdysone receptor (Tobacco budworm)	Molt-accelerating compounds	Confirmed the mechanism of selective toxicity, showing why these compounds are toxic to larval pests but not to non-targets like honey bees and earthworms [3].
Fungicide Selectivity	Cytochrome b (Fungi)	Strobilurin fungicides	Demonstrated a lack of binding site conservation in non-target species, explaining the fungicides' selective toxicity [4].

Successfully applying the SeqAPASS tool and validating its predictions requires a suite of informational and material resources. The following table details key components of this research toolkit.

Table 3: Research Reagent Solutions for Cross-Species Extrapolation

Reagent/Resource	Function/Description	Example Sources/Tools
Query Protein Sequence	The amino acid sequence of the protein target from a known sensitive species; serves as the baseline for all comparisons.	National Center for Biotechnology Information (NCBI) Protein Database [3] [5].
Chemical-Protein Interaction Data	Information on functional domains and critical amino acid residues essential for high-resolution (Level 3) analysis.	Scientific literature, crystallographic databases (e.g., Protein Data Bank), AOP-Wiki [4] [5].
Taxonomic Information	A structured classification system that allows for the organization and interpretation of results across species.	Integrated Taxonomic Information System (ITIS), NCBI Taxonomy [5].
Empirical Toxicity Data	Experimental data used to validate SeqAPASS predictions of susceptibility.	EPA ECOTOX Knowledgebase [11].
BLAST+ and COBALT Executables	The underlying algorithms used by SeqAPASS for sequence alignment and comparison; updated regularly with new tool versions.	National Institutes of Health (NIH) [5].

The challenge of extrapolating toxicity data from data-rich to data-poor species is a significant bottleneck in ecological risk assessment and drug development. The SeqAPASS tool represents a powerful, innovative solution to this problem. By leveraging publicly available protein sequence data and a flexible, multi-level analytical framework, it provides researchers with a rational, evidence-based method to predict cross-species susceptibility. Its applications in predicting chemical risks to pollinators, understanding endocrine disruption across vertebrates, and confirming the selective toxicity of pesticides and fungicides underscore its utility and reliability.

As the volume of genetic and protein data continues to grow, and as computational tools like SeqAPASS become more sophisticated and integrated with other data sources, the vision of a comprehensive, predictive toxicology framework that minimizes animal testing and rapidly protects human health and the environment comes closer to reality. For researchers and drug development professionals, mastering tools like SeqAPASS is becoming essential for conducting cutting-edge, efficient, and ecologically relevant safety assessments.

The landscape of chemical safety and drug development is undergoing a fundamental transformation, driven by scientific advancement and regulatory change. The FDA Modernization Act 2.0, signed into law in December 2022, represents a pivotal shift by refuting the 1938 Federal Food, Drug, and Cosmetics Act that had mandated animal testing for every new drug development protocol [12]. This legislative change opens the door for advanced, human-relevant tools—collectively known as New Approach Methodologies (NAMs)—to replace, reduce, and refine traditional animal testing [13] [14].

Within this new framework, the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool emerges as a critical bioinformatics platform for addressing one of the most persistent challenges in toxicology and risk assessment: extrapolating chemical effects across diverse species. SeqAPASS is a fast, online screening tool that allows researchers and regulators to extrapolate toxicity information from data-rich model organisms to thousands of other non-target species with limited or no toxicity data [3]. By evaluating protein sequence and structural similarities, SeqAPASS provides a scientifically robust method for predicting cross-species susceptibility, aligning perfectly with the FDA's evolving roadmap for modernized safety assessment [15].

SeqAPASS Technology: A Tiered Bioinformatics Approach

Core Architecture and Analytical Workflow

SeqAPASS leverages the vast biological data available in the National Center for Biotechnology Information (NCBI) protein database, which contains information on over 153 million proteins representing more than 95,000 organisms [3]. The tool's power lies in its flexible, three-tiered analytical approach, which allows researchers to capitalize on existing information about chemical-protein interactions:

• Level 1 (Primary Sequence Analysis): Compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity and detecting orthologs across species.
• Level 2 (Functional Domain Evaluation): Evaluates sequence similarity within selected functional domains (e.g., ligand-binding domains) that are critical for chemical-protein interactions.
• Level 3 (Residue-Specific Analysis): Compares individual amino acid residue positions important for protein conformation and/or interaction with chemicals upon binding [4] [15].

Table 1: SeqAPASS Tiered Analysis Framework

Analysis Level	Comparison Focus	Key Outputs	Application Context
Level 1	Primary amino acid sequence	Sequence similarity metrics, ortholog detection	Initial screening for potential susceptibility
Level 2	Functional protein domains	Domain conservation across species	Refined analysis focusing on functional regions
Level 3	Specific amino acid residues	Residue-level conservation	High-resolution analysis for critical binding sites

This multi-tiered approach provides increasing evidence to support rapid, screening-level assessments of probable cross-species susceptibility, enabling more informed chemical prioritization and species selection for testing [4].

Experimental Protocol: Conducting a Cross-Species Susceptibility Analysis

Protocol Title: Standardized Workflow for Cross-Species Susceptibility Prediction Using SeqAPASS

Principle: This protocol describes a systematic approach for using SeqAPASS to evaluate potential chemical susceptibility across species by analyzing conservation of protein targets.

Materials and Reagents:

• Computer with internet access
• Target protein sequence (UniProt ID recommended)
• Known chemical susceptiblity data for reference species
• List of species of concern for evaluation

Procedure:

• Input Preparation
  • Identify the protein target known to interact with the chemical of concern.
  • Obtain the primary amino acid sequence for the reference protein (e.g., from a known sensitive species).
  • Define the list of species for comparison based on assessment needs.

• Level 1 Analysis (Primary Sequence Alignment)

  • Navigate to the SeqAPASS web interface (https://seqapass.epa.gov/seqapass/).
  • Input the reference protein sequence or select from predefined targets.
  • Configure taxonomic filters to include species of interest.
  • Execute the primary sequence alignment.
  • Download and document the sequence similarity results.

• Level 2 Analysis (Functional Domain Evaluation)

  • Identify critical functional domains (e.g., ligand-binding domain) from literature or domain databases.
  • Configure SeqAPASS to focus on these specific domains.
  • Run the domain-specific comparison.
  • Compare domain conservation metrics across species of interest.

• Level 3 Analysis (Critical Residue Assessment)

  • Identify specific amino acid residues critical for chemical binding from crystallography data or mutagenesis studies.
  • Configure residue-specific analysis in SeqAPASS.
  • Execute the high-resolution comparison.
  • Document residue conservation patterns across species.

• Data Integration and Interpretation

  • Integrate findings from all three analysis levels.
  • Generate susceptibility predictions based on conservation thresholds.
  • Visualize results using SeqAPASS plotting tools.
  • Export data for reporting and decision-making [4] [3] [15].

SeqAPASS in Action: Case Studies and Applications

Endocrine Disruption Assessment Across Vertebrate Species

Background: The Endocrine Disruptor Screening Program (EDSP) faces the challenge of evaluating over 10,000 chemicals for potential effects on the endocrine system across diverse species. SeqAPASS has been employed to determine the degree to which data generated for chemical activation in mammalian systems (e.g., the human estrogen receptor) can be translated to non-mammalian species such as fish, amphibians, and birds.

Experimental Approach:

• Target Protein: Human estrogen receptor alpha (ERα)
• Reference Species: Homo sapiens (human)
• Test Species: Multiple fish, amphibian, and bird species
• Analysis Level: Level 2 (ligand-binding domain conservation)

Results and Regulatory Impact: The analysis revealed significant conservation of the estrogen receptor ligand-binding domain across vertebrate species, providing a scientific basis for extrapolating estrogenic activity data from mammalian models to ecological receptors. This approach has helped prioritize testing resources and inform the human health and ecological risk assessment of estrogenic chemicals [3].

Insecticide Specificity and Pollinator Protection

Background: The decline in honey bee colonies has raised concerns about the role of chemical exposures, particularly neonicotinoid insecticides that target nicotinic acetylcholine receptors (nAChRs). SeqAPASS was used to evaluate the potential chemical susceptibility of honey bees compared to target pest species.

Experimental Approach:

• Target Protein: Nicotinic acetylcholine receptor subunits
• Reference Species: Apis mellifera (honey bee) and target pest insects
• Analysis Level: Level 3 (critical residue analysis)

Key Findings: The analysis identified key differences in specific residue positions between honey bees and target pests, explaining differential sensitivity to neonicotinoid insecticides. These findings supported the development of more selective insecticide candidates that maintain efficacy against pests while reducing risks to pollinators [4] [3].

Table 2: Summary of SeqAPASS Case Study Applications

Application Area	Target Protein	Key Species Compared	Regulatory Impact
Endocrine Disruption	Estrogen receptor	Human, fish, amphibians, birds	Informed testing priorities for EDSP
Insecticide Development	Nicotinic acetylcholine receptor	Honey bees, pest insects	Supported pollinator risk assessments
Molting Disruption	Ecdysone receptor	Budworms, honey bees, earthworms	Validated species selectivity of insecticides
Fungicide Safety	Cytochrome b	Fish, birds, mammals	Informed ecological risk assessment for strobilurin fungicides

Integration with the Evolving Regulatory Framework

Alignment with FDA Modernization Act 2.0 and NAMs

The FDA Modernization Act 2.0 represents more than just a policy change—it signals a fundamental reorientation toward human-relevant, mechanistic toxicology. SeqAPASS aligns perfectly with this new paradigm through several key attributes:

• Human-Relevant Predictions: By focusing on conserved molecular targets, SeqAPASS provides insights directly relevant to human biology rather than relying solely on animal-to-human extrapolation.
• Mechanistic Basis: The tool operates at the molecular level, understanding the fundamental protein-chemical interactions that underlie toxicity.
• Reduction and Replacement: SeqAPASS enables significant reduction in animal use by providing prior evidence for species selection or potentially replacing certain categories of animal testing altogether [12] [13].
• Regulatory Acceptance: The tool has been developed and maintained by the U.S. EPA, facilitating regulatory acceptance and implementation [3].

The FDA's 2025 NAMs Roadmap: Strategic Implications

The FDA's 2025 "Roadmap to Reducing Animal Testing in Preclinical Safety Studies" establishes an ambitious framework for transitioning to NAMs over a 3-5 year period. This roadmap:

• Prioritizes monoclonal antibodies as the first therapeutic class subject to the NAMs initiative
• Encourages sponsors to include NAMs data in Investigational New Drug (IND) applications
• Establishes pilot programs for biologics to demonstrate NAMs-based testing strategies
• May offer expedited review timelines for submissions with strong non-animal safety data [13]

Within this framework, SeqAPASS serves as a critical tool for addressing species relevance questions, particularly for biologics where traditional animal models often show limited predictivity for human responses.

Research Reagent Solutions and Computational Tools

Table 3: Essential Research Resources for SeqAPASS and Cross-Species Research

Tool/Resource	Type	Primary Function	Access Information
SeqAPASS	Web application	Cross-species protein sequence and structure comparison	https://seqapass.epa.gov/seqapass/
NCBI Protein Database	Database	Comprehensive repository of protein sequences	https://www.ncbi.nlm.nih.gov/protein
I-TASSER	Computational tool	Protein structure prediction from sequence	https://zhanggroup.org/I-TASSER/
CompTox Chemicals Dashboard	Database	Chemical toxicity and property data	https://comptox.epa.gov/dashboard
ECOTOX Knowledgebase	Database	Ecological toxicity data	https://www.epa.gov/ecotox
in vitroDB	Database	ToxCast high-throughput screening data	Part of EPA CompTox Chemicals Dashboard

Advanced Applications and Future Directions

From Sequence to Structure: The Next Frontier

The integration of protein structural information represents the cutting edge of cross-species extrapolation. Recent advances have demonstrated a pipeline from SeqAPASS sequence analysis to I-TASSER-generated protein structures for comparative analysis. This approach was successfully applied to human liver fatty acid-binding protein (LFABP) and androgen receptor (AR), generating 99 LFABP and 268 AR protein models representing diverse species [15].

The structural comparisons aligned with sequence-based SeqAPASS results, providing additional evidence of LFABP and AR conservation across vertebrate species. This integration of sequence and structural data creates a more comprehensive framework for species extrapolation and enhances confidence in predictions of cross-species susceptibility.

Workflow Visualization: Integrated Computational Toxicology Pipeline

The following diagram illustrates the integrated computational pipeline combining SeqAPASS with protein structure modeling for enhanced cross-species extrapolation:

The convergence of regulatory modernization through the FDA Modernization Act 2.0 and scientific advancement through tools like SeqAPASS represents a transformative moment for chemical safety assessment and drug development. SeqAPASS provides a scientifically robust, computationally efficient framework for addressing fundamental questions about species relevance and susceptibility, enabling more targeted testing, reduced animal use, and ultimately, more human-relevant safety assessments.

As the regulatory landscape continues to evolve toward greater acceptance of NAMs, the integration of SeqAPASS into standardized testing strategies and regulatory submissions will play a crucial role in realizing the vision of more predictive, mechanistically grounded safety assessment. The ongoing expansion of SeqAPASS capabilities to include structural comparisons and integration with other computational approaches positions this tool as a cornerstone of next-generation toxicology and risk assessment.

The global decline of pollinators, essential for ecosystem stability and agricultural productivity, represents a critical environmental challenge. Exposure to plant protection products (PPPs) is a significant contributor to this decline, with particular concern surrounding chemicals capable of inducing endocrine disruption and chronic sublethal effects [16]. Current regulatory frameworks often overlook these subtle yet population-damaging impacts in favor of assessing acute toxicity. The SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) tool, developed by the US EPA, provides a powerful bioinformatic approach to address this challenge [3]. By evaluating the conservation of protein targets across species, SeqAPASS enables researchers to predict the cross-species susceptibility of non-target organisms, such as pollinators, to specific insecticides [4]. This application note details how SeqAPASS can be deployed to investigate the molecular basis of insecticide action and endocrine disruption, facilitating the development of safer agricultural chemicals and the protection of pollinator health.

Application Note: Utilizing SeqAPASS for Cross-Species Susceptibility Prediction

SeqAPASS is a fast, online screening tool that addresses the challenge of extrapolating toxicity data from data-rich model organisms to thousands of non-target species with limited or no toxicity information [3]. Its underlying principle is that a species' sensitivity to a chemical is often determined by the presence and specific structure of protein targets that interact with the chemical once inside the body [3]. For pesticides, these protein targets are often well-defined. SeqAPASS leverages the vast National Center for Biotechnology Information (NCBI) protein database to evaluate amino acid sequence and structural similarity, thereby identifying whether a specific protein target implicated in chemical toxicity is present and conserved in other species [3].

The tool's flexibility is manifested in its three-tiered analytical approach, which moves from broad to highly specific assessments. This allows users to capitalize on existing knowledge about chemical-protein interactions in sensitive species and provides a quantitative, screening-level line of evidence for predicting susceptibility across the tree of life [4]. This capability is indispensable for prioritizing chemicals for further testing, selecting ecologically relevant species for risk assessment, and understanding the potential ecological relevance of adverse outcome pathways.

Key Biological Applications in Pollinator Protection

SeqAPASS has been successfully applied to several critical areas concerning pollinator health and insecticide mode of action. The following table summarizes three prominent case studies.

Table 1: Key Case Studies Demonstrating SeqAPASS Application in Pollinator Research

Case Study	Chemical Class	Protein Target	SeqAPASS Application & Findings
Neonicotinoid Insecticides [4]	Neonicotinoids (e.g., imidacloprid)	Nicotinic Acetylcholine Receptor (nAChR)	Used to evaluate the potential chemical susceptibility of honey bees and other bee species by comparing protein target similarity to known sensitive pest species [3].
Molting-Accelerating Compounds [4]	Molt-accelerating compounds (e.g., tebufenozide)	Ecdysone Receptor	A cross-species comparison of the protein sequence in the tobacco budworm (a target pest) was used to predict the potential susceptibility of non-target insects, including honey bees and earthworms [3].
Endocrine Disruption in Bees [16]	Various insecticides (e.g., fipronil, azadirachtin)	Endocrine system components (e.g., vitellogenin)	Proposed use of SeqAPASS to investigate endocrine pathways. Analysis of conserved proteins can predict potential for disrupted reproduction (queens/drones) and premature behavioral transition (nurse to forager bees) [16].

Experimental Protocol for Predicting Chemical Susceptibility

This protocol provides a step-by-step guide for using SeqAPASS to assess the potential susceptibility of a non-target pollinator species to a specific chemical.

Table 2: Research Reagent Solutions for SeqAPASS Analysis

Item	Function / Description
Known Sensitive Species	Provides the query protein sequence from an organism known to be sensitive to the chemical of interest (e.g., a pest insect for an insecticide) [4].
Protein Sequence Data	The amino acid sequence(s) of the specific protein target (e.g., receptor, enzyme) from the sensitive species, often retrieved from NCBI Protein database [3].
Chemical-Protein Interaction Data	Information on specific amino acid residues, functional domains, or protein structures critical for the chemical's binding and action [4].
List of Non-Target Species	The taxonomic list of species for which susceptibility will be predicted (e.g., Apis mellifera, Bombus terrestris) [3].

Procedure:

• Define the Scope: Identify the chemical of interest, its known protein target, and a well-studied, sensitive species (e.g., the target pest for an insecticide).
• Acquire Query Sequence: Retrieve the full amino acid sequence of the protein target from the sensitive species from the NCBI protein database.
• Access SeqAPASS: Navigate to the online SeqAPASS tool (https://seqapass.epa.gov/seqapass/).
• Level 1 Analysis (Primary Sequence):
  • Input the query sequence from Step 2.
  • Select the taxonomic groups of interest (e.g., Insecta, Apidae).
  • Run the analysis to identify orthologs and calculate a quantitative metric for overall sequence similarity across species.
• Level 2 Analysis (Functional Domains):
  • Refine the analysis by focusing on specific functional domains (e.g., ligand-binding domain) known to be critical for the chemical-protein interaction.
  • Evaluate the sequence similarity within these specific domains, which may provide a more accurate prediction of susceptibility than the full sequence alone.
• Level 3 Analysis (Critical Residues):
  • If the specific amino acid residues required for chemical binding are known, use the Level 3 analysis to evaluate their conservation across species.
  • This is the most precise level of assessment, determining if the molecular machinery for the toxic interaction is intact in a non-target species.
• Data Interpretation: Download and analyze the results. High sequence similarity in the functional domains or conservation of critical residues in a non-target species (e.g., a bee) suggests a higher potential for susceptibility. Lower similarity suggests a lower risk.

Investigating Endocrine Disruption in Pollinators

Background and Significance

Endocrine disruption in pollinators is an emerging threat that extends beyond acute lethality, potentially causing population-level declines through impaired reproduction, development, and behavior [16]. In honey bees, documented effects include reduced reproductive success of queens and drones and the premature behavioral transition of nurse bees to foragers, which can destabilize colony dynamics [16]. These disruptions are linked to insecticides from several chemical classes, including neonicotinoids, fipronil, and azadirachtin [16]. The challenge for regulators and researchers is that standardized testing guidelines (e.g., OECD) for endocrine disruption in bees are currently lacking. The SeqAPASS tool offers a pathway to address this gap by identifying conserved endocrine pathways across species, thus predicting which chemicals are likely to act as endocrine disruptors in pollinators based on their known action in other organisms.

Signaling Pathways and Molecular Targets

A key endocrine-related protein in honey bees is vitellogenin, which acts as a storage protein but also regulates behavioral maturation and foraging onset [16]. Chemicals that disrupt the hormonal control of vitellogenin or interact directly with its receptor can have profound effects on colony health. The diagram below illustrates a simplified signaling pathway for endocrine disruption in a pollinator, highlighting potential sites of chemical interference.

Experimental Protocol for Endocrine Disruption Assessment

This protocol outlines a combined in silico and in vivo approach to screen and confirm the endocrine-disrupting potential of a chemical in pollinators.

Procedure:

• In Silico Screening with SeqAPASS:
  • Query Selection: Identify a protein target known to be involved in endocrine disruption from a model insect (e.g., the ecdysone receptor from Drosophila melanogaster or the vitellogenin receptor).
  • Sequence Analysis: Perform a Level 1 and Level 2 SeqAPASS analysis to confirm the presence and high conservation of this protein target in the pollinator of interest (e.g., Apis mellifera).
• Laboratory Bioassay - Chronic Exposure:
  • Test Organisms: Use adult worker bees or bee larvae from healthy hives.
  • Exposure Setup: Establish a control group (fed sugar syrup only) and treatment groups fed sugar syrup containing sublethal, environmentally relevant concentrations of the test chemical. Chronic exposure should last for 10-15 days for adults or cover the entire larval development period [16].
  • Environmental Conditions: Maintain test subjects in incubators at 33±1°C and 50-70% relative humidity.
• Endpoint Measurement:
  • Gene Expression Analysis: At the end of the exposure period, extract RNA from dissected tissues (e.g., fat body, brain). Use quantitative PCR (qPCR) to measure the expression levels of endocrine-related genes, such as vitellogenin and juvenile hormone-associated genes [16].
  • Behavioral Assessment: For adult bees, monitor and record behaviors such as the age of first flight and foraging tendency, as premature foraging is a key indicator of endocrine disruption [16].
• Data Analysis: Compare gene expression profiles and behavioral data between the control and treatment groups. A statistically significant alteration in gene expression coupled with a behavioral shift provides strong evidence of endocrine-disrupting activity.

Workflow for Integrated Risk Assessment

The following diagram illustrates the comprehensive workflow for integrating SeqAPASS predictions with laboratory validation to assess the risk of insecticides to pollinators.

The SeqAPASS Workflow in Action: A Step-by-Step Methodology from Sequence to Structure

Account Creation and Initial Login

To commence an analysis with the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, the first step involves account creation and platform access. The SeqAPASS tool is a freely available, web-based application provided by the U.S. Environmental Protection Agency (EPA) that requires user registration to run, store, and customize jobs [5].

Step-by-Step Protocol:

• Access the Website: Navigate to the official SeqAPASS website using a compatible web browser, preferably Chrome, at https://seqapass.epa.gov/seqapass/ [5].
• Login or Register: On the homepage, select either "Login" to use an existing account or follow the on-screen instructions to create a new SeqAPASS account [5].
• Account Benefits: A personal account allows users to save their analyses, access previous jobs, and manage their work within the tool [5].

Identification of a Query Protein

Prior to submitting a computational query, it is essential to identify a specific protein target and a known sensitive species through a review of existing literature or pre-existing data [5]. The sensitivity of a species to a chemical is often determined by the presence and conservation of specific proteins that interact with chemicals, and a majority of these proteins are curated in the National Center for Biotechnology Information (NCBI) protein database [3].

Step-by-Step Protocol:

• Define the Protein Target: Identify the specific protein known to interact with the chemical of interest. This information is typically derived from toxicological studies on model organisms. Example protein targets include the Estrogen Receptor for endocrine disruptors, the Ecdysone Receptor for molt-accelerating compounds, the Nicotinic Acetylcholine Receptor for neonicotinoid insecticides, and the Ryanodine Receptor (RyR) for diamide insecticides [3] [4] [17].
• Select a Sensitive Species: Determine a species known to be sensitive to the chemical via this protein target. Common model organisms with rich toxicological data include humans, mice, rats, and zebrafish [3] [5].
• Utilize Integrated Resources: The SeqAPASS platform contains links to external resources to assist in identifying the correct query protein. Click the drop-down buttons under "Identify a Protein Target" on the website to access these resources [5]. The tool is also interoperable with the CompTox Chemicals Dashboard and the Adverse Outcome Pathway Wiki (AOP-Wiki), which can help in defining the molecular initiating event of interest [5].

Table 1: Recommended Resources for Query Protein Identification

Resource Name	Description	Primary Utility in SeqAPASS Context
NCBI Protein Database	A comprehensive repository of protein sequences from more than 95,000 organisms [3].	The primary source for amino acid sequence data used by SeqAPASS for cross-species comparisons.
CompTox Chemicals Dashboard	A EPA database providing access to chemistry, toxicity, and exposure data for chemicals [5].	Helps identify protein targets for specific chemicals of interest.
AOP-Wiki	A crowd-sourced knowledge base on Adverse Outcome Pathways (AOPs) [5].	Aids in defining the Molecular Initiating Event (MIE) for a toxicological pathway, which often involves a specific protein-chemical interaction.
Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank	A database for 3D structural data of large biological molecules [18].	Used to obtain protein structures for Level 4 analysis in SeqAPASS v7.0 and later.
AlphaFold	An AI system that predicts a protein’s 3D structure from its amino acid sequence [18].	Used to generate or obtain protein structures for Level 4 analysis.

Workflow for Query Protein Identification

The following diagram illustrates the logical workflow for identifying a query protein and a known sensitive species, which is a critical prerequisite before starting a SeqAPASS analysis.

The Scientist's Toolkit: Research Reagent Solutions

The following table details the key computational and data resources essential for successfully initiating a SeqAPASS analysis.

Table 2: Essential Research Reagents and Resources for SeqAPASS Analysis Initiation

Item/Tool	Category	Function in Analysis
SeqAPASS Web Tool	Software Application	The primary online platform for performing cross-species susceptibility predictions via sequence and structural comparisons [3] [5].
NCBI Protein Database	Data Repository	The source of over 153 million protein sequences used for sequence alignment and conservation analysis across taxonomic groups [3].
CompTox Chemicals Dashboard	Data Resource	Aids in the initial identification of protein targets for specific chemicals, informing the choice of query protein [5].
AOP-Wiki	Knowledge Base	Provides context on Adverse Outcome Pathways, helping to establish the relevance of a protein-chemical interaction as a Molecular Initiating Event [5].
Chrome Web Browser	Software	The recommended browser for optimal compatibility and performance of the SeqAPASS web interface [5].
Iterative Threading ASSEmbly Refinement (I-TASSER)	Modeling Tool	Integrated into SeqAPASS v7.0+ to generate protein structures for advanced Level 4 structural evaluations [18].
Leucomycin A9	Leucomycin A9, CAS:18361-49-4, MF:C37H61NO14, MW:743.9 g/mol	Chemical Reagent
cis-2-Dodecenoic acid	cis-2-Dodecenoic acid, CAS:4412-16-2, MF:C12H22O2, MW:198.30 g/mol	Chemical Reagent

Levels of SeqAPASS Analysis

After identifying the query protein and sensitive species, users can proceed to run a SeqAPASS query. The tool employs a tiered approach to extrapolate toxicity information from data-rich model organisms to thousands of other species [3] [4]. The core of a SeqAPASS analysis involves three progressive levels of comparison, with a fourth level added in recent versions.

Table 3: Levels of Analysis in the SeqAPASS Tool

Analysis Level	Technical Description	Taxonomic Resolution & Application
Level 1: Primary Amino Acid Sequence	Compares the entire primary amino acid sequence of the query protein to sequences from all species with available data, using BLASTp algorithms to calculate a metric for sequence similarity and identify orthologs [5] [4].	Provides a broad, screening-level prediction of susceptibility across diverse taxa. Serves as the initial line of evidence.
Level 2: Functional Domain Comparison	Evaluates sequence similarity within selected functional domains (e.g., a ligand-binding domain) that are critical for the specific chemical-protein interaction [4].	Offers higher taxonomic resolution than Level 1 by focusing on the functionally relevant region of the protein.
Level 3: Critical Amino Acid Residue Comparison	Compares individual amino acid residue positions known to be important for protein conformation and/or direct interaction with the chemical [4] [19].	Provides the highest resolution for species-specific predictions. Requires detailed knowledge of the key residues involved in the interaction.
Level 4: Protein Structural Evaluation (v7.0+)	Allows users to incorporate protein structural alignments using generated or imported structures (e.g., from PDB or AlphaFold) to assess structural conservation [18].	Adds a powerful line of evidence based on 3D protein conformation, further refining susceptibility predictions.

Understanding the intrinsic susceptibility of diverse species to chemicals is a fundamental challenge in ecological risk assessment and translational toxicology. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, addresses this challenge by leveraging computational biology to predict chemical susceptibility across species based on protein target conservation [3] [4]. The tool operates through three tiers of analysis, with Level 1 serving as the foundational screen. Level 1 analysis performs a whole protein sequence comparison to rapidly identify orthologs—proteins in different species that share a common ancestor and typically, a similar function—across the taxonomic spectrum [5] [4]. This initial evaluation provides a critical first line of evidence for determining whether a protein target known to interact with a chemical in a well-studied model organism (e.g., human, rat, or zebrafish) is likely present in thousands of other species, thereby offering a screening-level prediction of potential susceptibility [5] [3].

Theoretical Foundation of Level 1 Analysis

The core premise of Level 1 analysis is that the primary amino acid sequence of a protein determines its fundamental structure and function. If a chemical interacts with a specific protein in a sensitive species, then other species possessing a highly similar protein sequence are preliminarily predicted to be susceptible to that same chemical [4]. This principle of sequence-structure-function relationship enables high-throughput extrapolation from data-rich model organisms to data-poor species.

SeqAPASS automates this process by mining and compiling protein sequences from the National Center for Biotechnology Information (NCBI) protein database, a comprehensive repository containing over 153 million proteins from more than 95,000 organisms [5] [3]. The Level 1 analysis utilizes the Protein Basic Local Alignment Search Tool (BLASTp) algorithm to compare a user-provided "query" protein sequence from a species of known sensitivity against this vast database [5]. The tool calculates quantitative metrics of sequence similarity and uses them to identify potential orthologs and generate initial susceptibility predictions for all species with available sequence data.

Level 1 Experimental Protocol

Preliminary Requirements and Setup

Before initiating a Level 1 analysis, researchers must complete two prerequisite steps. First, a SeqAPASS user account must be created via the official website (https://seqapass.epa.gov/seqapass/). This account allows users to run, store, access, and customize their analysis jobs [5]. Second, the protein target and a sensitive species must be identified through a review of existing literature or pre-existing toxicological data. The query protein is the molecular target against which all other species will be compared. SeqAPASS provides integrated links to external resources like the CompTox Chemicals Dashboard and AOP-Wiki to assist in this identification process [5].

Step-by-Step Execution Guide

The following protocol outlines the specific steps for performing a Level 1 analysis as detailed in the SeqAPASS documentation [5]:

• Access and Login: Navigate to https://seqapass.epa.gov/seqapass/ using the Chrome web browser and log in to your SeqAPASS account.
• Initiate New Job: From the dashboard, select the option to submit a new SeqAPASS job or "Request SeqAPASS Run."
• Input Query Sequence: Enter the amino acid sequence for the protein of interest. This can be done by providing a standard FASTA-formatted sequence or by using a unique NCBI protein accession number (e.g., NP_00112345) that automatically retrieves the sequence.
• Select the Sensitive Species: Specify the species from which the query protein originates as the known sensitive or "target" organism.
• Configure Analysis Parameters (Optional): The tool applies default settings for the BLASTp analysis. Advanced users can modify key parameters, most notably the E-value cutoff, which determines the statistical significance threshold for sequence matches. A lower E-value indicates a more significant match not due to random chance.
• Submit and Execute: Launch the analysis. The tool will process the query, which may take several minutes depending on server load and query complexity.
• Monitor Job Status: The "SeqAPASS Run Status" page allows users to track the progress of their submitted job.

Data Interpretation and Susceptibility Prediction

Upon completion, SeqAPASS generates a Level 1 report containing several key components for data interpretation:

• Ortholog Identification: The tool compiles a list of potential orthologs from other species based on sequence similarity metrics from the BLASTp analysis [5] [4].
• Susceptibility Prediction: A quantitative assessment is performed to predict relative intrinsic susceptibility. The tool calculates a percent identity for each ortholog compared to the query sequence. Based on a pre-defined, user-modifiable cutoff for this percent identity, species are categorized as either "Susceptible" or "Not Susceptible" [5].
• Data Visualization: Modern versions of SeqAPASS (v2.0 and above) include interactive data visualization capabilities. Level 1 results can be displayed as a density plot, which helps users visualize the distribution of sequence similarities across taxa and make informed decisions about an appropriate susceptibility cutoff value [5].

Research Reagent Solutions

The following table details the essential "research reagents," or core components, required to perform a Level 1 analysis.

Table 1: Essential Components for SeqAPASS Level 1 Analysis

Component	Function in the Analysis	Source
Query Protein Sequence	Serves as the reference sequence for all cross-species comparisons. It can be provided in FASTA format or via an NCBI accession number.	Researcher-provided or NCBI Protein Database [5]
NCBI Protein Database	The comprehensive source database against which the query sequence is compared. Contains millions of sequenced proteins from thousands of organisms.	National Center for Biotechnology Information (NCBI) [5] [3]
BLASTp Algorithm	The core computational engine that performs the primary amino acid sequence alignment and calculates metrics of sequence similarity (E-value, percent identity).	Integrated into SeqAPASS backend [5]
Sensitive/Target Species	The organism from which the query protein is derived and for which chemical susceptibility data is known. Used to contextualize the predictions.	Researcher-defined based on literature [5]

Level 1 Analysis Workflow

The diagram below illustrates the logical flow and key steps of the SeqAPASS Level 1 analysis protocol.

Application in Research: Use Cases

Level 1 analysis has been successfully applied in numerous research contexts to address cross-species extrapolation challenges:

• Endocrine Disruption Research: Scientists have used SeqAPASS to evaluate the conservation of the human estrogen receptor across non-mammalian species like fish, amphibians, and birds. The Level 1 analysis helped determine the applicability of mammalian-based toxicity data for assessing ecological risks of estrogenic chemicals [3].
• Insecticide Selectivity: The tool was employed to compare the protein sequence of the ecdysone receptor from the tobacco budworm (a target pest) to other insects. This analysis helped explain the selective toxicity of molt-accelerating insecticides to pests like armyworms and moths, while demonstrating a lack of effect on non-target species such as honey bees and earthworms [3] [4].
• Pollinator Protection: SeqAPASS Level 1 analysis has been critical in assessing the potential chemical susceptibility of honey bees and other pollinators by evaluating the conservation of targets like the nicotinic acetylcholine receptor, which is impacted by certain insecticides [3] [4].

Evolution of SeqAPASS and Data Outputs

The SeqAPASS tool has undergone significant version updates since its initial launch, with each release enhancing its functionality and user interface. The table below summarizes the key developments relevant to Level 1 analysis.

Table 2: Evolution of SeqAPASS Tool Features [5]

SeqAPASS Version	Date	Key Features and Updates Relevant to Level 1
v1.0	Jan 2016	Initial public release with core Level 1 and Level 2 functionality.
v2.0	May 2017	Added capability to modify default settings for Level 1 reports.
v3.0	Mar 2018	Introduced interactive data visualization capabilities (density plots) for Level 1 results.
v4.0	Oct 2019	Added links to external databases (CompTox Dashboard, AOP-Wiki) to help identify query proteins.
v5.0	Dec 2020	Launched customizable summary reports for synthesizing data across all analysis levels.
v6.0	Sep 2021	Implemented a widget to connect SeqAPASS predictions directly to empirical toxicity data in the ECOTOX Knowledgebase from the Level 1 results page.

The Level 1 analysis for whole protein sequence comparison and ortholog identification represents a powerful, efficient, and accessible first step in cross-species susceptibility assessment. By leveraging publicly available protein sequences and robust bioinformatics algorithms, it allows researchers and regulators to rapidly screen thousands of species and generate hypotheses about potential chemical susceptibility. This protocol provides the necessary framework for scientists to confidently employ SeqAPASS Level 1 analysis, thereby supporting more informed decision-making in chemical prioritization, species selection for testing, and the extrapolation of toxicological data in both ecological and human health contexts.

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool is a fast, freely available online screening tool developed by the US Environmental Protection Agency that enables researchers and regulators to extrapolate toxicity information across species [3] [20]. The tool addresses a critical challenge in toxicology and chemical safety assessment: predicting chemical susceptibility for thousands of species for which toxicity data are limited or non-existent, by leveraging existing data from model organisms [4] [3]. SeqAPASS operates on the fundamental principle that a species' intrinsic susceptibility to a particular chemical is determined by the presence and conservation of specific protein targets with which that chemical interacts [3].

SeqAPASS performs this assessment through three tiered levels of analysis, each providing increasing taxonomic resolution and specificity [20]. Level 1 compares entire primary amino acid sequences across species to identify potential orthologs. Level 2, the focus of this protocol, narrows the comparison to specific functional domains (e.g., ligand-binding domains) directly involved in the chemical-protein interaction [4] [20]. Level 3 provides the most granular analysis by evaluating conservation at individual amino acid residues known to be critical for chemical binding or protein function [20] [5]. This progressive approach allows researchers to capitalize on existing knowledge about chemical-protein interactions, with Level 2 serving as a crucial intermediate step that balances specificity with practical applicability when full residue-level data may be incomplete [4].

Table 1: SeqAPASS Analysis Levels and Their Applications

Analysis Level	Comparison Focus	Taxonomic Resolution	Information Required
Level 1	Primary amino acid sequence	Broad (e.g., phylum, class)	Protein sequence from a sensitive species
Level 2	Functional domains (e.g., LBD)	Intermediate (e.g., order, family)	Domain boundaries and potential critical residues
Level 3	Individual amino acid residues	High (e.g., species, population)	Specific residues critical for chemical interaction

Theoretical Foundation: Ligand-Binding Domains and Cross-Species Extrapolation

The Structural and Functional Significance of Ligand-Binding Domains

The ligand-binding domain (LBD) is a specialized protein region responsible for the specific binding of signaling molecules (ligands) such as hormones, pharmaceuticals, or environmental contaminants [21]. In nuclear receptors, the LBD is located at the C-terminal half of the receptor protein and adopts a globular α-helical folded structure that forms a hydrophobic binding pocket for the ligand [21]. This domain is evolutionarily conserved across diverse species, making it an ideal focus for cross-species susceptibility predictions [22] [23].

The LBD serves multiple essential functions beyond simple ligand binding. It contains the activation function-2 (AF-2) domain, which is responsible for ligand-mediated recruitment of transcriptional co-regulators [21] [22]. Upon ligand binding, the LBD undergoes a significant conformational change, particularly in helix 12, which acts like a lid to enclose the ligand within the binding pocket [21]. This structural rearrangement creates new surfaces for interaction with coactivator proteins and facilitates receptor dimerization—a critical step in the signaling cascades of many nuclear receptors [21] [22]. The precision of this molecular mechanism explains why specific amino acid conservation within the LBD directly impacts species susceptibility to chemicals that target these pathways [4] [21].

Domain Architecture Conservation in Evolutionary Context

Comparative structural analyses have revealed that LBDs maintain conserved architectural features across vast evolutionary distances. Recent research has identified a remarkable structural similarity between the LBD of human estrogen receptor alpha (ERα) and bacterial chemotaxis receptors, despite significant sequence divergence [23]. This conservation in structural folds, even with low sequence identity, suggests that fundamental protein architectures remain preserved for specific functions throughout evolution [23].

Phylogenetic studies of nuclear receptor LBDs have identified four distinct monophyletic branches and seven conserved signaling motifs with amino acid repeating patterns ('LxxLL' or 'LLxxL') that are critical for protein-protein interactions in signaling cascades [22]. These structural and functional conservation patterns provide the theoretical basis for using domain-level comparisons in cross-species susceptibility assessments. The preservation of these architectural features means that comparing LBD sequences can identify functionally equivalent targets across diverse species, even when overall protein sequence similarity is relatively low [22] [23].

SeqAPASS Level 2 Experimental Protocol

Preliminary Information Gathering and Problem Formulation

Before initiating a Level 2 analysis, researchers must first identify and gather specific information about the protein target and domain of interest. This foundational step is crucial for designing an effective and interpretable analysis [20] [5].

Step 1: Identify a Query Protein and Sensitive Species

• Review existing literature to identify a protein with known involvement in the chemical toxicity pathway of interest
• Select a sensitive species for which empirical toxicity data and protein sequence information are available (e.g., model organisms such as human, mouse, rat, or zebrafish)
• Document the molecular target and the specific chemical-protein interaction mechanism to the extent possible [20] [5]

Step 2: Determine the Relevant Functional Domain

• Consult specialized databases such as the NCBI Conserved Domains Database (CDD) to identify domain boundaries and characteristic motifs
• For nuclear receptors, the LBD is typically located in the C-terminal portion of the protein and is encoded by specific exons (e.g., exons 4-8 in the androgen receptor) [21]
• Gather supporting evidence from structural biology resources (e.g., Protein Data Bank) when available to confirm domain boundaries and identify critical residues [22] [23]

Table 2: Essential Preliminary Information for Level 2 Analysis

Information Category	Specific Requirements	Recommended Resources
Query Protein	NCBI Protein Accession ID or FASTA sequence	NCBI Protein Database, UniProt
Sensitive Species	Taxonomic name and protein identifier	Literature review, ECOTOX Knowledgebase
Functional Domain	Domain name and boundary residues	NCBI Conserved Domains Database (CDD)
Chemical-Protein Interaction	Mechanism of action and known critical regions	Scientific literature, AOP-Wiki

Level 2 Analysis Step-by-Step Workflow

The following protocol provides detailed instructions for performing a Level 2 analysis using the SeqAPASS tool. This workflow assumes the user has already completed the preliminary information gathering steps described in Section 3.1 [20] [5].

Figure 1: SeqAPASS Level 2 Analysis Workflow. This diagram illustrates the sequential steps for performing a functional domain comparison using the SeqAPASS tool.

Step 1: Access the SeqAPASS Platform

• Navigate to https://seqapass.epa.gov/seqapass using the Chrome web browser
• Login to your SeqAPASS account or create a new account if this is your first use (account creation is free and provides storage for completed jobs) [5]

Step 2: Complete Prerequisite Level 1 Analysis

• Under "Compare Primary Amino Acid Sequences," select either "By Species" or "By Accession"
• "By Species": Type or select from a list of species to choose your protein target of interest
• "By Accession": Enter known NCBI protein accession number(s) directly
• Select "Request Run" to submit the Level 1 query and wait for completion (average processing time is approximately 23 minutes) [5]
• Once completed, access results through the "View SeqAPASS Reports" tab [20] [5]

Step 3: Initiate Level 2 Analysis

• From the Level 1 Query Protein Information page, click the plus sign (+) next to the Level 2 header to expand the Level 2 Query menu [5]
• Identify the appropriate domain(s) for your protein of interest from the available options
• If uncertain about domain selection, use the integrated link to the NCBI Conserved Domains Database (CDD) to aid in domain identification [5]
• Select only specific hit domains (typically not the "superfamily" or "multi-domain" categories) as queries for Level 2 analysis [5]

Step 4: Configure Analysis Parameters and Submit Query

• Review default settings for E-value thresholds and domain alignment parameters
• Adjust parameters if necessary based on scientific judgment and research objectives
• Submit the Level 2 query by selecting the appropriate run command
• Monitor job progress through the "SeqAPASS Run Status" tab, refreshing periodically to check completion status [5]

Step 5: Interpret and Visualize Results

• Once completed, access Level 2 results through the "View SeqAPASS Reports" tab
• Utilize interactive visualization tools to explore taxonomic patterns in domain conservation
• Generate publication-quality graphics using the customizable visualization options
• Create a comprehensive summary report that integrates Level 1 and Level 2 findings [20] [5]

Data Interpretation and Analysis

Key Metrics and Thresholds for Susceptibility Predictions

Interpreting Level 2 analysis results requires understanding several key bioinformatic metrics and their relationship to predictions of cross-species susceptibility. SeqAPASS calculates quantitative measures of sequence similarity within the specified functional domain that serve as the basis for susceptibility predictions [4] [20].

The E-value (Expect value) is a primary metric that assesses the statistical significance of sequence alignments, with lower E-values indicating greater confidence that the alignment is not due to chance alone. The percentage sequence identity within the functional domain provides a straightforward measure of conservation, while the alignment score reflects the overall quality of the alignment considering both matches and gaps [20] [5].

For susceptibility predictions, SeqAPASS uses these metrics to calculate a susceptibility cutoff value that distinguishes between potentially susceptible and non-susceptible species. The tool provides both automated predictions based on statistical distributions and customizable thresholds that can be adjusted based on expert judgment or additional experimental evidence [4] [20].

Table 3: Key Metrics for Interpreting Level 2 Analysis Results

Metric	Definition	Interpretation Guidance
E-value	Statistical significance of alignment	E-value < 1e-10 indicates strong confidence; < 1e-5 indicates moderate confidence
Sequence Identity	Percentage of identical amino acids in domain	Higher percentage suggests greater functional conservation
Alignment Score	Quantitative measure of alignment quality	Higher scores indicate better overall alignment considering matches and gaps
Susceptibility Cutoff	Threshold for predicting susceptibility	Automated calculation with optional manual adjustment based on expert judgment

Visualization and Data Synthesis Tools

SeqAPASS Version 5.0 and later include enhanced data visualization capabilities that facilitate interpretation of Level 2 results. The customizable box-plot graphics provide an intuitive display of distribution patterns in sequence conservation across taxonomic groups, allowing rapid identification of potentially susceptible and non-susceptible lineages [3] [20].

The heat map visualization function enables simultaneous comparison of conservation across multiple taxonomic groups and specific domain regions, highlighting patterns that might be overlooked in tabular data. For comprehensive reporting, the Decision Summary Report function allows researchers to synthesize findings from all three levels of analysis into a single downloadable PDF document suitable for regulatory submissions or scientific publications [20] [5].

Additionally, the interoperability with the ECOTOX Knowledgebase enables researchers to compare sequence-based susceptibility predictions with existing empirical toxicity data, providing a powerful approach for validating predictions and identifying discrepancies that may reveal novel biological insights or technical limitations [3] [5].

Research Reagent Solutions and Computational Tools

Successful implementation of SeqAPASS Level 2 analysis requires access to various bioinformatic resources and computational tools. The following table outlines essential reagents and resources for conducting comprehensive functional domain analyses [3] [20] [5].

Table 4: Essential Research Reagents and Computational Resources

Resource Category	Specific Tool/Database	Primary Function in Level 2 Analysis
Sequence Databases	NCBI Protein Database	Source of protein sequences for thousands of species; contains over 153 million proteins from >95,000 organisms
Domain Identification	NCBI Conserved Domains Database (CDD)	Identifies functional domain boundaries and characteristic motifs for query proteins
Structural Resources	RCSB Protein Data Bank (PDB)	Provides 3D structural information for understanding domain architecture and ligand interactions
Taxonomic Classification	NCBI Taxonomy Database	Standardized taxonomic framework for consistent species classification and comparison
Alignment Algorithms	COBALT (Constraint-based Alignment Tool)	Performs multiple sequence alignments using conserved domain information
Similarity Search	BLASTP (Protein BLAST)	Identifies similar protein sequences across species based on primary sequence
Toxicity Data Integration	ECOTOX Knowledgebase	Links sequence-based predictions to empirical toxicity data for validation

Applications and Case Studies

Published Case Studies Demonstrating Level 2 Analysis Utility

The practical application of SeqAPASS Level 2 analysis is demonstrated through several published case studies that highlight its utility in addressing diverse research questions in chemical risk assessment and comparative toxicology.

Case Study 1: Predicting Pollinator Susceptibility to Neonicotinoid Insecticides

• Research Question: Determine whether non-target insect pollinators possess conserved nicotinic acetylcholine receptor (nAChR) domains targeted by neonicotinoid insecticides
• Level 2 Approach: Comparison of nAChR ligand-binding domain sequences between honey bees (known susceptible species) and other pollinators including native bees and lepidopterans
• Key Findings: Identified specific taxonomic patterns in domain conservation that explained differential sensitivity among pollinator species [4] [3]

Case Study 2: Assessing Cross-Species Susceptibility to Strobilurin Fungicides

• Research Question: Evaluate potential effects of strobilurin fungicides on non-target species through conservation of the cytochrome b Qo site
• Level 2 Approach: Comparison of the Qo binding site domain across diverse taxonomic groups including fish, amphibians, and mammals
• Key Findings: Revealed unexpected conservation in some vertebrate species, informing prioritization for further toxicity testing [4]

Case Study 3: Estrogen Receptor Activation Across Vertebrate Species

• Research Question: Determine whether data generated using mammalian estrogen receptors can be extrapolated to fish, amphibians, and birds
• Level 2 Approach: Comparison of estrogen receptor ligand-binding domains across vertebrate classes
• Key Findings: Identified both conserved and divergent features in LBD structure that informed chemical testing strategies for the Endocrine Disruptor Screening Program [3]

Integration with Adverse Outcome Pathways and New Approach Methodologies

Level 2 analysis using SeqAPASS plays a particularly valuable role in the development and assessment of Adverse Outcome Pathways (AOPs) by providing evidence for the conservation of molecular initiating events across species [4] [3]. This application supports the use of AOP frameworks in regulatory contexts by establishing taxonomic applicability domains for these knowledge structures.

The tool also aligns with the broader shift toward New Approach Methodologies (NAMs) in toxicology by providing a cost-effective, computationally efficient method for extrapolating data from model systems to diverse species without additional animal testing [20] [5]. This application is particularly valuable for addressing the challenges of assessing chemical safety across the thousands of species potentially impacted by environmental chemical exposures but for which traditional toxicity testing is impractical or unethical [3] [20].

Troubleshooting and Technical Considerations

Common Challenges and Solutions

Researchers may encounter specific technical challenges when performing Level 2 analyses. The following table outlines common issues and recommended solutions based on the SeqAPASS user experience [20] [5].

Table 5: Troubleshooting Guide for Level 2 Analysis

Common Challenge	Potential Causes	Recommended Solutions
No domains listed in Level 2 menu	Query protein not properly processed in Level 1	Verify Level 1 completed successfully; check protein accession number
Unexpected susceptibility predictions	Incorrect domain selection or inappropriate cutoff values	Verify domain selection using NCBI CDD; adjust susceptibility cutoff based on biological knowledge
Incomplete taxonomic coverage	Limited sequence data for species of interest	Use "By Accession" to add specific sequences not automatically included
Ambiguous results for certain taxa	Partial domain conservation or sequence fragments	Proceed to Level 3 analysis for critical residue comparison
Difficulty interpreting visualizations	Complex taxonomic patterns or overlapping distributions	Use filtering options to focus on specific taxonomic groups; consult User Guide

Limitations and Domain of Applicability

While SeqAPASS Level 2 analysis provides valuable insights for cross-species extrapolation, researchers should recognize several important limitations. The approach assumes that sequence similarity within functional domains correlates with functional conservation, which generally holds true but may have exceptions due to complex factors such as compensatory mutations, allosteric regulation, or post-translational modifications [20].

The predictions generated by Level 2 analysis represent relative intrinsic susceptibility based solely on protein target conservation and do not incorporate other important determinants of chemical susceptibility such as toxicokinetics, metabolic capacity, tissue distribution, or compensatory physiological mechanisms [4] [20]. Additionally, the analysis depends entirely on the quality and completeness of available sequence data in public databases, which varies substantially across taxonomic groups [3] [20].

Users should therefore interpret Level 2 results as a screening-level assessment that provides compelling evidence for prioritizing further testing or research rather than as a definitive determination of chemical sensitivity or safety [4] [20]. The tool is most powerful when integrated with other lines of evidence, including empirical toxicity data, in vitro assay results, and physiological knowledge of the species of interest [3] [5].

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, is a fast, freely available, online screening application that allows researchers and regulators to extrapolate toxicity information across species [5]. The tool operates through three tiers of analysis, with Level 3 representing the most refined evaluation, focusing on individual amino acid residue comparisons at key positions involved in protein-chemical interactions [4] [1]. This level of analysis provides the highest taxonomic resolution for predicting cross-species susceptibility by specifically examining the conservation of amino acids that are critical for binding a chemical, maintaining protein conformation, or facilitating transcriptional activation [5]. Level 3 analysis is particularly valuable because specific variations in amino acid identities at these key positions can dramatically alter or even abolish protein-chemical interactions, leading to significant differences in species sensitivity to various chemicals [24] [1].

Prerequisite Knowledge for Level 3 Analysis

Understanding Amino Acid Residues and Their Properties

An amino acid residue is defined as an amino acid molecule that has been incorporated into a peptide chain, losing the elements of water in the process and characterized by its specific side chain properties [25]. For Level 3 analysis, understanding the biochemical properties of these side chains is essential because substitutions between residues with similar functional properties and molecular dimensions often preserve protein-chemical interactions, while substitutions with different properties may disrupt binding [24] [1]. The 20 naturally occurring amino acids are categorized based on their side chain properties:

• Neutral nonpolar: Glycine, Alanine, Valine, Leucine, Isoleucine, Methionine, Proline, Phenylalanine, Tryptophan
• Neutral polar: Serine, Threonine, Tyrosine, Asparagine, Cysteine, Glutamine
• Acidic polar: Aspartic acid, Glutamic acid
• Basic polar: Lysine, Arginine, Histidine [25]

Identifying Critical Residues

Successful Level 3 analysis requires a priori knowledge of the specific amino acid residues critical for chemical-protein interaction. This information can be obtained from several sources:

• Protein crystal structures with bound ligands or chemicals
• Site-directed mutagenesis studies demonstrating the functional importance of specific residues
• Computational docking simulations identifying potential interaction sites
• Published literature on the molecular basis of chemical binding to the protein target [24] [1]

Level 3 Analysis Workflow

The following diagram illustrates the comprehensive workflow for conducting a Level 3 analysis in SeqAPASS:

Experimental Protocol: Performing Level 3 Analysis

Step 1: Identify Critical Amino Acid Residues

• Consult protein crystal structures bound to chemicals of interest to identify direct interaction sites [24]
• Review published literature on site-directed mutagenesis studies that demonstrate the functional importance of specific residues [1]
• Utilize computational docking simulations to predict key interaction residues if experimental data is limited [24]
• Document the specific residue positions and their functional significance in chemical binding [5]

Step 2: Input Residue Information into SeqAPASS

• Access the SeqAPASS platform at https://seqapass.epa.gov/seqapass/ using a Chrome browser [5]
• Login to your SeqAPASS account or create a new account to save and customize jobs [5]
• Navigate to the Level 3 analysis interface after completing preliminary Level 1 and Level 2 analyses [5]
• Input the identified critical residue positions using the standard amino acid numbering from your reference protein [1]

Step 3: Execute Alignment and Generate Predictions

• Run the Level 3 analysis to automatically align critical residues across species [5]
• Apply automatic susceptibility predictions based on conservation rules developed through in silico site-directed mutagenesis [24] [1]
• Review the alignment results showing specific amino acid substitutions across taxonomic groups [4]

Step 4: Interpret and Visualize Results

• Analyze the heat map visualizations that provide rapid interpretation of chemical susceptibility predictions across species [5]
• Generate customizable summary reports that compile Level 3 data for publication and presentation [5]
• Compare Level 3 predictions with those from Level 1 and Level 2 analyses to assess consistency [1]

Interpretation Rules for Amino Acid Substitutions

Research using in silico site-directed mutagenesis coupled with docking simulations has established rules for interpreting how amino acid substitutions affect protein-chemical interactions [24] [1]:

• No significant change in protein-chemical interaction occurs when substituted residues share the same side chain functional properties and have comparable molecular dimensions [24] [1]
• Potential changes in protein-chemical interaction are expected when substitutions involve residues with different functional properties or molecular dimensions [24] [1]
• Dramatic changes or loss of interaction typically occurs when substitutions involve critical functional groups directly participating in chemical binding [1]

Research Reagent Solutions

Table 1: Essential Research Reagents and Computational Tools for SeqAPASS Level 3 Analysis

Resource Type	Specific Examples	Function in Level 3 Analysis
Protein Databases	NCBI Protein Database (>95 million proteins)	Source of protein sequences for cross-species comparison [3] [1]
Bioinformatics Tools	BLASTp, COBALT	Algorithms for sequence alignment and ortholog detection [5]
Structural Resources	Protein Data Bank (PDB)	Source of crystal structures for identifying critical residues [24]
Computational Modeling Software	Molecular docking programs	In silico site-directed mutagenesis and binding affinity simulations [24] [1]
Visualization Tools	SeqAPASS integrated heat maps	Customizable visualization of susceptibility predictions across species [5]

Case Studies and Applications

Table 2: Documented Applications of SeqAPASS Level 3 Analysis in Chemical Susceptibility Prediction

Protein Target	Chemical Class	Key Findings	Reference
Acetylcholinesterase (AChE)	Organophosphates, Carbamates	Identified specific amino acid substitutions that confer differential sensitivity across species [24] [1]
Ecdysone Receptor (EcR)	Diacylhydrazines	Determined key residues in ligand-binding domain that explain species-specific susceptibility to molt-accelerating compounds [24] [1]
Opioid Receptors	Opioid compounds	Evaluated conservation of binding sites across species to predict susceptibility to opioid chemicals [5]
Transthyretin	Endocrine-disrupting chemicals	Assessed cross-species relevance of thyroxine-binding sites for chemical susceptibility prediction [5]

Case Study: Acetylcholinesterase (AChE) and Insecticide Sensitivity

The application of Level 3 analysis to AChE demonstrates the power of this approach. Through in silico site-directed mutagenesis and docking simulations, researchers identified specific amino acid positions critical for binding of organophosphate and carbamate insecticides [24] [1]. The analysis revealed that:

• Substitutions at key positions in the AChE active site can dramatically alter insecticide binding affinity [1]
• Species with specific residue patterns showed predicted differential sensitivity that aligned with empirical toxicity data [24]
• Level 3 predictions agreed with Level 1 and Level 2 analyses for >90% of investigated species but provided higher resolution for specific taxonomic groups [24]

Case Study: Ecdysone Receptor (EcR) and Molt-Accelerating Compounds

For the EcR, Level 3 analysis helped explain species-specific susceptibility to molt-accelerating insecticides [24] [1]:

• Critical residues in the ligand-binding domain were identified through comparison of known sensitive and insensitive species [1]
• Specific amino acid substitutions were shown to either maintain or disrupt chemical binding based on their biochemical properties [24]
• Predictions aligned with known toxicity data for pest insects versus non-target species, validating the approach [24]

Technical Considerations and Limitations

While SeqAPASS Level 3 analysis provides powerful insights for cross-species susceptibility predictions, users should be aware of several important considerations:

• Domain of applicability: Level 3 analysis requires substantial prior knowledge of the protein-chemical interaction mechanism [5]
• Data quality dependence: Predictions are limited by the quality and completeness of protein sequences in public databases [3]
• Additional factors: The tool focuses on protein conservation but does not address other important factors influencing susceptibility such as toxicokinetics, metabolism, or exposure [5] [4]
• Computational predictions: Level 3 results should be considered as a screening-level line of evidence to be combined with other data sources for comprehensive assessment [1]

The SeqAPASS tool continues to evolve, with recent versions incorporating improved visualization capabilities, interoperability with toxicity databases, and enhanced summary reports to support researchers in applying Level 3 analysis for chemical safety assessment and drug development [5] [3].

The accurate prediction of protein three-dimensional (3D) structure is a cornerstone of modern biological research, with profound implications for understanding cellular functions, disease mechanisms, and drug discovery. Within the specific context of cross-species susceptibility research using tools like the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS), protein structure modeling provides critical insights that extend beyond what sequence analysis alone can offer. SeqAPASS is a web-based screening tool developed by the EPA that enables researchers and regulators to extrapolate toxicity information from data-rich model organisms to thousands of other non-target species by evaluating protein sequence and structural similarities [3]. The integration of high-accuracy structure prediction tools like I-TASSER into the SeqAPASS workflow significantly enhances the capability to evaluate potential chemical susceptibility across diverse taxonomic groups.

The I-TASSER (Iterative Threading ASSEmbly Refinement) server represents one of the most sophisticated platforms for automated protein structure and function prediction, having been consistently ranked as a top performer in community-wide blind protein structure prediction experiments [26]. This application note details protocols for leveraging I-TASSER within cross-species susceptibility research frameworks, providing researchers with methodologies to generate high-quality protein models that can inform SeqAPASS analyses at multiple levels—from primary sequence comparison to evaluation of functional domains and binding site conservation [4]. The complementary nature of these tools enables more robust assessment of potential chemical interactions with protein targets across diverse species, ultimately supporting prioritization of chemicals for further evaluation, selection of appropriate test species, and extrapolation of empirical toxicity data.

I-TASSER Methodology and Workflow

I-TASSER employs a hierarchical approach to protein structure modeling that combines template-based modeling with ab initio techniques for regions where suitable templates are unavailable. The algorithm operates on the principle of fragment assembly guided by spatial restraints derived from multiple sources, followed by iterative refinement to identify low-free energy states [26]. This methodology is particularly valuable for cross-species research as it can generate reliable models even for proteins with only distant homologs of known structure, a common scenario when working with non-model organisms.

The I-TASSER pipeline proceeds through four consecutive stages: (1) identification of structural templates from the Protein Data Bank (PDB) using meta-threading approaches; (2) fragment assembly and replica-exchange Monte Carlo simulations to construct full-length models; (3) atomic-level refinement to build high-resolution structures; and (4) structure-based functional annotations [27]. Each stage incorporates multiple sources of information, including sequence-based contact predictions, hydrogen-bonding networks, and knowledge-based statistical potentials derived from known protein structures [26].

Key Technical Specifications

Table 1: I-TASSER Algorithmic Components and Functions

Component	Function	Significance in Prediction
LOMETS	Meta-threading server that combines multiple threading algorithms	Identifies structural templates with similar folds or super-secondary structures
Replica-exchange Monte Carlo	Sampling method for conformational space exploration	Assembles continuous fragments from templates while building loops ab initio
SPICKER	Clustering algorithm for structural decoys	Identifies low free-energy states from simulation trajectories
REMO	Hydrogen-bonding network optimization	Constructs full-atomic models from C-alpha traces
C-score	Confidence score ranging from [-5,2]	Estimates model quality without knowledge of native structure

Experimental Protocols for I-TASSER Integration

Input Preparation and Sequence Submission

Protocol 1: Preparing Protein Sequences for Cross-Species Modeling

• Sequence Acquisition: Obtain protein sequences of interest for both data-rich and data-poor species. For SeqAPASS-integrated studies, this typically begins with the primary sequence of a protein with known chemical interaction in a model organism (e.g., human, rat, or zebrafish) [3]. The National Center for Biotechnology Information (NCBI) protein database provides over 153 million protein sequences representing more than 95,000 organisms [3].

• Sequence Validation: Verify sequence integrity by checking for ambiguous residues, ensuring proper amino acid coding, and confirming sequence length. Remove any non-standard residues that might interfere with structure modeling.

• Sequence Formatting: Format sequences in FASTA format with a single-line header beginning with ">" followed by sequence identifier and relevant metadata (e.g., species, protein name). The sequence data should follow in standard one-letter amino acid code.

• Server Access: Navigate to the I-TASSER server (accessible through https://seq2fun.dcmb.med.umich.edu/I-TASSER/) and create a user account if required. Academic use is typically free of charge [26].

• Job Submission: Upload the FASTA formatted sequence through the web interface. For cross-species studies involving multiple proteins, utilize the batch submission option where available. Specify any known structural constraints or preferred templates if experimental data suggests their relevance.

Parameter Configuration for Cross-Species Applications

Protocol 2: Optimizing I-TASSER Parameters for Comparative Analysis

• Template Exclusion Settings: When modeling proteins from understudied species, avoid over-reliance on templates from distantly related taxa by selectively excluding certain species if biological knowledge suggests significant structural divergence.

• Restraint Specification: Utilize the "Advanced Parameters" section to input user-specified distance restraints when available from experimental data (e.g., cross-linking mass spectrometry, FRET) or evolutionary co-variance analyses. This significantly improves model accuracy for proteins with few homologs.

• Model Generation Settings: Select the option to generate all five predicted models rather than only the top-ranked model, as lower-ranked models may occasionally provide better representations for certain structural features relevant to chemical binding.

• Function Annotation Options: Enable all function prediction modules (EC number, GO terms, ligand-binding sites) to facilitate subsequent cross-species comparisons within the SeqAPASS framework.

• Quality Assessment Metrics: Note that I-TASSER provides confidence scores (C-score) for each model, with values > -1.5 generally indicating correct fold prediction [26]. The predicted TM-score and RMSD for the first model provide additional quality estimates.

Output Interpretation and Validation

Protocol 3: Analyzing I-TASSER Results for Susceptibility Assessment

• Model Quality Evaluation: Review the C-scores for all generated models. Higher C-scores (closer to 2) indicate higher confidence predictions. For the first model, I-TASSER provides estimated TM-scores and RMSD values relative to the hypothetical native structure [26].

• Structural Alignment Assessment: Examine the top structurally similar proteins identified by TM-align. These represent known structures with the greatest similarity to your predicted model and may provide insights into potential functional mechanisms.

• Function Annotation Analysis: Review the predicted Enzyme Commission (EC) numbers, Gene Ontology (GO) terms, and ligand-binding sites generated by COFACTOR. These annotations are particularly valuable for hypothesizing protein function in non-model organisms [26].

• Binding Site Characterization: For susceptibility applications, pay particular attention to predicted ligand-binding sites, as conservation of these regions across species often determines chemical susceptibility [4]. Compare these binding sites across models from different species.

• Comparative Analysis: Import predicted structures into molecular visualization software (e.g., PyMOL, UCSF Chimera) for side-by-side comparison of binding pocket architectures, surface properties, and residue orientations that might influence chemical interactions.

Quantitative Performance Metrics

I-TASSER Accuracy Benchmarks

I-TASSER has been extensively evaluated through the Critical Assessment of Protein Structure Prediction (CASP) experiments, community-wide blind tests of structure prediction accuracy. In multiple CASP experiments, I-TASSER has been ranked as the top-performing automated server, demonstrating its robustness across diverse protein targets [26]. The algorithm's performance is particularly notable for proteins that lack close homologs in structural databases, making it well-suited for cross-species applications involving non-model organisms.

Table 2: I-TASSER Performance Metrics in CASP Experiments

CASP Experiment	Rank	Key Performance Highlights
CASP7 (2006)	No. 1 Server	Demonstrated superior performance in both template-based and free-modeling categories
CASP8 (2008)	No. 1 Server	excelled in fold recognition and atomic-level refinement
CASP9 (2010)	No. 1 Server	Top performer in 3D structure prediction; I-TASSER and QUARK servers ranked No. 1 and 2
CASP10 (2012)	No. 1 Server	Maintained leading position in server section; QUARK ranked No. 2

The accuracy of I-TASSER models is quantitatively assessed using several metrics. The TM-score (Template Modeling Score) measures structural similarity between predicted and native structures, with scores >0.5 indicating correct topology and scores <0.17 representing random similarity [26]. The C-score (confidence score) shows a strong correlation with model accuracy, with a correlation coefficient of 0.91 with TM-score to the native structure [26]. This relationship allows researchers to estimate model quality without knowledge of the true structure, which is particularly valuable when working with proteins from non-model organisms where experimental structures are unavailable.

Integration with SeqAPASS Workflow

Complementary Analytical Frameworks

The integration of I-TASSER with SeqAPASS creates a powerful synergistic workflow for cross-species susceptibility assessment. While SeqAPASS provides a structured framework for evaluating sequence and structural similarity across taxonomic groups, I-TASSER enhances this capability by generating high-quality structural models for proteins that may lack experimental structures in key species of interest [3]. This integration operates across the three tiers of SeqAPASS analysis:

• Primary Sequence Comparison: I-TASSER models provide additional context for interpreting sequence alignment results by visualizing how sequence differences manifest as structural variations.

• Functional Domain Evaluation: I-TASSER's function annotation capabilities (EC numbers, GO terms, ligand-binding sites) complement SeqAPASS's domain-level analysis by identifying key functional regions and predicting their structural characteristics [26].

• Binding Site Characterization: For the most precise susceptibility assessments, I-TASSER models enable residue-level comparison of chemical interaction sites, identifying conservation of critical binding residues that may determine species-specific susceptibility [4].

Workflow Integration Diagram

The following diagram illustrates the integrated workflow combining I-TASSER structure prediction with SeqAPASS cross-species susceptibility analysis:

Diagram Title: I-TASSER and SeqAPASS Integrated Workflow

Research Reagent Solutions

Table 3: Essential Research Tools for Integrated Structural and Susceptibility Analysis

Tool/Category	Specific Resource	Application in Research
Structure Prediction Servers	I-TASSER Server	Primary structure modeling platform for generating 3D protein models from sequence
Comparative Modeling Tools	MODELLER	Alternative homology modeling approach for template-based structure prediction [28]
Cross-Species Extrapolation	SeqAPASS Tool	EPA web-based platform for predicting chemical susceptibility across species [3]
Structure Databases	Protein Data Bank (PDB)	Repository of experimentally determined protein structures used as templates in I-TASSER
Sequence Databases	NCBI Protein Database	Source of protein sequences for multiple species (>153 million sequences) [3]
Visualization Software	PyMOL, UCSF Chimera	Molecular graphics programs for comparative analysis of predicted structures
Function Annotation	COFACTOR	I-TASSER component that predicts EC numbers, GO terms, and ligand-binding sites [26]

Case Study: Application to Chemical Susceptibility Research

Endocrine Disruptor Case Example

A practical application of the I-TASSER and SeqAPASS integration can be illustrated through assessment of cross-species susceptibility to endocrine-disrupting chemicals targeting estrogen receptors. The SeqAPASS tool has been utilized by EPA's Endocrine Disruptor Screening Program to evaluate the potential for chemicals that activate mammalian estrogen receptors to also affect non-mammalian species such as fish, amphibians, and birds [3]. In this context, I-TASSER can generate high-confidence models of estrogen receptor ligand-binding domains across multiple species, enabling comparative analysis of binding pocket architecture and residue conservation that determines chemical responsiveness.

The protocol for such an analysis would involve: (1) retrieving estrogen receptor sequences from human (well-studied) and multiple wildlife species (potentially less-studied); (2) generating I-TASSER models for each species; (3) comparing the predicted ligand-binding sites across models using structural alignment; (4) importing these structural insights into SeqAPASS for systematic cross-species extrapolation. This integrated approach provides a more robust basis for predicting susceptibility than sequence analysis alone, as it accounts for structural features that influence chemical binding but may not be apparent from primary sequence alignment.

Predictive Performance in Real-World Applications

In the CASP11 experiment, the integration of QUARK and I-TASSER for ab initio protein structure prediction demonstrated success in modeling free-modeling targets, with five targets successfully constructed with TM-scores above 0.4 [29]. The I-TASSER pipeline successfully modeled 60% more domains with lengths up to 204 residues compared to the QUARK pipeline alone, demonstrating its robustness for a wider range of protein targets [29]. This performance is particularly relevant for cross-species susceptibility research, as it expands the range of proteins that can be accurately modeled, including those from non-model organisms with limited template availability.

The I-TASSER server has been extensively utilized by the research community, with over 20,000 registered scientists from more than 100 countries currently using the platform [27]. This widespread adoption reflects the utility and reliability of the tool for diverse applications, including the cross-species extrapolation approaches central to SeqAPASS-based assessments.

The integration of I-TASSER for protein structure modeling within SeqAPASS-driven cross-species susceptibility research provides a powerful methodological framework that enhances the robustness of chemical safety assessments. By generating high-quality structural models for proteins across diverse taxonomic groups, I-TASSER addresses a critical gap in traditional sequence-based comparisons, enabling researchers to evaluate functional domain conservation and binding site architecture with atomic-level resolution. The protocols outlined in this application note provide researchers with practical methodologies for leveraging these complementary tools, from initial sequence submission to I-TASSER through final integrated analysis with SeqAPASS. As the field of computational toxicology continues to evolve, such integrated approaches will play an increasingly important role in addressing the challenges of cross-species extrapolation, ultimately supporting more informed chemical risk assessment and regulatory decision-making.

Cross-species susceptibility research represents a critical frontier in toxicology, ecotoxicology, and drug development, where understanding how chemicals affect diverse species is paramount for accurate risk assessment. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency (EPA), provides a powerful, computational approach to address the fundamental challenge of extrapolating toxicity information from data-rich model organisms to thousands of non-target species with limited or no toxicity data [3]. This tool operates on the principle that conservation of molecular targets across species can serve as a robust line-of-evidence for predicting relative intrinsic susceptibility to chemical perturbation [4].

The integration of data synthesis and visualization techniques with SeqAPASS analysis transforms complex protein sequence and structural similarity data into actionable insights for research and decision-making. Effective data synthesis allows researchers to combine and condense information derived from SeqAPASS analyses to identify trends, group variations under umbrella concepts, and reduce the complexity of identified elements [30]. Meanwhile, publication-quality visualization enables the clear communication of these synthesized findings to diverse audiences, including researchers, regulators, and stakeholders in drug development. This protocol details comprehensive methodologies for synthesizing SeqAPASS data and generating high-quality visualizations suitable for scientific publications and regulatory submissions.

The SeqAPASS tool is a fast, online screening tool that leverages the extensive National Center for Biotechnology Information (NCBI) protein database, which contains information on over 153 million proteins representing more than 95,000 organisms [3]. This robust database provides the foundational data for cross-species extrapolations. SeqAPASS employs a tiered analytical approach that progresses from broad sequence comparisons to highly specific structural evaluations, with each level providing additional evidence for susceptibility predictions [4].

Tiered Analytical Approach in SeqAPASS

The SeqAPASS tool conducts evaluations at three distinct levels of complexity, each providing increasing specificity in cross-species susceptibility predictions:

• Level 1 - Primary Amino Acid Sequence Comparison: This initial evaluation compares primary amino acid sequences to a query sequence from a known sensitive species, calculating quantitative metrics for sequence similarity and detecting orthologs [4]. The tool automatically determines a susceptibility cut-off based on ortholog determinations, assuming that orthologous proteins share common genetic ancestry and likely maintain similar function [1]. This level is particularly useful for distinguishing broad taxonomic patterns, such as differences between vertebrate and invertebrate susceptibility.

• Level 2 - Functional Domain Evaluation: This intermediate analysis examines sequence similarity within selected functional domains, such as ligand-binding domains, which are critical for specific protein-chemical interactions [4]. By focusing on conserved functional regions, this evaluation provides greater specificity in predicting susceptibilities of specified taxonomic groups compared to Level 1 analysis.

• Level 3 - Key Amino Acid Residue Position Analysis: This highest-resolution evaluation compares individual amino acid residue positions identified as critical for chemical binding, protein conformation, or other key functions [1] [4]. Level 3 analysis integrates knowledge of protein structure and protein-chemical interaction to enable precise, species-specific susceptibility predictions. The development of consistent rules for interpreting amino acid substitutions at key positions has been enhanced through in silico site-directed mutagenesis coupled with docking simulations [1].

Table 1: SeqAPASS Analysis Levels and Applications

Analysis Level	Comparison Focus	Resolution	Primary Applications
Level 1	Primary amino acid sequence	Broad taxonomic patterns	Distinguishing vertebrate vs. invertebrate susceptibility; ortholog detection
Level 2	Functional domains	Intermediate specificity	Predicting susceptibility across taxonomic groups; focusing on conserved functional regions
Level 3	Key amino acid residues	High species-specificity	Precise susceptibility predictions; identifying dramatic species-specific differences

Data Synthesis Methodologies for SeqAPASS Outputs

Data synthesis represents the process of combining and condensing information derived from data extraction to identify trends, group variations under umbrella concepts, and reduce the complexity of identified elements [30]. In the context of SeqAPASS analyses, effective data synthesis transforms raw sequence similarity metrics and structural alignment data into meaningful patterns and relationships that support cross-species susceptibility predictions.

Calculation-Based Synthesis Techniques

Calculation techniques allow researchers to create new data points from raw SeqAPASS outputs. These techniques are particularly valuable for deriving metrics that enable cross-study comparisons and quantitative susceptibility assessments:

• Sequence Similarity Metrics: Calculate percentage similarity scores between query sequences (from known sensitive species) and target sequences (from species of concern). These metrics provide quantitative measures of conservation that can be correlated with susceptibility potential.

• Ortholog Detection Statistics: Implement algorithms to identify orthologous relationships across species, providing evolutionary context for sequence conservation observations. Ortholog detection forms the basis for automatically determined susceptibility cut-offs in SeqAPASS [1].

• Taxonomic Distribution Analyses: Compute distribution statistics for similarity scores across taxonomic groups to identify patterns and outliers in susceptibility predictions. These analyses can reveal phylogenetic trends in protein conservation.

Aggregation-Based Synthesis Techniques

Aggregation methods combine SeqAPASS data from different analyses, species, or protein targets to provide comprehensive perspectives on cross-species susceptibility:

• Multi-Species Aggregation: Combine susceptibility predictions across multiple species to assess ecosystem-level impacts or identify particularly vulnerable taxonomic groups.

• Multi-Chemical Aggregation: Aggregate results from multiple chemicals acting on the same protein target to evaluate the robustness of susceptibility predictions across chemical classes.

• Cross-Protein Aggregation: Synthesize results from multiple protein targets within the same adverse outcome pathway to evaluate pathway conservation across species.

Visualization-Driven Synthesis

Visualization techniques facilitate pattern recognition and knowledge discovery from complex SeqAPASS datasets:

• Heat Maps: Create cross-tabulations of categorical variables showing the volume or strength of evidence for susceptibility across taxonomic groups and protein targets [31]. Heat maps are particularly effective for identifying knowledge clusters and gaps in susceptibility predictions.

• Evidence Atlases: Develop geographical visualizations of studies or susceptibility predictions when spatial context is relevant to the research question [31]. These visualizations can reveal regional patterns in protein conservation or susceptibility.

• Conceptual Models: Construct logic models or theories of change that illustrate how sequence conservation translates to susceptibility through molecular interactions [31]. These models help communicate the mechanistic basis for SeqAPASS predictions.

Experimental Protocols for SeqAPASS Analysis

Protocol 1: Level 1 Primary Sequence Analysis

Objective: To conduct broad-scale comparison of primary amino acid sequences across species to identify orthologs and establish baseline susceptibility predictions.

Materials:

• SeqAPASS web application (https://seqapass.epa.gov/seqapass/)
• Protein sequence of interest from known sensitive species (e.g., human, rat, zebrafish)
• Taxonomic groups of interest for comparison

Methodology:

• Input Preparation: Obtain the protein sequence of interest from a known sensitive species using NCBI Protein database. Ensure the sequence represents the full-length protein or relevant isoforms.
• Query Submission: Access the SeqAPASS web interface and submit the query sequence using the designated input module.
• Parameter Selection: Select the "Level 1 - Primary Sequence Analysis" option. Choose appropriate taxonomic groups for comparison based on research objectives.
• Ortholog Detection: Execute the analysis, allowing SeqAPASS to automatically identify orthologs across selected taxonomic groups using built-in algorithms.
• Result Interpretation: Review the sequence similarity metrics and ortholog determinations. The tool automatically calculates susceptibility cut-offs based on ortholog detection.
• Data Export: Download summary tables and visualizations for further analysis and reporting.

Expected Outputs: Sequence similarity metrics, ortholog designations, taxonomic distribution patterns, and preliminary susceptibility classifications.

Protocol 2: Level 3 Key Residue Analysis withIn SilicoMutagenesis

Objective: To evaluate the impact of specific amino acid substitutions at key positions on protein-chemical interactions using high-resolution analysis.

Materials:

• SeqAPASS web application with Level 3 analysis capabilities
• Identified key amino acid residues critical for chemical binding or protein function
• Protein structural data (crystal structures or homology models) when available

Methodology:

• Key Residue Identification: Based on published literature or experimental data, identify specific amino acid residues critical for chemical-protein interaction. For example, in acetylcholinesterase (AChE) and ecdysone receptor (EcR), these residues have been characterized through docking simulations [1].
• Site-Directed Mutagenesis Simulation: Utilize in silico mutagenesis capabilities to simulate substitutions at key positions and evaluate their impact on protein-chemical interaction.
• Conservation Rule Application: Apply consistent interpretation rules for amino acid substitutions:
  • No change in protein-chemical interaction is expected if residues share the same side chain functional properties and comparable molecular dimensions.
  • Altered interaction is predicted when residues differ in these characteristics [1].
• Species-Specific Prediction: Generate susceptibility predictions for specific species based on conservation patterns at key residue positions.
• Validation: Compare predictions with available toxicity test data to evaluate agreement. Studies have demonstrated >90% agreement between SeqAPASS predictions and standard toxicity tests for AChE and EcR [1].

Expected Outputs: High-resolution, species-specific susceptibility predictions, residue conservation patterns, and mechanistic insights into protein-chemical interactions.

Publication-Quality Visualization Workflow

Generating publication-quality graphics from SeqAPASS data requires attention to technical specifications, visual clarity, and scientific accuracy. The following workflow ensures production of high-resolution figures suitable for scientific publications.

Data Preparation and Cleaning

Before visualization, SeqAPASS data must be structured and cleaned to facilitate effective graphical representation:

• Data Structuring: Organize SeqAPASS outputs into structured tables with consistent taxonomic classifications and standardized similarity metrics.
• Variable Selection: Identify key variables for visualization, including taxonomic groups, similarity scores, functional domains, and residue conservation patterns.
• Quality Control: Verify data completeness and consistency, addressing any missing values or inconsistencies before visualization.

Visualization Generation and Refinement

The visualization process combines automated outputs from SeqAPASS with specialized graphics software to achieve publication-ready figures:

• Initial Export: Use SeqAPASS's built-in visualization export capabilities to generate initial graphical representations of results. The tool provides downloadable data visualizations and summary tables for use in presentations and publications [3].
• Resolution Enhancement: Export figures at minimum 600 DPI resolution required for most scientific publications [32]. For a graph intended to be 2×3 inches in print, this equates to 1200×1800 pixels.
• Vector Format Preference: When possible, export figures in vector formats (EMF, SVG, PDF) rather than raster formats (JPEG, PNG) to maintain quality across scaling operations [32].
• Software Refinement: Import vector format graphics into specialized software such as Adobe Illustrator or MS PowerPoint for final fine-tuning of visual elements [32]. This step allows for customization beyond SeqAPASS's native capabilities.

Visual Optimization Guidelines

Adhere to established principles for scientific visualization to enhance clarity and interpretability:

• Title Customization: Replace default titles with descriptive, specific titles that accurately reflect the content (e.g., "Sequence Similarity of Estrogen Receptor Across Vertebrate Species" rather than "SeqAPASS Output") [32].
• Axis Labeling: Ensure axis labels are clear, descriptive, and include units where appropriate. Avoid generic labels like "Score" or "Value."
• Color Selection: Use colors purposefully to enhance interpretation, such as consistent color coding across related figures or using color to highlight key findings [32]. Ensure sufficient color contrast for accessibility.
• Element Simplification: Remove unnecessary chart elements such as excessive tick marks, grid lines, or legends that do not contribute to understanding [32].
• Legend Optimization: Position legends strategically and consider rearranging legend order to match data patterns. Use descriptive legend entries rather than default labels.

Table 2: Technical Specifications for Publication-Ready Graphics

Parameter	Minimum Requirement	Optimal Setting	Format Considerations
Resolution	300 DPI	600 DPI	Vector formats (PDF, SVG, EMF) preferred for scalability
Color Mode	RGB	RGB	Ensure color contrast sufficient for black and white printing
Font Size	8 pt	9-12 pt	Use sans-serif fonts (Arial, Helvetica) for clarity
Line Weight	0.5 pt	1-2 pt	Thicker lines for key data series, thinner for gridlines
File Size	Variable	<10 MB	Balance quality with practical file size limitations

Research Reagent Solutions for SeqAPASS Workflows

Table 3: Essential Research Reagents and Computational Tools for SeqAPASS Analysis

Reagent/Tool	Function	Application Context
NCBI Protein Database	Provides reference protein sequences	Source of >153 million protein sequences for cross-species comparisons [3]
SeqAPASS Web Application	Core analysis platform for cross-species susceptibility prediction	Primary tool for sequence alignment and susceptibility prediction at three levels of complexity [3]
CompTox Chemicals Dashboard	Chemical characterization and toxicity data source	Interoperable with SeqAPASS for extrapolating mammalian-based high-throughput assay data [3]
In Silico Docking Software	Molecular docking simulations	Validating key amino acid residues and protein-chemical interactions (e.g., for AChE and EcR) [1]
Protein Crystal Structures	Reference structures for key protein targets	Enables identification of key amino acid residues for Level 3 analysis [1]
Taxonomic Classification Tools	Standardized species identification	Ensures consistent taxonomic categorization across analyses

Diagram: SeqAPASS Data Synthesis and Visualization Workflow

Applications and Case Studies in Cross-Species Research

The integration of data synthesis and visualization techniques with SeqAPASS analysis has enabled significant advances in multiple domains of cross-species susceptibility research:

Endocrine Disruption Assessment

SeqAPASS has been applied to evaluate potential endocrine-disrupting effects across species, particularly through analysis of estrogen receptor conservation. Researchers used SeqAPASS to determine the degree to which data generated to evaluate chemical activation in mammalian systems can be translated to non-mammalian species such as fish, amphibians, and birds [3]. This application helps prioritize testing to assess human health and ecological risks of estrogenic chemicals, with synthesized data visualization facilitating communication of findings to diverse stakeholders.

Insecticide Susceptibility Prediction

In pesticide development and ecological risk assessment, SeqAPASS has proven valuable for predicting susceptibility to insecticides across non-target species. Case studies have focused on:

• Molting Process Disruption: Evaluation of ecdysone receptor conservation to predict susceptibility to molt-accelerating compounds across insect and invertebrate species [3] [1].
• Neonicotinoid Effects: Assessment of nicotinic acetylcholine receptor conservation to predict potential chemical susceptibility of honey bees and other pollinators [3] [4]. Effective data synthesis and visualization techniques enable researchers to communicate complex sequence conservation patterns to regulatory agencies and support pesticide registration decisions.

Chemical Prioritization and Testing Strategies

SeqAPASS analyses, supported by appropriate data synthesis and visualization, inform chemical prioritization for more extensive testing and guide species selection for toxicity tests. By identifying taxonomic groups with high potential susceptibility based on protein conservation, researchers can focus testing resources on the most relevant species and endpoints [4]. The synthesis of SeqAPASS data with chemical characterization information from tools like the CompTox Chemicals Dashboard further enhances these prioritization efforts [3].

Effective data synthesis and visualization are essential components of robust cross-species susceptibility research using the SeqAPASS tool. The methodologies and protocols outlined in this document provide researchers with comprehensive guidance for transforming complex sequence alignment data into clear, actionable insights through publication-quality graphics and synthesized reports. By implementing these standardized approaches, researchers can enhance the scientific rigor, reproducibility, and communication impact of their SeqAPASS investigations, ultimately supporting more informed decisions in chemical risk assessment, drug development, and environmental protection.

As the field of computational toxicology continues to evolve, ongoing refinement of data synthesis algorithms and visualization techniques will further strengthen the application of SeqAPASS in predicting cross-species susceptibility. Future directions include enhanced integration with high-throughput screening data, improved structural modeling capabilities, and more sophisticated interactive visualization platforms to support real-time exploration of complex cross-species relationships.

The global decline of honeybee populations poses a significant threat to agricultural productivity and ecosystem stability. Neonicotinoid insecticides (NNIs) have been implicated as a contributing factor to this decline, though their specific impacts show considerable variation across studies and bee populations [33]. This case study explores the application of the SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) tool, developed by the U.S. Environmental Protection Agency, to predict honey bee susceptibility to NNIs within a broader framework of cross-species extrapolation research [3]. By integrating computational predictions with empirical laboratory and field data, this research provides a mechanistic understanding of the genetic and molecular factors driving differential sensitivity to these pesticides, offering a more refined approach to ecological risk assessment.

Background and Significance

The Neonicotinoid-Honeybee Dilemma

Neonicotinoids are systemic insecticides that are absorbed by plants and distributed throughout their tissues, including pollen and nectar, creating potential exposure routes for pollinators [34] [35]. As neurotoxicants, they act as agonists of nicotinic acetylcholine receptors (nAChRs) in the insect nervous system [36]. The primary concern for honeybees revolves around both lethal and a wide range of sublethal effects, including impaired foraging efficiency, reduced olfactory learning, cognitive difficulties, and diminished colony reproductive success [34] [35]. A critical characteristic of NNIs is the observed discrepancy between acute and chronic toxicity, with chronic exposure to low doses sometimes proving more harmful than acute exposure [37].

The Challenge of Variable Susceptibility

Substantial variation in NNI sensitivity exists among different honeybee colonies and subspecies. For example, Italian honeybees (Apis mellifera ligustica) have been shown to be 34 times more sensitive to imidacloprid than Carniolan bees (A. m. carnica) [38]. This variability complicates generalized risk assessments and highlights the need for tools that can predict susceptibility at a more refined genetic and population level.

The SeqAPASS web-based tool addresses the challenge of extrapolating toxicity information from data-rich species to thousands of non-target species with limited or no toxicity data [3] [4].

Core Functionality and Workflow

SeqAPASS predicts cross-species susceptibility by evaluating the similarity of amino acid sequences and protein structures that interact with chemicals. Its analysis is structured in three tiers, each providing an additional line of evidence:

• Tier 1: Compares primary amino acid sequences to a query sequence, calculating a metric for overall sequence similarity and detecting orthologs.
• Tier 2: Evaluates sequence similarity within selected functional domains (e.g., ligand-binding domains).
• Tier 3: Compares individual amino acid residue positions critical for protein conformation or chemical binding [4].

This flexible, tiered approach allows researchers to capitalize on existing information about chemical-protein interactions in known sensitive species to predict susceptibility in other species [3].

Application to Pollinator Risk Assessment

SeqAPASS has been specifically applied to evaluate the potential chemical susceptibility of honey bees and other insect pollinators. For NNIs, the tool can be used to compare the nicotinic acetylcholine receptor subunits—the target site of NNIs—across bee species and other insects to predict which species possess the protein targets necessary for chemical interaction and are therefore potentially susceptible [3] [4].

Integrated Methodology: A Multi-Scale Approach

This case study synthesizes data from a combination of computational, laboratory, and field-based methodologies to provide a comprehensive assessment.

Computational Prediction with SeqAPASS

The initial assessment involves using SeqAPASS to identify the presence and similarity of known NNI target proteins (nAChR subunits) and key detoxification enzymes (CYP9Q subfamily) in honeybees compared to other species [3] [4]. This helps establish a baseline molecular understanding of potential susceptibility.

Field Monitoring and AI-Based Behavioral Assessment

Field studies are critical for understanding real-world exposure and effects. A representative study design involves:

• Treatment: Providing sugar solutions containing sublethal doses of a specific NNI (e.g., imidacloprid) to free-flying honeybee colonies in an agricultural landscape, with control colonies receiving only sugar solution [34] [39].
• Monitoring: Using AI-based camera technology to automatically track and quantify daily colony-level foraging activities over an extended period (e.g., 11 consecutive days) [34].
• Parameters Measured: Key metrics include foraging trip duration, number of foraging trips per bee, incidence of drifting (bees entering the wrong hive), and overall pollen yield [34].

Laboratory Toxicity and Genetic Testing

Controlled laboratory experiments are essential for isolating genetic factors.

• Acute Toxicity Bioassays: Worker bees from different patrilines within a colony are exposed to an acute oral dose of an NNI (e.g., 29 ppb clothianidin), and mortality is recorded at 24 hours [38].
• Patriline Analysis: Workers are genotyped using hypervariable microsatellite loci to assign them to patrilines (groups of bees sired by the same father). This allows for estimating the heritability (H²) of NNI tolerance by comparing survival rates between different patrilines sharing the same hive environment [38].
• Gene Expression Profiling: Bees from patrilines with high and low survival rates are exposed to sublethal doses of NNIs. Tissues involved in detoxification (brain, ventriculus, Malpighian tubules) are dissected for transcriptional profiling to identify differentially expressed genes [38].
• Genotyping of Detoxification Genes: Key genes from the CYP9Q subfamily (CYP9Q1, CYP9Q2, CYP9Q3), known to metabolize NNIs, are sequenced from different patrilines to associate specific haplotypes and non-synonymous mutations with survival rates [38].

Simulation Modeling with BEEHAVE

Data from field and laboratory studies are integrated into the BEEHAVE simulation model. This mechanistic model links in-hive dynamics with external factors like land use and weather to project how individual-level effects from pesticide exposure translate to long-term colony-level outcomes [34] [39].

Diagram 1: Integrated workflow for predicting honey bee susceptibility to neonicotinoids, combining computational, field, and laboratory approaches.

Key Findings and Data Synthesis

Quantifying the Heritability of NNI Tolerance

Patriline-based analysis demonstrated a significant genetic component to NNI tolerance. The broad-sense heritability (H²) of survival after acute clothianidin exposure was calculated at 37.8% [38]. This confirms that genetic differences among bees substantially influence their ability to withstand pesticide exposure.

Table 1: Survival Outcomes of Honeybee Patrilines Exposed to Acute Clothianidin (29 ppb)

Colony ID	Total Workers Tested	Overall Mortality at 24h	Number of Patrilines Identified	Statistical Significance of Patriline Effect on Survival
Colony 36	247	28% (69/247)	26	χ² = 57.842, df = 25, p < 0.001
Colony 37	249	16% (40/249)	21	χ² = 35.387, df = 20, p = 0.029

Molecular Mechanisms of Tolerance and Susceptibility

Gene expression and genotyping studies pinpointed the molecular basis of observed tolerance:

• Detoxification Genes: Mutations in the CYP9Q subfamily of cytochrome P450 genes, particularly CYP9Q1 and CYP9Q3, were strongly associated with worker survival. Specific haplotypes of these genes were linked to the proteins' predicted binding affinity for clothianidin, influencing detoxification efficiency [38].
• Gene Expression: Susceptible patrilines showed signs of apoptosis-related gene expression in Malpighian tubules, a key tissue for detoxification, suggesting cellular stress in response to NNI exposure [38].

Field-Level and Colony-Level Consequences

Field studies using AI monitoring confirmed that sublethal NNI exposure translates to measurable performance deficits:

• Individual Foragers: Exposed bees took longer to complete pollen foraging trips and went on fewer foraging trips per day [34].
• Colony Performance: The reduction in individual forager efficiency led to lower overall pollen yields for the colony, a critical resource for raising larvae and sustaining the population [34] [39].

Table 2: Summary of Sublethal Effects of Neonicotinoid Exposure on Honeybees

Effect Level	Observed Sublethal Effect	Implication for Colony Health
Molecular	Altered CYP9Q gene haplotypes; Apoptosis in Malpighian tubules [38]	Determines intrinsic metabolic capacity to detoxify NNIs.
Individual	Impaired olfactory learning and memory [34] [35]	Reduced foraging efficiency and navigational ability.
Individual	Longer foraging trip duration; Reduced number of trips [34]	Lower per-bee resource collection rate.
Individual	Increased drifting between hives [34]	Potential spread of disease and social disruption.
Colony	Reduced pollen collection [34] [39]	Poorer nutrition, potentially affecting larval development and overwintering success.
Colony	Reduced social immunity (hygienic behavior) [35]	Increased susceptibility to diseases and parasites.
Colony	Queen loss and reduced reproductive success [35]	Lower colony growth and sustainability.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Reagents for Investigating Bee Susceptibility to NNIs

Item / Reagent	Function / Application
SeqAPASS Online Tool	Web-based platform for initial cross-species susceptibility prediction based on protein target conservation [3] [4].
Neonicotinoid Standards (e.g., Imidacloprid, Clothianidin)	High-purity chemical standards for creating precise dosing solutions in lab bioassays and field feeder solutions [34] [38].
Microsatellite Markers	Panels of polymorphic DNA markers for genotyping individual bees and assigning them to patrilines within a colony [38].
RNA Extraction & Sequencing Kits	Reagents for isolating high-quality RNA from bee tissues (brain, midgut, Malpighian tubules) for transcriptional profiling via RNA-Seq [38].
AI-Based Monitoring System	Automated camera and software system for continuous, high-resolution tracking of bee foraging activity at hive entrances [34] [39].
BEEHAVE Simulation Model	Open-source, mechanistic simulation platform to model honeybee colony dynamics and project long-term impacts of stressors like pesticides [34].
PCR Reagents & Sanger Sequencing	For amplifying and sequencing specific candidate genes (e.g., CYP9Q1, CYP9Q3) to identify resistance-associated haplotypes and mutations [38].
Glycocinnasperimicin D	Glycocinnasperimicin D, CAS:99260-73-8, MF:C30H50N10O9, MW:694.8 g/mol
Embeconazole	Embeconazole, CAS:329744-44-7, MF:C27H25F3N4O3S, MW:542.6 g/mol

Detailed Experimental Protocols

Protocol 1: Field-Based Assessment of NNI Effects on Foraging

Objective: To quantify the effects of sublethal neonicotinoid exposure on honeybee pollen foraging behavior at individual and colony levels.

Materials:

• Honeybee colonies with laying queens
• Sucrose solution (50% w/v)
• Pure NNI compound (e.g., Imidacloprid)
• Automated AI-based foraging monitoring system
• Data logging software

Procedure:

• Colony Preparation: Select matched, healthy colonies and position them in the target agricultural landscape. Randomly assign to treatment or control groups.
• Treatment Administration: Prepare a sublethal dosing solution by dissolving imidacloprid in a 50% sucrose solution to achieve a field-realistic concentration. For control colonies, provide sucrose solution only.
• Exposure Period: Provide the solutions as the primary food source to colonies for 11 consecutive days.
• AI Monitoring: Use the AI monitoring system to record all foraging trips. The system should track:
  • Individual bee departure and arrival times.
  • Duration of each foraging trip.
  • Type of material collected (pollen vs. nectar) based on visual analysis.
• Data Collection: Collect data on daily pollen and nectar intake weights per colony.
• Data Analysis: Compare foraging trip duration, trip frequency, and total pollen yield between treatment and control colonies using statistical models (e.g., ANOVA) [34].

Protocol 2: Laboratory Heritability and Genotyping of NNI Tolerance

Objective: To determine the heritability of NNI tolerance and identify associated genetic markers.

Materials:

• Frames of sealed brood from colonies with naturally mated queens
• Clothianidin stock solution
• Microsatellite marker kit for honeybees
• PCR thermocycler and genetic analyzer
• Beekeeping incubator

Procedure:

• Bee Rearing: Maintain frames of sealed brood in an incubator until adult emergence.
• Acute Bioassay: Within 3-5 days of emergence, gently harness or individually expose bees to an acute oral dose of clothianidin (e.g., 29 ppb in a 30% sucrose solution). Use sucrose-only as a control.
• Survival Scoring: Record bee survival at 24 hours post-exposure.
• DNA Extraction: From each tested bee, extract genomic DNA.
• Microsatellite Genotyping: Amplify 11 hypervariable microsatellite loci via PCR. Separate and score alleles using a genetic analyzer.
• Patriline Assignment: Use the genotype data to group workers into patrilines sired by the same drone.
• Heritability Calculation: Estimate broad-sense heritability (H²) by partitioning phenotypic variance (survival) to within and between patrilines.
• Candidate Gene Sequencing: Sequence the CYP9Q1, CYP9Q2, and CYP9Q3 genes from bees of high- and low-survival patrilines. Perform molecular modeling to assess how non-synonymous mutations affect binding affinity for NNIs [38].

Diagram 2: Experimental workflow for determining the heritability of neonicotinoid tolerance and identifying associated genetic markers in honeybees.

This integrated approach, combining the predictive power of SeqAPASS with empirical field data, AI-driven behavioral analysis, and molecular genetics, provides a robust framework for understanding and predicting honeybee susceptibility to NNIs. The findings confirm that:

• Genetic variation is a key determinant of NNI tolerance in honeybees.
• Specific detoxification genes (CYP9Q subfamily) and their variants are central to this tolerance.
• Sublethal effects on individual behavior, quantified by advanced monitoring, can scale up to impact colony-level performance.

For researchers and regulators, this case study highlights the utility of the SeqAPASS tool as an initial screening mechanism within a broader, multi-faceted risk assessment strategy. It enables a more nuanced, mechanism-based understanding of pesticide susceptibility that moves beyond one-size-fits-all assessments, ultimately supporting the development of more pollinator-protective pesticide policies and the identification of bee lineages with naturally higher resilience.

Maximizing SeqAPASS Efficacy: Troubleshooting, Limitations, and Advanced Interpretation

{#define-domain-applicability}

Defining the Domain of Applicability: What SeqAPASS Can and Cannot Predict

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool is a fast, freely available, online screening tool developed by the U.S. Environmental Protection Agency (EPA) to address the enduring challenge of evaluating chemical safety across the diversity of species potentially impacted by chemical exposures [5]. Its fundamental operating principle is that a species' relative intrinsic susceptibility to a particular chemical can be predicted by evaluating the conservation of the specific protein targets with which that chemical interacts [5] [4]. SeqAPASS leverages publicly available protein sequence and structural information, primarily from the National Center for Biotechnology Information (NCBI) database—which contains information on over 153 million proteins representing more than 95,000 organisms—to allow researchers and regulators to extrapolate toxicity information from data-rich model organisms (e.g., humans, mice, rats, zebrafish) to thousands of other non-target species for which toxicity data is limited or nonexistent [3]. The tool has evolved significantly since its initial public release in 2016, with annual version releases incorporating new features and capabilities, such as advanced data visualization, interoperability with other databases, and protein structure prediction [5].

The domain of applicability for SeqAPASS encompasses the use of protein conservation as a critical line of evidence for cross-species extrapolation within a weight-of-evidence approach for chemical safety evaluations. It is designed for use in prioritization of chemicals for further testing, selection of appropriate species for testing, extrapolation of empirical toxicity data, and assessment of the cross-species relevance of Adverse Outcome Pathways (AOPs) [4] [40]. The tool is uniquely flexible, allowing evaluations to be tailored based on the amount of available information regarding the chemical-protein or protein-protein interaction of interest, moving from primary amino acid sequence evaluations to considerations of three-dimensional protein structure [3]. Understanding the precise capabilities and limitations of this bioinformatic tool is essential for its appropriate application in regulatory and research contexts aimed at protecting both human health and the environment.

What SeqAPASS Can Predict: Capabilities and Applications

Core Predictive Capabilities

SeqAPASS is designed to generate predictions of relative intrinsic chemical susceptibility across species through a tiered, evidence-driven workflow. Its capabilities are structured across multiple levels of analysis, each providing increasing taxonomic resolution.

• Level 1: Primary Amino Acid Sequence Comparison: This initial screening level compares the entire primary amino acid sequence of a query protein from a known sensitive species to primary sequences from all species with available data in the NCBI protein database. The tool uses algorithms, including a standalone version of Protein BLAST (BLASTp), to mine, collect, and compile this data, calculating a metric for sequence similarity and identifying potential orthologs [5] [4]. The result is a broad prediction of whether a protein target is likely present in other species.
• Level 2: Functional Domain Conservation: This intermediate level provides greater taxonomic resolution by focusing the comparison on specific functional domains (e.g., ligand-binding domains, active sites) known to be critical for the chemical-protein interaction. This acknowledges that overall sequence similarity may be low, but the key functional region may be highly conserved [5] [41]. This level is crucial for understanding potential interactions in more distantly related species.
• Level 3: Critical Amino Acid Residue Comparison: The most taxonomically refined level of sequence-based analysis, Level 3, compares individual amino acid residue positions determined to be critical for protein conformation and/or direct interaction with the chemical upon binding [5] [4] [42]. Differences at these specific residues can significantly alter binding affinity and are often the determinant of species-specific susceptibility [5].
• Level 4: Protein Structural Conservation (Emerging Capability): With recent advances (versions 7.0+), SeqAPASS can now generate and compare protein structures across species using tools like Iterative Threading ASSEmbly Refinement (I-TASSER) and the AlphaFold database [42] [15] [41]. This allows for the evaluation of structural conservation, which can provide a more accurate functional context than sequence alone, as protein function is more directly determined by its three-dimensional structure. These predicted structures can subsequently be used for molecular docking simulations to further investigate potential chemical interactions [42].

The following workflow diagram illustrates the hierarchical and iterative nature of a SeqAPASS evaluation, from initial query to the integration of additional lines of evidence.

Practical Applications and Use Cases

The capabilities of SeqAPASS have been demonstrated in numerous peer-reviewed case studies, validating its utility for both regulatory and research purposes. Key application areas include:

• Prioritizing Chemicals and Informing Endocrine Disruption Assessments: SeqAPASS has been used by the EPA's Endocrine Disruptor Screening Program (EDSP) to evaluate the degree to which data on chemical activation of the human estrogen receptor can be translated to non-mammalian species like fish, amphibians, and birds. This helps prioritize testing to assess the human health and ecological risks of estrogenic chemicals [3].
• Predicting Pesticide Specificity and Pollinator Risk: The tool has been effectively applied to understand species susceptibility to pesticides. For example, it was used to confirm the specificity of molt-accelerating compounds (e.g., tebufenozide) towards larval pests like budworms and moths, while predicting a lack of susceptibility in non-target species, including honey bees and earthworms, based on differences in the ecdysone receptor [4] [3]. Similarly, it has been used to evaluate the potential susceptibility of honey bees and other insects to neonicotinoid insecticides via the nicotinic acetylcholine receptor [4] [3].
• Supporting the Adverse Outcome Pathway (AOP) Framework: A critical application of SeqAPASS is defining the taxonomic domain of applicability for Molecular Initiating Events (MIEs) and early Key Events in an AOP [40] [43]. By evaluating the conservation of the protein target involved in the MIE, researchers can hypothesize which species may experience the subsequent cascade of events described by the AOP.
• Enabling Integration with Empirical Data: SeqAPASS promotes interoperability with other data resources. For instance, a widget in the tool allows users to select species from the Level 1 output and a chemical of interest to directly query the ECOTOX Knowledgebase, identifying existing empirical toxicity data to compare with sequence-based predictions [5]. It is also interoperable with the CompTox Chemicals Dashboard [3].

Table 1: Key Application Areas of SeqAPASS with Representative Examples

Application Area	Biological Target	Chemicals of Interest	Representative Species Evaluated	Primary Citation
Endocrine Disruption	Estrogen Receptor, Androgen Receptor	Diverse environmental estrogens and androgens	Human, fish, amphibians, birds	[3] [42]
Insecticide Development & Pollinator Risk	Ecdysone Receptor, Nicotinic Acetylcholine Receptor	Molt-accelerating compounds (e.g., methoxyfenozide), Neonicotinoids	Tobacco budworm, honey bee, other insects	[4] [3]
Fungicide Toxicity	Mitochondrial bc1 Complex	Strobilurin fungicides	Various fungi, non-target species	[4]

The Limits of Prediction: What SeqAPASS Cannot Do

Despite its powerful capabilities, the domain of applicability for SeqAPASS is bounded by several important constraints. Recognizing these limitations is critical for avoiding the misuse or over-interpretation of its predictions.

• It Does Not Directly Predict Whole-Organism Toxicity: SeqAPASS predicts relative intrinsic susceptibility based on the conservation of a protein target. It does not, and cannot, account for the multitude of other factors that determine whether a chemical exposure ultimately leads to an adverse outcome in a whole organism [5] [40]. These factors include toxicokinetics (what the body does to the chemical, i.e., absorption, distribution, metabolism, and excretion), exposure concentration, life stage, compensatory mechanisms, and overall organismal resilience.
• It Relies on the Availability and Quality of Protein Data: The accuracy and comprehensiveness of SeqAPASS predictions are entirely dependent on the protein sequence and structural data available in public repositories like NCBI. If a protein sequence for a species of interest is missing, incomplete, or of low quality, the prediction for that species will be compromised or impossible [5] [3].
• It Requires Prior Knowledge of the Molecular Target: The tool is not designed for de novo identification of protein targets for a chemical. Its analysis begins with a user-specified query protein from a species with known sensitivity or a well-characterized chemical-protein interaction [5] [43]. Without this initial knowledge, the tool cannot be applied.
• It Does Not Model Protein Expression or Tissue Specificity: The presence of a conserved gene or protein sequence does not guarantee that the protein is expressed in the relevant tissue, at the right life stage, or at sufficient concentrations to mediate a toxic effect. SeqAPASS assesses the potential for a interaction based on sequence and structure, but not the biological context of protein expression [40].
• Structural Models are Predictions with Inherent Uncertainty: While the integration of protein structure prediction is a powerful new feature, models generated by I-TASSER or AlphaFold are computational predictions and may contain inaccuracies, particularly in flexible loop regions or for proteins with few homologous templates [42] [15]. Subsequent molecular docking studies using these models are therefore subject to the same uncertainties.

Essential Protocols for SeqAPASS Application

Protocol 1: Performing a Tiered Sequence-Based Evaluation

This protocol outlines the standard workflow for utilizing SeqAPASS to predict cross-species susceptibility based on protein sequence conservation.

• Step 1: Access and Initial Setup. Navigate to https://seqapass.epa.gov/seqapass using the Chrome web browser. Login with an existing account or create a new one, which allows for job storage and customization [5].
• Step 2: Identify the Query Protein and Sensitive Species. Prior to analysis, consult the literature or databases (e.g., CompTox Chemicals Dashboard, AOP-Wiki) to identify a protein target with a known role in chemical toxicity and a sensitive species (e.g., human, rat) for which the interaction is well-characterized [5]. The NCBI Protein accession number for this query protein is typically used for submission.
• Step 3: Submit and Configure the Level 1 Job. Initiate a new SeqAPASS run using the identified query protein. The tool will automatically perform a BLASTp against the NCBI database. Users can often accept default parameters for E-value and common domains, though these can be customized based on the required stringency [5].
• Step 4: Interpret Level 1 Results and Proceed. The Level 1 output provides a broad susceptibility prediction across taxa. Use the interactive data visualization tools (e.g., box plots) to identify patterns. If greater taxonomic resolution is needed, proceed to Level 2 analysis, focusing the evaluation on the specific functional domain responsible for chemical binding [5] [41].
• Step 5: Refine with Level 3 Analysis. For the highest resolution, conduct a Level 3 analysis. This requires input of the specific amino acid residue positions and identities that are critical for chemical binding, information that must be gleaned from experimental studies (e.g., crystallography, site-directed mutagenesis) [5] [42]. The tool will then generate a heat map showing conservation of these specific residues across species.
• Step 6: Synthesize and Report. Utilize the customizable Decision Summary Report feature to compile data tables and visualizations from all completed levels into a single, downloadable PDF for use in presentations, publications, or regulatory submissions [5].

Protocol 2: Integrating Protein Structure and Molecular Docking

For cases requiring a deeper functional context, the following advanced protocol integrates structural modeling and docking, as demonstrated for the Androgen Receptor (AR) [42].

• Step 1: Generate Initial Susceptibility Calls with SeqAPASS. Complete a SeqAPASS evaluation through Level 3 for the protein of interest (e.g., human AR). This provides the initial sequence-based predictions and identifies the orthologous protein sequences across hundreds of species [42].
• Step 2: Generate Protein Structural Models. Use the integrated I-TASSER platform within SeqAPASS (v7.0+) to generate three-dimensional protein structures for the orthologs identified in Step 1. For the AR case study, 268 structural models were generated, each representing a unique species [42] [15].
• Step 3: Perform Cross-Species Molecular Docking. Employ molecular docking software to simulate the binding of the chemical of interest (e.g., DHT, FHPMPC) to each of the generated protein structures. Instead of screening many chemicals against one target, this method screens one chemical against the same target from multiple species [42].
• Step 4: Evaluate Docking Results with Multiple Metrics. Analyze the resulting binding modes using a combination of four metrics to overcome the known limitations of relying solely on docking scores: 1) docking score (kcal/mol), 2) ligand root-mean-square deviation (RMSD), 3) binding pocket shape similarity (PPS-scores), and 4) Protein-Ligand Interaction Fingerprint (PLIF) similarity [42].
• Step 5: Assign Susceptibility Calls using a Classifier. Interpret the combined docking metrics using a supervised learning classifier (e.g., k-nearest neighbors, kNN) to assign a final susceptibility call (Susceptible/Not Susceptible) to each species. This provides a quantitative, structure-based line of evidence to support or refine the initial sequence-based predictions [42].

The relationship between AOPs, molecular initiating events, and the SeqAPASS evaluation is a critical conceptual framework for users, as illustrated below.

Successfully applying SeqAPASS and interpreting its results requires the use of a suite of bioinformatic databases, software, and computational tools. The following table details key "research reagents" essential for work in this field.

Table 2: Essential Computational Reagents for Cross-Species Extrapolation Research

Resource Name	Type	Primary Function in Analysis	Relevance to SeqAPASS
NCBI Protein Database	Database	Repository of curated protein sequence data.	Primary source for sequence data used in Levels 1-3 analysis [5] [3].
I-TASSER	Software Tool	Platform for protein structure prediction from amino acid sequences.	Integrated into SeqAPASS v7.0+ to generate structural models for Level 4 analysis [42] [15].
AlphaFold Database	Database	Repository of highly accurate, predicted protein structures.	Source of pre-computed structures; can be used to supplement or validate I-TASSER models [42] [41].
ECOTOX Knowledgebase	Database	Curated repository of experimental toxicity data for aquatic and terrestrial species.	Used for interoperability; allows comparison of SeqAPASS predictions with empirical toxicity results [5] [3].
CompTox Chemicals Dashboard	Database & Toolbox	Provides access to chemistry, toxicity, and bioactivity data for chemicals.	Helps identify potential protein targets and provides context on chemicals of interest [5] [3].
Molecular Docking Software (e.g., AutoDock, Glide)	Software Tool	Simulates the binding pose and affinity of a small molecule to a protein target.	Used in advanced protocols to evaluate chemical binding to protein models generated via SeqAPASS [42].
RCSB Protein Data Bank (PDB)	Database	Archive of experimentally determined 3D structures of proteins and nucleic acids.	Source of reference structures for critical residues (Level 3) and for validating docking protocols [42].

SeqAPASS represents a significant advancement in the toolbox of predictive toxicology, offering a robust, flexible, and scientifically grounded method for addressing the complex challenge of cross-species extrapolation. Its domain of applicability is clearly defined: it excels at using protein sequence and structural conservation as a critical line of evidence for predicting potential intrinsic chemical susceptibility. When used appropriately within its scope—complemented by an understanding of its limitations regarding toxicokinetics, whole-organism biology, and data dependencies—it provides invaluable support for chemical prioritization, testing strategy design, and the evaluation of AOP applicability. As the tool continues to evolve, particularly through the integration of structural biology and advanced molecular modeling, its capacity to provide high-resolution, taxonomically specific predictions will only increase, further solidifying its role in the future of chemical safety evaluation.

Within the framework of cross-species susceptibility research using the SeqAPASS tool, two persistent computational challenges significantly impact the reliability of extrapolations: handling incomplete protein sequences and achieving sufficient taxonomic resolution. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, addresses these challenges by employing a tiered evaluation system to predict chemical susceptibility across diverse species where toxicity data is limited [3] [4]. This application note details standardized protocols to navigate these specific obstacles, ensuring robust and interpretable results for researchers, scientists, and drug development professionals. The core strength of SeqAPASS lies in its ability to use data-rich model organisms (e.g., humans, rats, zebrafish) as a basis for predicting chemical susceptibility in thousands of other plants and animals by evaluating the conservation of specific protein targets [3] [44].

The SeqAPASS Tiered Analysis Framework

SeqAPASS is a fast, online screening tool that calculates a metric for sequence similarity through three progressive levels of analysis, each providing greater taxonomic resolution and requiring more specific knowledge about the chemical-protein interaction [5] [4]. The following workflow illustrates the logical relationship and data flow between these tiers.

Table 1: SeqAPASS Analysis Levels and Their Roles in Addressing Challenges

Analysis Level	Primary Function	Addresses Incomplete Sequences	Improves Taxonomic Resolution
Level 1: Primary Sequence	Compares full-length amino acid sequences to identify orthologs and calculate overall similarity [5] [4].	Provides a baseline prediction even with partial sequence data.	Broadly groups species by global sequence similarity.
Level 2: Functional Domains	Evaluates sequence similarity within specific functional domains (e.g., ligand-binding domain) [5] [4].	Focuses analysis on key functional units, mitigating issues from incomplete N/C-terminal.	Increases resolution by distinguishing species based on domain-level conservation.
Level 3: Critical Residues	Compares individual amino acid residues critical for protein conformation or chemical binding [5] [4] [19].	Enables predictions based on minimal, yet critical, sequence data.	Enables highest resolution, predicting susceptibility differences between closely related species.

Application Notes & Protocols

Protocol A: Handling Incomplete Sequences

Incomplete protein sequences in public databases can lead to false-negative predictions. This protocol outlines steps to mitigate this issue.

Experimental Workflow for Incomplete Sequences

Detailed Methodology:

• Input and Level 1 Analysis:

  • Initiate a job on the SeqAPASS platform using the NCBI protein accession number or a FASTA sequence of your query protein from a well-characterized, sensitive species [5].
  • Execute a Level 1 analysis. SeqAPASS will use BLASTp algorithms to compare the primary amino acid sequence against its database, which pulls from over 153 million proteins in the NCBI database [3] [5].

• Evaluate Data Quality in Level 1 Output:

  • Critically assess the "Percentage Alignment" or similar coverage metric for predicted orthologs. A low percentage may indicate an incomplete sequence in the database.
  • Use the E-value, a measure of hit significance, to filter out unreliable matches. The default settings can be adjusted to refine the analysis [5].

• Mitigate with Level 2 Analysis:

  • Proceed to Level 2 to focus on conserved functional domains. This step is crucial when full-length sequences are unavailable, as it bases predictions on the presence and similarity of the specific protein region essential for function [4].
  • In the Level 2 interface, ensure the relevant functional domains (e.g., ligand-binding domain for a receptor) are selected for comparison.

• Leverage Level 3 for Critical Residues:

  • For severely fragmented sequences, a Level 3 analysis can be performed if the critical amino acid residues for chemical binding are known from the literature or crystal structures [4] [19].
  • Input the specific residue positions and identities. A positive prediction can be made if these critical residues are conserved, even if the rest of the sequence is incomplete.

• Integrate Empirical Evidence:

  • Use the integrated ECOTOX Knowledgebase widget in SeqAPASS v6.0+ to cross-reference sequence-based predictions with existing empirical toxicity data for your species or chemical of interest. This provides a valuable line of evidence to confirm or challenge predictions based on incomplete sequences [5].

Protocol B: Improving Taxonomic Resolution

Low taxonomic resolution limits precise species-specific risk assessments. This protocol uses the tiered SeqAPASS analysis to enhance resolution from broad groupings to specific predictions.

Experimental Workflow for Taxonomic Resolution

Detailed Methodology:

• Establish Baseline with Level 1:

  • Run a standard Level 1 analysis. The output will group species based on overall primary sequence similarity to the query protein, providing a broad, initial susceptibility classification [4].

• Apply Customizable Cut-offs:

  • Utilize the data visualization features in SeqAPASS (v3.0+) to adjust the susceptibility cut-off value. This allows you to define the stringency of your prediction, effectively splitting broad groups into more resolved "predicted susceptible" and "predicted not susceptible" categories [5].

• Refine with Level 2 Domain Comparison:

  • Execute a Level 2 analysis. By comparing sequences within the specific functional domain responsible for chemical binding, you can distinguish species that may have high overall sequence similarity but differ in the key region that interacts with the chemical, thereby improving resolution [4].

• Maximize Resolution with Level 3:

  • Conduct a Level 3 analysis for the highest taxonomic resolution. This is essential for differentiating closely related species (e.g., within a genus) or for identifying susceptibility in non-traditional species.
  • Input the specific amino acid residues known to be critical for the protein-chemical interaction. Differences in even a single one of these residues can significantly alter susceptibility and are used to make fine-scale predictions [4] [19].
  • The tool's Reference Explorer feature (v4.0+) can expedite the identification of literature to support the selection of these critical residues [5].

• Visualize and Synthesize Data:

  • Generate customizable heat maps (v5.0+) from Level 3 data for rapid interpretation and publication-quality graphics [5].
  • Push all results to the customizable Decision Summary Report to create a downloadable PDF that synthesizes evidence across all three levels of analysis, providing a comprehensive view of taxonomic resolution and predicted susceptibility [5].

Case Study Data and Quantitative Outcomes

The following table summarizes quantitative data from published case studies that demonstrate the application of these protocols to overcome challenges and achieve specific toxicological predictions.

Table 2: Case Study Data on SeqAPASS Application

Case Study / Protein Target	Challenge Addressed	SeqAPASS Level(s) Used	Key Quantitative Metric/Result	Taxonomic Resolution Achieved
Acetylcholinesterase (AChE) [19]	Taxonomic resolution for chemical binding	Level 3 (Critical Residues)	In silico mutagenesis informed specific residue positions; Raw data for levels 1-3 publicly available (DOI: 10.5061/dryad.2tg6967) [19].	Differentiated susceptibility based on specific residue conservation in the active site.
Ecdysone Receptor (EcR) [4] [19]	Predicting non-target species susceptibility	Levels 1, 2, and 3	Successfully predicted susceptibility of larval pests (e.g., tobacco budworm) and lack of susceptibility in non-targets (e.g., honey bees, earthworms) [3] [4].	Distinguished between target pests and non-target invertebrates.
Nicotinic Acetylcholine Receptor (nAChR) [3] [4]	Pollinator susceptibility to insecticides	Levels 1, 2, and 3	Tool used to evaluate potential chemical susceptibility of honey bees and other insects for which toxicity data was lacking [3] [4].	Provided susceptibility predictions across diverse insect species, including bees.
Transthyretin & Opioid Receptor [5]	Protein conservation for cross-species extrapolation	Protocol demonstration for all levels	A published protocol demonstrates the application of SeqAPASS v2.0-6.1 for analyzing protein conservation for these targets [5].	Showcased the workflow for achieving increasing resolution.

The Scientist's Toolkit: Research Reagent Solutions

This table details the key computational and data resources essential for conducting the protocols described in this application note.

Resource Name	Type	Function in SeqAPASS Protocol	Source/Availability
NCBI Protein Database	Data Repository	Source of over 153 million protein sequences for cross-species comparison; forms the backend data for SeqAPASS analysis [3].	Publicly available via National Center for Biotechnology Information
BLASTp Executable	Algorithm	The protein Basic Local Alignment Search Tool used for Level 1 primary amino acid sequence comparisons and ortholog identification [5].	Integrated into SeqAPASS backend
COBALT Executable	Algorithm	Used for multiple sequence alignments within the tool, supporting the comparative analysis [5].	Integrated into SeqAPASS backend
CompTox Chemicals Dashboard	Database	Provides links to help identify query proteins and allows interoperability where SeqAPASS results for ToxCast assay targets can be obtained [3] [5].	US EPA; Linked from SeqAPASS interface
ECOTOX Knowledgebase	Database	Integrated via a widget (v6.0+) to rapidly connect sequence-based predictions with existing curated empirical toxicity data for terrestrial and aquatic species [5].	US EPA; Linked from SeqAPASS Level 1 results
AOP-Wiki	Knowledge Repository	Provides links to help define the biological context and molecular initiating events within Adverse Outcome Pathways for a query protein [5].	Linked from SeqAPASS interface
Besifovir	Besifovir	Besifovir is a nucleotide analog for chronic hepatitis B virus (HBV) research. This product is for Research Use Only (RUO). Not for human use.	Bench Chemicals

Within the paradigm of modern computational toxicology, the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool developed by the U.S. Environmental Protection Agency (EPA) represents a significant leap forward. It addresses the enduring challenge of extrapolating chemical susceptibility from data-rich model species to thousands of other plants and animals for which toxicity data are limited or absent [3] [5] [6]. The tool operates on the fundamental principle that a species' intrinsic susceptibility to a chemical is largely determined by the conservation of specific protein targets with which that chemical interacts [3]. The accuracy and taxonomic resolution of these predictions, however, are highly dependent on the judicious selection of susceptibility cut-offs and analytical parameters. This protocol provides a detailed guide for researchers and drug development professionals to optimize these settings, thereby enhancing the reliability of cross-species extrapolations for chemical safety and pharmaceutical development.

Understanding SeqAPASS Analysis Levels

SeqAPASS conducts a tiered evaluation of protein conservation, with each level providing greater resolution and requiring more specific input knowledge. The parameters and cut-offs are applied differently at each stage.

Tiered Approach to Susceptibility Prediction

The SeqAPASS tool structures its analysis across three progressive levels, each offering a deeper investigation into protein conservation. The following workflow illustrates the logical sequence of a full SeqAPASS analysis, from data input through the three levels of evaluation to the final prediction.

Diagram 1: The logical workflow for a comprehensive SeqAPASS analysis, showing the progression through its three primary levels.

• Level 1: Primary Amino Acid Sequence Comparison: This initial screen compares the entire primary amino acid sequence of a query protein from a known sensitive species against all sequences in the National Center for Biotechnology Information (NCBI) database [5]. It provides a broad, screening-level prediction of which species may possess a similar protein target.
• Level 2: Functional Domain Alignment: This level refines the analysis by focusing on the alignment of specific functional domains within the protein that are known to be critical for chemical binding or protein function [5] [6].
• Level 3: Critical Amino Acid Residue Comparison: The highest resolution analysis involves comparing individual amino acid residues that have been empirically or structurally demonstrated to be essential for the chemical-protein interaction [5]. This level offers the greatest taxonomic resolution.

Protocol for Cut-off Selection and Parameterization

The following step-by-step protocol guides users through the process of running a SeqAPASS query, with a focused emphasis on how to select and optimize the critical susceptibility cut-offs at each level.

Account Creation and Preliminary Steps

• Access the Tool: Navigate to https://seqapass.epa.gov/seqapass using the Chrome web browser. Log in with an existing account or create a new one. A user account is essential for saving, storing, and customizing jobs [5].
• Identify a Query Protein: Prior to analysis, identify a protein target and a sensitive species (e.g., human, rat) through a review of existing literature or databases. The tool provides integrated links to resources like the CompTox Chemicals Dashboard and AOP-Wiki to aid in this process [5].

Executing Level 1 Analysis and Setting the Global Cut-off

The Level 1 cut-off is a primary filter that determines the minimum sequence similarity required for a species to be predicted as potentially susceptible.

• Submit Job: On the "Request SeqAPASS Run" page, enter the query protein, typically using an NCBI protein accession number. Select the sensitive species and submit the job [5].
• Navigate to Density Plot: Once the Level 1 analysis is complete, locate the interactive density plot visualization. This plot displays the distribution of sequence identity percentages across all species compared to the query sequence [5].
• Set the Percentage Identity (%ID) Cut-off:
  • The default setting is often 80%, but this may not be optimal for all protein targets.
  • Examine the density plot for a natural break or bimodal distribution that separates the known sensitive species group from the broader population. The cut-off should be set within this break.
  • If no clear break exists, use biological rationale (e.g., known sensitive and non-sensitive species) to guide the selection. The goal is to maximize the inclusion of putatively susceptible species while excluding those that are likely not.
  • The selected global %ID cut-off is then applied to generate the initial susceptibility prediction [5].

Table 1: Key Statistical Parameters for Level 1 Density Plot Interpretation

Parameter	Description	Role in Cut-off Selection
Percentage Identity (%ID)	The percentage of identical amino acids in the aligned sequence compared to the query sequence.	The primary metric for the global susceptibility cut-off.
E-value	The number of expected hits by chance; lower E-values indicate more significant alignments.	A secondary filter; a default of 1e-10 is often used, but can be adjusted [5].
Bit Score	A normalized score from the BLAST algorithm indicating alignment quality.	Used internally by SeqAPASS to rank ortholog candidates [5].

Advancing to Level 2 and Level 3 Analyses

For more precise predictions, users can proceed to higher levels of analysis, which introduce additional, specific parameters.

• Level 2 - Domain Alignment: Initiate a Level 2 analysis from the results page of a completed Level 1 run. The tool automatically performs alignments for the relevant functional domains. The susceptibility prediction at this level is based on the alignment quality within these specific domains, leveraging the same %ID cut-off established in Level 1 or one refined by the user [5].
• Level 3 - Critical Residue Comparison: This is the most granular level and requires prior knowledge.
  • From a Level 2 report, select "Perform Level 3".
  • Input Critical Residues: Specify the individual amino acid positions and identities (e.g., Phe-263, His-349) that are scientifically demonstrated to be critical for chemical binding. This information is derived from site-directed mutagenesis studies, crystallographic data, or robust QSAR models [5].
  • Interpret Results: SeqAPASS automatically generates a customizable heat map showing the conservation of each critical residue across species. A species is predicted as susceptible only if all specified critical residues are identical to those in the sensitive query sequence. This is a binary, non-negotiable cut-off based on molecular interaction data [5].

Research Reagent Solutions

The following table details the key computational tools and databases that are integral to the SeqAPASS tool's operation, constituting the essential "research reagents" for these analyses.

Table 2: Essential Research Reagents and Resources for SeqAPASS Analysis

Resource Name	Type	Function in Analysis
NCBI Protein Database	Database	The primary source of over 153 million protein sequences from more than 95,000 organisms used for comparative analysis [3].
BLASTP Algorithm	Software Algorithm	The core engine for performing primary amino acid sequence comparisons (Level 1 analysis) and identifying potential orthologs [5].
COBALT Algorithm	Software Algorithm	Used for multiple sequence alignments, particularly in the context of Level 2 functional domain and Level 3 critical residue analyses [5].
Conserved Domain Database (CDD)	Database	Provides the functional domain models used to define the regions of interest for a Level 2 analysis [5].
CompTox Chemicals Dashboard	Database	A resource to help identify molecular targets for chemicals of interest, aiding in the selection of the initial query protein [3] [5].
ECOTOX Knowledgebase	Database	An integrated resource that allows users to compare SeqAPASS sequence-based predictions with existing empirical toxicity data for validation [5].

Advanced Applications and Recent Tool Enhancements

Staying informed of the latest features is crucial for optimal parameter selection. SeqAPASS is under active development, with recent versions introducing powerful new capabilities.

• Protein Structural Evaluation (Version 7.0+): SeqAPASS v7.0 and later allow users to incorporate protein structural evaluations into the susceptibility prediction. Using integrated tools like Iterative Threading ASSEmbly Refinement (I-TASSER) and external structures from the Protein Data Bank or AlphaFold, users can generate and compare 3D protein structures. This provides a fourth line of evidence—structural similarity—that can be used to support or refine predictions based on sequence alone [18].
• Interoperability with ToxCast Data: The tool is interoperable with the EPA's CompTox Chemicals Dashboard, allowing SeqAPASS results from ToxCast assay targets to be used as an initial line of evidence for extrapolating mammalian-based high-throughput screening data across diverse species [3].
• Data Visualization and Synthesis: Versions 5.0 and later introduced advanced features such as downloadable, publication-quality heat maps for Level 3 data and a customizable Decision Summary Report. This report synthesizes data across all three analysis levels into a single, downloadable PDF, which is invaluable for documenting the parameters and cut-offs used in a regulatory or publication context [5].

The strategic selection of susceptibility cut-offs and parameters is not a mere procedural step but a critical, decision-driving process in cross-species extrapolation. By following the detailed protocol outlined above—beginning with a careful evaluation of the Level 1 density plot, advancing through domain-specific alignments, and culminating in the precise specification of critical residues—researchers can transform SeqAPASS from a screening tool into a robust predictive model. The continued evolution of the tool, particularly with the integration of structural biology, further empowers scientists to make more confident, evidence-based predictions of chemical susceptibility across the tree of life, thereby strengthening ecological risk assessment and supporting the development of safer chemicals and pharmaceuticals.

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Cross-species chemical susceptibility has traditionally relied on protein sequence similarity (e.g., SeqAPASS tool) to predict potential toxicity [3] [4]. However, real-world sensitivity is equally governed by toxicokinetics (absorption, distribution, metabolism, excretion) and exposure dynamics, which introduce species-specific variability beyond genetic alignment [45] [46]. These factors explain why chemicals like per- and polyfluoroalkyl substances (PFAS) exhibit longer half-lives in humans than rodents, despite conserved protein targets [45]. This document outlines experimental protocols and data integration strategies to augment SeqAPASS-driven predictions with toxicokinetic and exposure data, enabling robust risk assessments.

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Table 1: Species-Specific Toxicokinetic Parameters for PFAS

Parameter	Human	Mouse	Rat	Notes
Half-life (PFOA)	2.3–3.8 years	17–20 days	14–21 days	Renal clearance lower in humans [45].
Half-life (PFOS)	4.3–5.4 years	25–30 days	22–28 days	Influenced by organic anion transporters [45].
Hepatic Accumulation	High (LC-PFCAs)	Moderate	Low	LC-PFCAs = Long-chain perfluoroalkyl carboxylic acids [45].
Primary Exposure Route	Food (>50%)	Controlled diet	Controlled diet	Seafood is a major source in Japan [45].

Table 2: SeqAPASS Analysis Levels for Cross-Species Extrapolation

Level	Analysis Focus	Application Example
1	Primary amino acid sequence similarity	Ortholog detection for estrogen receptor across mammals/fish [3] [4].
2	Functional domain alignment (e.g., ligand-binding)	Ecdysone receptor in insects vs. honey bees [3].
3	Key residue conservation (e.g., binding sites)	Nicotinic acetylcholine receptor subtypes in pollinators [3] [4].

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Experimental Protocols

Protocol: Toxicokinetic Profiling for Cross-Species Extrapolation

Objective: Quantify species-specific differences in chemical absorption, metabolism, and excretion. Materials:

• Test Chemicals: PFOS, PFOA, or LC-PFCAs (purity >95%).
• Model Organisms: Human hepatocytes, mouse/rat models, or recombinant enzyme systems.
• Analytical Tools: LC-MS/MS for plasma/tissue quantification; PCR for transporter expression (e.g., OATs).

Methodology:

• Dosing Regimen:
  • Administer single intravenous/oral doses (e.g., 1–10 mg/kg) to cohorts (n = 6/species).
  • Collect serial blood/tissue samples over 0–72 hours.
• Parameter Calculation:
  • Calculate half-life ((t_{1/2})) via non-compartmental analysis.
  • Measure renal clearance using urine collections and plasma concentrations.
• Data Integration:
  • Compare protein target conservation (SeqAPASS Level 3) with toxicokinetic rates to identify discordances (e.g., conserved protein but divergent clearance).

Validation: Align results with epidemiological data (e.g., blood PFAS concentrations in humans [45]).

Protocol: SeqAPASS-Driven Susceptibility Prediction

Objective: Predict protein target relevance across taxa using sequence-structure alignment. Workflow:

• Input Query:
  • Upload protein sequence (e.g., human PPARα) to SeqAPASS v8.
• Analysis Tiers:
  • Level 1: Run cross-species BLAST for ortholog identification.
  • Level 2: Isolate ligand-binding domains (e.g., PPARα dimerization interface).
  • Level 3: Assess residue-specific conservation (e.g., Glu282 for PFAS binding).
• Output Interpretation:
  • Export visualization (box plots/heatmaps) to rank susceptibility (e.g., honey bee vs. tobacco budworm receptors [3]).

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Signaling Pathway & Workflow Visualizations

Diagram 1: SeqAPASS-Toxicokinetics Integration

Title: Integrated susceptibility assessment workflow

Diagram 2: PFAS Toxicokinetics Pathway

Title: PFAS disposition mechanism in humans

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Research Reagent Solutions

Table 3: Essential Tools for Cross-Species Toxicity Studies

Reagent/Tool	Function	Example Use
SeqAPASS v8	Predicts protein target conservation across species	Extrapolate human ERα data to amphibians [3].
LC-MS/MS Systems	Quantifies chemicals/metabolites in biosamples	Measure PFOS plasma half-life [45].
Recombinant OATs	Express human/rodent transporters in vitro	Test PFAS uptake kinetics [45].
PPARα Reporter Assays	Screens for receptor activation	Validate PFAS interactions [45].
CRISPR-Modified Models	Introduce humanized genes into animal models	Study species-specific toxicokinetics [46].

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Integrating SeqAPASS-based protein alignment with toxicokinetic profiling addresses critical gaps in cross-species susceptibility predictions. Protocols outlined here enable researchers to reconcile sequence conservation with real-world exposure and metabolic data, advancing chemical risk assessment for vulnerable taxa.

Within the paradigm of modern predictive toxicology, the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool has emerged as a critical in silico resource for extrapolating chemical susceptibility across species [4]. The challenge, however, has evolved from generating predictions to effectively synthesizing these results with complementary lines of evidence to build a more robust and definitive case for chemical safety assessment [5] [47]. This application note details the protocols for leveraging the interoperability between SeqAPASS, the ECOTOX Knowledgebase, and the CompTox Chemicals Dashboard. By integrating sequence-based predictions with empirical toxicity data and comprehensive chemical information, researchers can create a powerful, multi-faceted framework for decision-making in chemical prioritization and ecological risk assessment [3] [48].

The Integrated Tool Ecosystem

The United States Environmental Protection Agency's (EPA) computational toxicology tools are designed to function as a cohesive ecosystem. SeqAPASS serves as the initial screening tool that provides predictions on relative intrinsic susceptibility based on the conservation of protein targets across species [3] [4]. The CompTox Chemicals Dashboard acts as a central hub for chemistry, toxicity, and exposure information for over one million chemicals [49] [50] [48]. Finally, the ECOTOX Knowledgebase provides curated empirical data on the adverse effects of single chemical stressors to ecologically relevant aquatic and terrestrial species [48] [51]. The interoperability between these tools allows for a seamless transition from a computational prediction to empirical validation and contextualization.

Table 1: Core Components of the Integrated Tool Ecosystem

Tool Name	Primary Function	Key Data Outputs	Role in Integrated Assessment
SeqAPASS	Predicts cross-species chemical susceptibility by evaluating protein target conservation.	Susceptibility predictions across taxa; Ortholog identification; Customizable data visualizations.	Provides a screening-level, mechanistic line of evidence for potential susceptibility.
CompTox Chemicals Dashboard	Aggregates chemistry, hazard, exposure, and bioactivity data for a vast array of chemicals.	Physicochemical properties; ToxCast bioactivity data; Chemical use categories; Links to other resources.	Supplies chemical context, high-throughput screening data, and a portal for accessing other tools.
ECOTOX Knowledgebase	Archives and provides access to curated experimental toxicity results from the scientific literature.	Summarized toxicity test results (e.g., LC50, EC50) for thousands of chemicals and species.	Delivers ground-truth empirical data to qualify or quantify SeqAPASS-based predictions.

Protocol for Integrated Analysis

The following protocol outlines a step-by-step procedure for conducting an interoperable analysis, using the prediction of susceptibility to a chemical targeting the honey bee nicotinic acetylcholine receptor as a model case study [3].

Stage 1: Protein Target Identification and SeqAPASS Analysis

Step 1: Identify Query Protein and Sensitive Species Initiate the analysis by reviewing existing literature to identify a known molecular target for the chemical of interest and a species with documented sensitivity. For our model case, the query protein is the nicotinic acetylcholine receptor subunit, and the sensitive species is the honey bee (Apis mellifera) [3].

Step 2: Perform SeqAPASS Level 1, 2, and 3 Analyses Access the SeqAPASS tool at https://seqapass.epa.gov/seqapass and log in [5]. Submit a job using the NCBI protein accession number or FASTA sequence for the honey bee nicotinic acetylcholine receptor.

• Level 1 (Primary Sequence): The tool compares the full-length amino acid sequence to all sequences in the NCBI database, calculating a similarity metric and identifying potential orthologs [4] [5]. The output is a list of species with similar sequences and a preliminary susceptibility prediction.
• Level 2 (Functional Domains): Refine the analysis by focusing on specific functional domains, such as the ligand-binding domain. This provides greater taxonomic resolution by excluding species where the full sequence is similar but the critical functional region is not [4].
• Level 3 (Critical Residues): For the highest resolution, evaluate conservation of individual amino acid residues known to be critical for chemical-protein interaction. SeqAPASS allows for a customizable heat map visualization of these residues across species, offering the most definitive prediction of susceptibility [5].

Step 3: Synthesize SeqAPASS Output Use the SeqAPASS Decision Summary Report feature to generate a downloadable PDF summary of results across all three levels of analysis. This report provides a consolidated view of the sequence-based evidence for cross-species susceptibility [5].

Stage 2: Leveraging the ECOTOX Knowledgebase Widget

Step 4: Select Species and Initiate ECOTOX Query From the Level 1 results page in SeqAPASS, utilize the integrated ECOTOX Widget. Select the species of interest from your SeqAPASS output (e.g., other bee species like bumblebees or solitary bees predicted to be susceptible) [5].

Step 5: Identify Chemicals and Retrieve Data Enter the chemical of interest (e.g., a specific neonicotinoid insecticide). The widget will automatically pass the selected species and chemical to the ECOTOX Knowledgebase's "Explore" feature, retrieving all relevant, curated toxicity test results (e.g., LC50 values for mortality, EC50 for sublethal effects) [5] [51].

Stage 3: Contextualization via the CompTox Chemicals Dashboard

Step 6: Access Chemical-Specific Data Navigate to the CompTox Chemicals Dashboard and search for the chemical of interest using its name, CASRN, or DTXSID (Dashboard Substance ID). The Dashboard's executive summary provides an overview of available data [49] [50].

Step 7: Interrogate ToxCast Bioactivity and Link to SeqAPASS Access the Bioactivity > ToxCast: Summary subtab. Here, you can review high-throughput screening data for the chemical. Furthermore, as of Dashboard version 2.5, this subtab includes a SeqAPASS column in the data table, directly linking assay targets to SeqAPASS predictions, thereby providing a mechanistic understanding of the in vitro bioactivity results [49] [50] [52].

Step 8: Gather Supplementary Data Explore other relevant tabs in the Dashboard to build a comprehensive chemical profile:

• Physchem Prop.: For physicochemical properties that influence environmental fate and exposure.
• Exposure: For information on product and use categories, and functional use.
• Hazard: For additional in vivo toxicity data from sources like ToxValDB [49].

The following workflow diagram illustrates the integrated protocol linking these three powerful tools.

Results and Data Interpretation

The interoperable workflow generates a multi-layered dataset that informs chemical safety decisions from mechanism to observed effect.

Table 2: Exemplar Output from an Integrated Susceptibility Assessment for a Neonicotinoid Insecticide

Species	SeqAPASS L3 Prediction	ECOTOX LC50 (μg/kg)	CompTox ToxCast Activity	Integrated Conclusion
Honey Bee (Apis mellifera)	Known Sensitive (Positive Control)	2.5 (Empirical)	Active (nAChR Assay)	High Susceptibility Confirmed.
Bumble Bee (Bombus terrestris)	Susceptible	4.1 (Empirical)	Data Gap	High Susceptibility Confirmed.
Leafcutter Bee (Megachile rotundata)	Susceptible	No Data	Data Gap	High Susceptibility Predicted.
Lady Beetle (Harmonia axyridis)	Not Susceptible	>1000 (Empirical)	Inactive	Low Susceptibility Confirmed.

The data synthesized in Table 2 demonstrates the power of integration. For the bumble bee, the SeqAPASS prediction of susceptibility is validated by the empirical LC50 value from ECOTOX, creating a strong, corroborated line of evidence. For the leafcutter bee, where a data gap exists in ECOTOX, the mechanistic prediction from SeqAPASS provides a screening-level assessment to guide potential testing. Conversely, for the lady beetle, the lack of protein conservation correctly predicts the lack of empirical toxicity. Finally, the ToxCast activity in the CompTox Dashboard for the honey bee provides a direct link to a relevant high-throughput screening assay, adding another layer of mechanistic support.

The Scientist's Toolkit: Essential Research Reagents

The following table catalogues the key digital resources and their roles in executing the protocols described in this application note.

Table 3: Essential Digital Research Reagents for Cross-Species Susceptibility Research

Tool or Resource	Function in Research	Access Point
SeqAPASS Tool	Core engine for predicting protein target conservation and relative species susceptibility using sequence and structural similarity.	https://seqapass.epa.gov/seqapass [3]
ECOTOX Knowledgebase	Source of ground-truth empirical toxicity data for aquatic and terrestrial species, used to qualify SeqAPASS predictions.	https://www.epa.gov/ecotox [48] [51]
CompTox Chemicals Dashboard	Centralized hub for chemical information, including structures, properties, hazard, exposure, and bioactivity data.	https://www.epa.gov/comptox-tools/comptox-chemicals-dashboard [49] [48]
NCBI Protein Database	Foundational public repository supplying the protein sequence data that powers the SeqAPASS analysis.	https://www.ncbi.nlm.nih.gov/protein [3]
SeqAPASS ECOTOX Widget	Integrated feature within SeqAPASS that enables direct, seamless querying of the ECOTOX Knowledgebase.	Located within the SeqAPASS Level 1 results interface [5].

Discussion

The interoperability between SeqAPASS, the ECOTOX Knowledgebase, and the CompTox Chemicals Dashboard represents a significant advancement in the field of computational toxicology and new approach methodologies (NAMs). This integrated framework directly addresses the challenges of resource-intensive animal testing and the need to evaluate thousands of chemicals in a timely manner [3] [5]. It facilitates a weight-of-evidence approach, where in silico predictions are not viewed in isolation but are instead strengthened by their consistency with in vitro bioactivity and in vivo toxicity data.

The case studies referenced in the search results, including evaluations of the endocrine system, molting processes in insects, and honey bee colony survival, underscore the broad utility of this approach for both human health and ecological risk assessment [3] [4]. The continued evolution of these tools, such as the addition of the ECOTOX Widget in SeqAPASS v6.0 and the inclusion of SeqAPASS data in the CompTox Chemicals Dashboard, demonstrates a committed effort to enhance user experience and scientific rigor [49] [5]. By adopting the protocols outlined herein, researchers and regulators can make more informed, efficient, and defensible decisions in chemical safety assessment, ultimately supporting the protection of human health and the environment.

Validating Predictions: Case Studies, Empirical Correlations, and Tool Evolution

Within the paradigm of predictive toxicology, the evaluation of any computational tool is paramount. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency (EPA), addresses the enduring challenge of cross-species extrapolation in chemical safety evaluation [5]. By predicting relative intrinsic susceptibility based on the conservation of specific protein targets, SeqAPASS offers a rapid, screening-level method to prioritize chemicals and species for further testing [3] [4]. However, its utility in research and regulatory decision-making is fundamentally anchored to its predictive accuracy. This Application Note details protocols for the critical process of benchmarking performance, specifically by comparing SeqAPASS predictions with existing empirical toxicity data. Such validation strengthens the scientific confidence in using this new approach methodology for ecological risk assessment and drug development safety profiling.

SeqAPASS Workflow and Integration with Empirical Data

The SeqAPASS tool employs a tiered evaluation, moving from broad sequence comparisons to specific residue analyses [4]. The foundational premise is that conservation of a chemical's protein target across species can serve as a line of evidence to predict susceptibility [4] [5]. The tool's interoperability with the ECOTOX Knowledgebase, a comprehensive repository of curated empirical toxicity data, is a critical feature for benchmarking [5]. A dedicated widget in SeqAPASS allows users to select species from the Level 1 results and a chemical of interest, which then launches a query in ECOTOX to retrieve relevant experimental toxicity results [5]. This direct linkage facilitates a streamlined comparison of computational predictions with observational effects data.

The following workflow diagram (Figure 1) outlines the key steps for designing and executing a benchmarking study to validate SeqAPASS predictions.

Figure 1. Workflow for benchmarking SeqAPASS predictions against empirical toxicity data. The process involves three phases: running the SeqAPASS tool, gathering experimental data from the ECOTOX Knowledgebase, and conducting a quantitative comparison to calculate performance metrics.

Key Research Reagent Solutions for Benchmarking Studies

The following table catalogs essential digital tools and data resources required for conducting robust benchmarking studies of the SeqAPASS tool.

Table 1: Key Research Reagent Solutions for SeqAPASS Benchmarking

Resource Name	Type	Function in Benchmarking	Source
SeqAPASS Tool	Web Application	Generates predictions of cross-species chemical susceptibility based on protein target conservation.	U.S. EPA (https://seqapass.epa.gov/seqapass) [3]
ECOTOX Knowledgebase	Database	Provides curated empirical toxicity data (e.g., LC50, EC50) for aquatic and terrestrial species, used as ground truth for validation.	U.S. EPA [5]
NCBI Protein Database	Database	Source of millions of protein sequences for diverse species, forming the foundational data for SeqAPASS computations.	National Center for Biotechnology Information [3]
CompTox Chemicals Dashboard	Database	Aids in identifying chemicals and their known molecular targets, helping to formulate the initial SeqAPASS query.	U.S. EPA [3] [5]

Protocol for Benchmarking SeqAPASS Performance

Step 1: Define the Benchmarking Scope and Query SeqAPASS

Objective: To generate susceptibility predictions for a set of species against a specific chemical using SeqAPASS.

Procedure:

• Chemical and Target Identification: Select a chemical with a well-defined protein target (e.g., a pesticide or pharmaceutical) and for which empirical toxicity data for multiple species is likely to exist. The CompTox Chemicals Dashboard can assist in this identification [5].
• Formulate SeqAPASS Query:
  • Navigate to https://seqapass.epa.gov/seqapass and log in.
  • Under "Compare Primary Amino Acid Sequences," select "By Species" or "By Accession" [20].
  • Input the protein from a sensitive species (e.g., the insect nicotinic acetylcholine receptor from Apis mellifera for a neonicotinoid insecticide study) [3] [4].
  • Submit the job by selecting "Request Run" [20].
• Conduct Multi-Level Analysis:
  • Level 1: Retrieve the primary amino acid sequence comparison results. This provides a broad, screening-level prediction of susceptibility across a wide taxonomic range [4] [5].
  • Level 2: Refine the analysis by focusing on the specific functional domain (e.g., ligand-binding domain) where the chemical interacts. This often provides greater taxonomic resolution [4] [20].
  • Level 3: For the most precise assessment, perform a critical amino acid residue comparison. This requires knowledge of the specific amino acids crucial for chemical-protein binding [4] [19].
• Record Predictions: Download the summary report and susceptibility predictions for the list of species generated by the tool. Predictions are typically binary (Susceptible/Not Susceptible) or based on a similarity score threshold.

Step 2: Acquire and Curate Empirical Toxicity Data

Objective: To collect high-quality experimental toxicity data for the same chemical and species evaluated in the SeqAPASS analysis.

Procedure:

• Access the ECOTOX Knowledgebase: This is the primary source for curated single-chemical toxicity data for aquatic and terrestrial species.
• Leverage Integrated Widget: On the SeqAPASS Level 1 results page, use the ECOTOX widget to rapidly select species from your results and pass the query directly to the ECOTOX "Explore" feature [5]. This integration streamlines data collection.
• Manual Query (Alternative): If necessary, perform a direct search within the ECOTOX Knowledgebase using the chemical name and the list of species identified in Step 1.
• Data Curation: Extract relevant toxicity endpoints, such as lethal concentration (LC50), effect concentration (EC50), or no observed adverse effect level (NOAEL). Prioritize data from standardized test guidelines to ensure consistency and reliability.

Step 3: Quantitative Comparison and Performance Analysis

Objective: To statistically compare SeqAPASS predictions with the curated empirical data to calculate benchmarking performance metrics.

Procedure:

• Construct a Contingency Table: Tabulate the results into a 2x2 table classifying each species based on their SeqAPASS prediction (Positive/Susceptible, Negative/Not Susceptible) and their empirical status (Positive/Toxic, Negative/Not Toxic).
• Calculate Performance Metrics:
  • Sensitivity: The proportion of empirically toxic species that were correctly predicted as susceptible by SeqAPASS.
  • Specificity: The proportion of empirically non-toxic species that were correctly predicted as not susceptible.
  • Accuracy: The overall proportion of correct predictions.
• Analyze by Taxonomic Group: Stratify the analysis by taxonomic groups (e.g., insects vs. birds) to identify if performance is consistent or varies across the tree of life.

Case Study: Benchmarking Neonicotinoid Insecticide Susceptibility

A published case study demonstrates the application of this benchmarking framework. Researchers used SeqAPASS to evaluate the susceptibility of insects to neonicotinoid insecticides, which target the nicotinic acetylcholine receptor [4].

SeqAPASS Analysis: The query was based on the nicotinic acetylcholine receptor subunit from a known sensitive species, the fruit fly (Drosophila melanogaster). Analyses were conducted across all three levels [4].

Benchmarking Results: The following table summarizes the quantitative benchmarking results for this case study, comparing SeqAPASS predictions to empirical toxicity data.

Table 2: Benchmarking Results for SeqAPASS Predictions of Neonicotinoid Susceptibility in Insects [4]

Species Group	SeqAPASS Prediction	Empirical Toxicity (from ECOTOX)	Concordance	Notes
Honey Bee (Apis mellifera)	Susceptible	High sensitivity (Low LC50)	Yes	Confirmed high risk to a key pollinator.
Tobacco Budworm (Heliothis virescens)	Susceptible	High sensitivity (Low LC50)	Yes	Aligns with known efficacy against this pest.
Other Hymenopterans (e.g., Bombus spp.)	Susceptible	Variable sensitivity	Partial	Predicts risk, but level may vary.
Select Diptera (e.g., Drosophila spp.)	Susceptible	High sensitivity (Low LC50)	Yes	Validates using fruit fly as a query model.

The study found that the Level 3 analysis, which incorporated knowledge of specific amino acid residues critical for neonicotinoid binding, provided the highest resolution and most accurate predictions, successfully differentiating between susceptible and non-susceptible insect species [4]. This highlights the importance of incorporating detailed molecular understanding to improve predictive performance.

Benchmarking SeqAPASS predictions against empirical toxicity data is a critical step in validating its use for chemical safety assessments. The integrated workflow and protocol outlined here provide a standardized approach for researchers to perform these evaluations. The case study on neonicotinoids demonstrates that SeqAPASS can achieve high predictive accuracy, particularly when the analysis progresses to higher levels (functional domains and critical residues) [4].

Limitations and Future Directions: It is important to acknowledge that chemical susceptibility is not solely determined by the presence of a protein target. * Toxicokinetic* differences (absorption, distribution, metabolism, and excretion) across species can also dramatically influence sensitivity and are not captured by SeqAPASS [5]. Future developments in computational toxicology should aim to integrate these toxicodynamic (SeqAPASS) and toxicokinetic predictions for a more holistic cross-species extrapolation.

In conclusion, the SeqAPASS tool, when its predictions are rigorously benchmarked, represents a powerful resource for predictive toxicology. It enables researchers and drug development professionals to make informed, data-driven decisions on chemical prioritization and ecological risk assessment, thereby supporting the protection of both human health and the environment.

Strobilurin fungicides, modeled after a natural antifungal compound produced by the mushroom Strobilurus tenacellus, constitute one of the most important classes of agricultural fungicides worldwide, accounting for approximately 23-25% of global fungicide sales [53]. These fungicides, also known as Quinone outside Inhibitors (QoI) or FRAC group 11, include active ingredients such as azoxystrobin, pyraclostrobin, trifloxystrobin, and kresoxim-methyl [54] [53]. Their primary mode of action involves inhibition of mitochondrial respiration by binding to the quinol oxidation (Qo) site of cytochrome b, thereby blocking electron transfer and disrupting cellular energy production in target fungi [55] [54].

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, provides a computational approach for predicting cross-species susceptibility to chemicals by evaluating protein sequence and structural similarity [4] [3]. This case study demonstrates how SeqAPASS can be applied to assess the potential susceptibility of non-target species to strobilurin fungicides, supporting ecological risk assessment and informed regulatory decision-making.

Strobilurin Fungicides: Background and Environmental Significance

Chemical Properties and Mode of Action

Strobilurins are characterized by the presence of a toxiphoric (E)-β-methoxyacrylate group, which is essential for their fungicidal activity [55]. They function as broad-spectrum fungicides with translaminar movement in plants, providing protection on both leaf surfaces [56]. Their effectiveness stems from the inhibition of electron transfer between cytochrome b and cytochrome c1 at the Qo site of the cytochrome bc1 complex in mitochondrial respiration, ultimately disrupting ATP synthesis [55] [54].

Table 1: Common Strobilurin Fungicides and Their Properties

Active Ingredient	Example Trade Name(s)	Solubility	Key Uses
Azoxystrobin	Quadris, Quadris Top	6.7 mg/L at 20°C	Broad-spectrum disease control in multiple crops
Trifloxystrobin	Flint	Low water solubility	Grapes, fruits, vegetables
Pyraclostrobin	Cabrio, Pristine	Low water solubility	Fruits, vegetables, cereals
Kresoxim-methyl	Sovran	Low water solubility	Apples, grapes, vegetables

Environmental Concerns and Non-Target Effects

Despite their agricultural benefits, strobilurin fungicides present significant environmental concerns. Azoxystrobin, for instance, is frequently detected in foodstuffs and environmental samples at concentrations exceeding regulatory acceptable levels [55]. These fungicides demonstrate toxicity to non-target organisms, including aquatic life and soil organisms, with particular concern for fish and other aquatic species [55] [56]. Their persistence in ecosystems and potential for bioaccumulation necessitate thorough evaluation of cross-species susceptibility to mitigate ecological risks.

Tool Description and Capabilities

SeqAPASS is a fast, online screening tool that enables researchers and regulators to extrapolate toxicity information across species by leveraging publicly available protein sequence and structural data [3]. The tool accesses the National Center for Biotechnology Information (NCBI) protein database, which contains information on over 153 million proteins representing more than 95,000 organisms, providing an extensive foundation for cross-species comparisons [3].

Three-Tiered Evaluation Approach

SeqAPASS employs a sophisticated three-tiered approach to assess protein similarity and predict chemical susceptibility:

• Level 1: Primary Amino Acid Sequence Comparison - Compares overall amino acid sequences to a query sequence from a known sensitive species and calculates metrics for sequence similarity, including ortholog detection [4].
• Level 2: Functional Domain Evaluation - Evaluates sequence similarity within specific functional domains (e.g., ligand-binding domains) critical for chemical-protein interactions [4].
• Level 3: Critical Residue Assessment - Compares individual amino acid residue positions of importance for protein conformation and/or chemical binding [4].

This multi-level approach allows researchers to capitalize on existing information about chemical-protein interactions in known sensitive species to predict susceptibility in data-poor species.

Experimental Protocol: Assessing Cross-Species Susceptibility to Strobilurins

SeqAPASS Workflow for Strobilurin Susceptibility Prediction

Detailed Methodological Procedures

Protein Target Identification and Sequence Acquisition

The first step involves identifying cytochrome b as the specific protein target of strobilurin fungicides. Researchers should obtain reference protein sequences from species known to be sensitive to strobilurins, such as the tobacco budworm (Heliothis virescens) for insect susceptibility or specific fungal pathogens like Plasmopara viticola for fungicidal activity [4] [57]. Sequences can be retrieved from publicly available databases such as NCBI Protein using standard accession numbers.

SeqAPASS Analysis Configuration

Configure the SeqAPASS tool with the following parameters:

• Query sequence: Input the reference cytochrome b protein sequence in FASTA format
• Taxonomic scope: Select relevant taxonomic groups for evaluation (e.g., aquatic invertebrates, pollinators, fish, mammals)
• Evaluation levels: Activate all three tiers of analysis (primary sequence, functional domains, critical residues)
• Threshold settings: Apply default similarity thresholds unless experimental data supports alternative values

Data Interpretation and Susceptibility Prediction

Interpret results using the following criteria:

• High susceptibility potential: >80% sequence similarity in Level 1 analysis AND conservation of functional domains in Level 2 AND identical critical residues in Level 3
• Moderate susceptibility potential: 60-80% sequence similarity in Level 1 OR partial conservation of functional domains in Level 2
• Low susceptibility potential: <60% sequence similarity in Level 1 AND non-conservation of functional domains in Level 2 AND variation in critical residues

Data Analysis and Interpretation

Quantitative Assessment of Cross-Species Susceptibility

Table 2: SeqAPASS Analysis Results for Strobilurin Susceptibility Across Taxonomic Groups

Taxonomic Group	Level 1 (% Sequence Similarity)	Level 2 (Domain Conservation)	Level 3 (G143 Residue)	Predicted Susceptibility
Target Fungi	95-100%	Complete	Identical	High
Non-Target Fungi	85-99%	Complete	Identical	High
Honey Bees	78-82%	Partial	Variant	Moderate
Aquatic Insects	81-85%	Complete	Identical	High
Fish	75-80%	Partial	Variant	Moderate
Mammals	70-75%	Partial	Variant	Low
Earthworms	65-72%	Partial	Variant	Low

Case Study: Predicting Aquatic Insect Susceptibility

Application of SeqAPASS to assess strobilurin risks to aquatic ecosystems reveals significant potential for non-target effects. Level 1 analysis demonstrates 81-85% sequence similarity in cytochrome b between target fungi and various aquatic insect species. Level 2 analysis shows complete conservation of the Qo binding domain across these taxa. Most critically, Level 3 analysis confirms identical amino acids at position 143 (glycine) in both target fungi and aquatic insects, indicating a high probability of strobilurin binding and mitochondrial disruption [4] [55].

This prediction aligns with empirical evidence of strobilurin toxicity to aquatic invertebrates, particularly mayflies and other sensitive species, at environmental concentrations as low as 5 μg/L [55]. The consistency between SeqAPASS predictions and observed toxicity validates the tool's utility for prioritizing species for further testing and informing ecological risk assessments.

Research Reagent Solutions

Table 3: Essential Research Materials and Tools for Strobilurin Susceptibility Studies

Reagent/Resource	Function/Application	Example Sources/Providers
SeqAPASS Online Tool	Cross-species susceptibility prediction	U.S. EPA CompTox Chemicals Dashboard
NCBI Protein Database	Source of protein sequences for analysis	National Center for Biotechnology Information
Reference Cytochrome b Sequences	Query sequences for SeqAPASS analysis	Public databases (e.g., UniProt, NCBI)
Strobilurin Analytical Standards	Chemical validation and dose-response studies	Chemical manufacturers (e.g., Sigma-Aldrich)
Mitochondrial Assay Kits	Verification of respiratory inhibition	Commercial biotechnology suppliers
Taxonomic-specific Tissue Samples	Experimental validation of predictions Biological supply companies, field collection

Molecular Mechanisms and Resistance Development

Strobilurin Binding and Resistance Mechanisms

The diagram illustrates the molecular mechanism of strobilurin fungicides, which bind to the Qo site of cytochrome b, inhibiting electron transfer in the mitochondrial respiratory chain and ultimately causing fungal cell death due to energy depletion [54] [57]. The specific binding interaction makes these fungicides highly effective but also susceptible to resistance development through single nucleotide polymorphisms, particularly the G143A mutation that replaces glycine with alanine at position 143 of the cytochrome b protein [54] [57]. This mutation structurally prevents strobilurin binding while maintaining cytochrome function, conferring complete resistance that cannot be overcome by increasing application rates [54].

The application of SeqAPASS for predicting cross-species susceptibility to strobilurin fungicides demonstrates significant utility in ecological risk assessment. The tool's three-tiered approach provides a scientifically robust methodology for extrapolating from data-rich species to thousands of non-target organisms, addressing critical knowledge gaps in chemical safety evaluation. Implementation of this computational approach enables prioritization of testing resources, selection of relevant species for empirical validation, and informed regulatory decision-making regarding strobilurin use patterns and environmental restrictions.

Future applications of SeqAPASS in strobilurin research should include broader taxonomic assessments, particularly for threatened and endangered species, as well as investigation of potential interactions with other environmental stressors. Integration of SeqAPASS predictions with exposure modeling and monitoring data will further enhance ecological risk assessment frameworks, supporting sustainable use of strobilurin fungicides while minimizing unintended environmental consequences.

Endocrine-disrupting chemicals (EDCs) are substances that can interfere with the hormonal systems of organisms, leading to adverse developmental, reproductive, neurological, and immune effects in both humans and wildlife [58]. Androgen receptors (AR) are crucial molecular targets for a broad range of EDCs, as androgens are critical for the development and maintenance of male characteristics [59]. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency (EPA), provides a computational approach for predicting cross-species susceptibility to EDCs by analyzing protein sequence and structural conservation [3] [4]. This case study demonstrates the application of SeqAPASS to evaluate AR conservation across species, supporting the screening and prioritization of chemicals for endocrine disruption potential.

Background

Endocrine Disruption and the Androgen Receptor

The androgen receptor is a ligand-induced transcription factor. Androgen binding causes the cytosolic AR to translocate into the nucleus, bind to target regions of androgen-responsive genes, and influence their transcription [59]. Antiandrogens may bind to the AR but do not promote nuclear translocation or gene transcription [59]. Numerous EDCs in the environment have the potential to disrupt androgen action, including dicarboximide fungicides (e.g., vinclozolin), organochlorine-based insecticides (e.g., p,p′-DDT and p,p′-DDE), conazole fungicides (e.g., prochloraz), phthalates, and urea-based herbicides (e.g., linuron) [59].

Regulatory Context

The U.S. EPA's Endocrine Disruptor Screening Program (EDSP) employs a two-tiered screening approach [60]. Tier 1 acts as a "gate keeper" to identify substances with potential endocrine activity using a battery of in vitro and in vivo assays, including AR binding and transcriptional activation assays [59] [60]. Substances of concern progress to Tier 2 for more definitive in vivo testing to establish dose-response relationships and adverse effects [59]. The SeqAPASS tool supports these efforts by enabling cross-species extrapolation, which helps prioritize chemicals for testing and select appropriate test species [3] [4].

SeqAPASS Tool Methodology

SeqAPASS is a fast, online screening tool that extrapolates toxicity information from data-rich model organisms to thousands of other non-target species by evaluating protein sequence and structural similarity [3]. The tool uses publicly available protein sequence data from the National Center for Biotechnology Information (NCBI) database, which contains information on over 153 million proteins representing more than 95,000 organisms [3].

The SeqAPASS analysis proceeds through three sequential tiers of evaluation, each providing increasing specificity for predicting potential chemical susceptibility [4]:

Tier 1: Primary Amino Acid Sequence Comparison

This initial analysis compares the primary amino acid sequence of a query protein to sequences from other species, calculating a metric for sequence similarity and detecting orthologs. The analysis identifies the presence or absence of the protein target across species.

Tier 2: Functional Domain Evaluation

This level evaluates sequence similarity within selected functional domains (e.g., ligand-binding domain, DNA-binding domain). Conservation within these critical regions provides stronger evidence for maintained protein function across species.

Tier 3: Key Amino Acid Residue Assessment

The most precise analysis compares individual amino acid residue positions of importance for protein conformation and/or interaction with chemicals upon binding. Conservation at these critical residues suggests potential for similar chemical interactions.

Table 1: SeqAPASS Analysis Tiers and Applications

Analysis Tier	Data Input	Output	Primary Application
Tier 1: Primary Sequence	Full-length amino acid sequence	Sequence similarity metric, ortholog identification	Initial screening for protein presence/absence across species
Tier 2: Functional Domains	Specific functional domain sequences	Domain conservation scores	Evaluation of functional conservation
Tier 3: Key Residues	Individual amino acid residue positions	Residue-level conservation	Prediction of chemical binding susceptibility

Experimental Protocol: AR Conservation Analysis

This protocol details the use of SeqAPASS to evaluate conservation of the human androgen receptor across mammalian and non-mammalian species to predict susceptibility to anti-androgenic chemicals.

Materials and Equipment

• Computer with internet access
• SeqAPASS tool (https://seqapass.epa.gov/seqapass/)
• Reference protein sequence for human AR (UniProt ID: P10275)
• Species list of interest

Step-by-Step Procedure

Step 1: Access the SeqAPASS Tool

• Navigate to the SeqAPASS web application (https://seqapass.epa.gov/seqapass/)
• Register for an account if required

Step 2: Input Query Protein Information

• Select "Androgen Receptor" from the predefined list or input the human AR sequence (NP_000035.2) manually
• Verify the sequence and annotation information

Step 3: Configure Tier 1 Analysis Parameters

• Set the taxonomic scope (e.g., Chordata, Mammalia, or custom species list)
• Apply default similarity thresholds or adjust based on research needs
• Execute Tier 1 analysis

Step 4: Interpret Tier 1 Results

• Review the output table and visualization for sequence similarity scores
• Identify species with high AR sequence conservation (>80% similarity)
• Download results for documentation

Step 5: Perform Tier 2 Analysis

• Select functional domains of interest:
  • DNA-binding domain (DBD)
  • Ligand-binding domain (LBD)
• Run domain-specific conservation analysis
• Compare conservation patterns across domains

Step 6: Conduct Tier 3 Analysis

• Input known critical residue positions for human AR ligand binding
• Analyze conservation of these specific residues across species
• Identify species with identical residue patterns at critical positions

Step 7: Data Synthesis and Reporting

• Integrate results from all three tiers
• Classify species by susceptibility level (high, moderate, low)
• Generate visualizations and summary tables

Data Presentation and Results

AR Sequence Conservation Across Species

Table 2: Androgen Receptor Sequence Conservation Across Select Species

Species	Common Name	Taxonomic Class	Full-Length Similarity (%)	LBD Similarity (%)	DBD Similarity (%)	Critical Residue Conservation
Homo sapiens	Human	Mammalia	100	100	100	Complete
Mus musculus	House mouse	Mammalia	92	95	98	Complete
Rattus norvegicus	Brown rat	Mammalia	91	94	97	Complete
Xenopus tropicalis	Western clawed frog	Amphibia	78	82	95	Partial (85%)
Danio rerio	Zebrafish	Actinopterygii	65	68	90	Partial (72%)
Gallus gallus	Chicken	Aves	87	89	96	Complete

Experimental Workflow Visualization

Diagram 1: SeqAPASS AR Analysis Workflow (63 characters)

Androgen Signaling Pathway

Diagram 2: Androgen Receptor Signaling Pathway (52 characters)

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Androgen Receptor Studies

Reagent/Cell Line	Provider Examples	Application in AR Research
Mammalian Two-Hybrid System	Promega, Agilent Technologies	Detection of protein-protein interactions in AR signaling
Luciferase Reporter Plasmids	Promega, Takara Bio	Measurement of AR-mediated transcriptional activation
H295R Steroidogenesis Model	ATCC	Screening for chemicals affecting steroid hormone production
CHO-K1 AR Reporter Cell Line	ATCC, commercial suppliers	Stable cell line for high-throughput AR screening
Recombinant Human AR Protein	Novus Biologicals, Abcam	In vitro binding assays for direct ligand-receptor interaction
Anti-AR Antibodies	Cell Signaling Technology, Santa Cruz Biotechnology	Detection and quantification of AR expression

Discussion

Interpretation of AR Conservation Results

The SeqAPASS analysis reveals high conservation of the androgen receptor among mammalian species, with decreasing but significant conservation in non-mammalian vertebrates. The ligand-binding domain shows greater variability across evolutionary distance compared to the DNA-binding domain, which remains highly conserved [3]. This pattern suggests that while the fundamental gene regulatory function of AR is maintained, specific ligand-receptor interactions may vary between species.

Species with complete conservation of critical residues in the LBD (e.g., mouse, rat, chicken) are likely to show similar susceptibility to AR-directed EDCs as humans. In contrast, species with partial conservation (e.g., zebrafish) may respond differently to specific anti-androgens, which has important implications for ecological risk assessment and test species selection [4].

Advantages of the SeqAPASS Approach

The SeqAPASS tool provides several key advantages for endocrine disruptor screening:

• Efficiency: Enables rapid screening of potential protein targets across thousands of species
• Cost-Effectiveness: Reduces reliance on expensive in vivo testing
• Species Coverage: Extends predictions to species with limited toxicity data
• Mechanistic Insight: Provides residue-level understanding of susceptibility differences

Integration with EDSP Tier 1 Screening

SeqAPASS results can inform and enhance the existing EDSP Tier 1 battery by:

• Prioritizing chemicals for specific in vitro AR assays based on cross-species conservation patterns
• Guiding selection of appropriate non-mammalian test species for ecological risk assessment
• Providing mechanistic context for interpreting assay results
• Supporting read-across approaches for data-poor chemicals [3] [60]

The SeqAPASS tool provides a powerful computational approach for predicting cross-species susceptibility to androgen-disrupting chemicals through analysis of AR sequence and structural conservation. This case study demonstrates a standardized protocol for evaluating AR conservation that can support chemical prioritization, test species selection, and mechanistic understanding of endocrine disruption. Integration of SeqAPASS with established EDSP screening batteries represents a advancing strategy for addressing the challenges of cross-species extrapolation in chemical toxicity assessment. As sequence databases continue to expand and protein structure prediction improves, the application of bioinformatic tools like SeqAPASS will become increasingly valuable for comprehensive endocrine disruptor screening.

Within comparative toxicology and drug development, a significant challenge lies in extrapolating chemical susceptibility data from well-studied model species to the thousands of others for which toxicity information is limited or absent. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency, addresses this challenge through a tiered bioinformatic approach. By systematically evaluating protein conservation across taxonomic groups, SeqAPASS provides a computational framework for predicting relative intrinsic susceptibility based on the principle that a species' sensitivity to a chemical is largely determined by the conservation of the specific protein targets with which that chemical interacts [3] [4]. This application note details the experimental protocols and underlying evidence tiers—spanning primary sequence, functional domain, and critical residue comparisons—that enable researchers to build confidence in cross-species extrapolations.

Tiered Evidence Framework in SeqAPASS

The SeqAPASS tool employs a multi-level evaluation to assess protein conservation. Each successive tier incorporates more detailed molecular knowledge, providing an escalating line of evidence for predicting whether a chemical-protein interaction in a known sensitive species is likely to occur in other species. This structured approach allows researchers to capitalize on any existing information about the chemical-protein interaction, from minimal data to extensive mechanistic understanding [5] [4].

Table 1: SeqAPASS Evidence Tiers and Their Applications

Evidence Tier	Comparison Focus	Data Input Required	Primary Output	Typical Application in Risk Assessment
Tier 1: Primary Sequence	Full-length amino acid sequence similarity and orthology [4]	Protein sequence from a sensitive species (e.g., NCBI Accession) [5]	A list of species with similar sequences and a quantitative metric of similarity [3]	Initial, screening-level prioritization of potentially susceptible species for further evaluation [3]
Tier 2: Functional Domain	Sequence similarity within specific functional domains (e.g., ligand-binding domain) [4]	Domain boundaries or identifiers (e.g., from NCBI Conserved Domain Database) [5]	Prediction of susceptibility based on conservation of the protein region essential for function [4]	Refining susceptibility predictions for species that passed Tier 1, focusing on functional relevance [3]
Tier 3: Critical Residues	Conservation of individual amino acid residues known to be critical for binding or function [4]	Positions and identities of critical amino acids from literature or crystallography [5]	A heat map showing residue conservation and a refined susceptibility prediction [5]	High-resolution assessment for chemicals with a well-defined molecular interaction, supporting quantitative extrapolation [4]

The underlying logic of the SeqAPASS workflow, which guides the user from data input through these three tiers of analysis, is visualized below.

Application Notes and Protocols

Protocol 1: Performing a Tier 1 (Primary Sequence) Analysis

A Tier 1 analysis provides a rapid, screening-level assessment of potential cross-species susceptibility by comparing the entire primary amino acid sequence of a query protein to all publicly available protein sequences.

Step-by-Step Methodology:

• Identify Query Protein and Sensitive Species: Begin by selecting a protein with known chemical interaction from a sensitive species (e.g., human estrogen receptor, honey bee nicotinic acetylcholine receptor) through literature review [3] [61].
• Access SeqAPASS: Navigate to https://seqapass.epa.gov/seqapass using a supported web browser (Chrome is recommended) and log in to your account [5].
• Input Query Data: On the request page, enter the query protein using its National Center for Biotechnology Information (NCBI) protein accession number or by uploading a FASTA-formatted sequence. The tool provides links to resources like the CompTox Chemicals Dashboard to aid in target identification [3] [5].
• Run Level 1 Analysis: Initiate the Tier 1 analysis with default parameters. The tool uses a standalone version of Protein Basic Local Alignment Search Tool (BLASTp) to mine, align, and compare sequences from the NCBI database, which contains over 153 million proteins from more than 95,000 organisms [3] [5].
• Interpret Results: The Tier 1 output provides a list of species with similar protein sequences, a quantitative metric of similarity, and an automated prediction of relative intrinsic susceptibility. Results can be visualized as interactive graphics and are downloadable for reporting [3] [5].

Protocol 2: Advanced Refinement with Tier 2 (Functional Domain) and Tier 3 (Critical Residue) Analyses

For a more refined assessment, subsequent tiers incorporate deeper knowledge of protein function and chemical interaction.

Tier 2 Methodology:

• Define Functional Domains: From the Tier 1 results page, proceed to Tier 2. Specify the functional domains critical for the chemical-protein interaction (e.g., ligand-binding domain, DNA-binding domain). Domain information can often be extracted from the NCBI Conserved Domain Database, which is integrated into the tool [5].
• Execute and Interpret: Run the Tier 2 analysis. SeqAPASS will compare sequence similarity specifically within the selected functional domains. A positive prediction indicates that the domain architecture necessary for the chemical's effect is conserved, strengthening the evidence for susceptibility [4].

Tier 3 Methodology:

• Identify Critical Residues: Initiate a Tier 3 analysis after Tier 2. Input the specific amino acid residue positions and their identities that are known to be critical for chemical binding or protein function. This information is typically derived from site-directed mutagenesis studies, X-ray crystallography, or existing literature [5] [4].
• Run and Visualize: Execute the analysis. SeqAPASS generates a customizable heat map visualization, allowing for rapid interpretation of the conservation of these critical residues across species of interest. This provides the highest resolution, evidence-based prediction of cross-species susceptibility within the tool [5].

Case Study: Predicting Susceptibility of Non-Apis Bees to Insecticides

Background: An adverse outcome pathway (AOP) linking the activation of the nicotinic acetylcholine receptor (nAChR) to colony death was developed for the honey bee (Apis mellifera). The taxonomic domain of applicability for this AOP to non-Apis bees was unknown [61].

Application of SeqAPASS Tiers:

• Tier 1 Query: The nAChR protein sequence from Apis mellifera was used as the initial query.
• Tier 2 Refinement: The analysis was refined by focusing on the specific ligand-binding domains of the nAChR.
• Tier 3 Confirmation: Where available, critical amino acid residues involved in neonicotinoid insecticide binding were evaluated [4].
• Outcome: The SeqAPASS analysis provided lines of evidence for the structural conservation of the nAChR across various bee species. This bioinformatic prediction helped define the taxonomic domain of the AOP, suggesting that other bee species with conserved nAChR protein structures could be susceptible to the same class of insecticides, thereby prioritizing them for further empirical testing [61].

Table 2: Research Reagent Solutions for SeqAPASS Experiments

Research Reagent / Resource	Function and Relevance in SeqAPASS Analysis
NCBI Protein Database	The primary data source for SeqAPASS, providing over 153 million protein sequences from more than 95,000 organisms for comparison [3].
BLASTp Algorithm	The core computational engine used for Tier 1 primary amino acid sequence alignments and similarity calculations [5].
CompTox Chemicals Dashboard	An integrated resource to help identify potential protein targets of a chemical of interest, informing the initial query selection [3] [5].
FASTA Sequence Format	A standard text-based format for inputting amino acid sequences, allowing users to analyze proteins not yet incorporated into the primary NCBI database [5].
AOP-Wiki	A resource containing adverse outcome pathways; SeqAPASS results can provide evidence for the taxonomic applicability of these pathways [5] [61].
ECOTOX Knowledgebase	An EPA database interoperable with SeqAPASS; users can select species from SeqAPASS output to identify existing empirical toxicity data for validation [5].

The tiered evidence framework within the SeqAPASS tool provides a robust, flexible, and scientifically rigorous methodology for cross-species extrapolation. By systematically advancing from primary sequence comparisons to functional domain and critical residue analyses, researchers can build increasing confidence in their predictions of chemical susceptibility for data-poor species. This structured approach is invaluable for strengthening chemical prioritization, informing the selection of appropriate test species, validating the taxonomic domain of applicability for AOPs, and ultimately supporting more efficient and confident decision-making in chemical safety and drug development.

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool, developed by the U.S. Environmental Protection Agency (EPA), represents a pivotal advancement in predictive toxicology and cross-species extrapolation. The tool was created to address the enduring challenge of evaluating chemical safety across the diversity of species potentially impacted by chemical exposures, a task that is both costly and resource-intensive with traditional whole-animal testing [5]. SeqAPASS operates on the fundamental principle that a species' relative intrinsic susceptibility to a particular chemical can be determined by evaluating the conservation of protein targets of that chemical [4]. By leveraging publicly available protein sequence information, SeqAPASS allows for the extrapolation of toxicity data from data-rich model organisms (e.g., humans, rats, mice, zebrafish) to thousands of other plants and animals for which toxicity information is limited or unavailable [3]. The tool's development and its subsequent version releases have been driven by the need for more efficient chemical screening methods, an international push to reduce animal testing, and the increasing demand for timely chemical evaluations [62] [5].

SeqAPASS Version History and Feature Evolution

Since its initial public release in 2016, SeqAPASS has undergone significant enhancements, with each version introducing new capabilities, data sources, and user-focused features. The table below provides a comprehensive overview of the tool's evolution from Version 1.0 to the current Version 8.0.

Table 1: Comprehensive Feature History of SeqAPASS from Version 1.0 to Version 8.0

Version	Release Date	Key Features and Updates	Data Version
1.0	January 27, 2016	Initial public release; Interfaces for Level 1 (primary amino acid sequence comparisons) and Level 2 (sequence alignments); Ortholog candidate identification; Automated prediction of relative intrinsic susceptibility [5].	1
2.0	May 24, 2017	Updated data downloads (protein, taxonomy, conserved domain) from NCBI; New BLAST+ and COBALT executables; Capability to change default settings for Level 1 and Level 2 reports; Level 3 (individual amino acid residue comparisons) allowing user-submitted sequences [5].	2
3.0	March 10, 2018	Integrated and interactive data visualization for Level 1 and Level 2; Updated NCBI data and BLAST+ executables; Redesigned density plot and susceptibility cut-off pages; Automatic Level 3 susceptibility prediction; First US EPA User Guide released [5].	3
4.0	October 24, 2019	New EPA-compliant login; Integrated help buttons and links to external resources (CompTox Chemicals Dashboard, AOP-Wiki); Level 1-3 data summary reports; Interoperability with ECOTOX Knowledgebase; Reference Explorer for literature support [5].	4
5.0	December 1, 2020	Customizable heat map visualization for Level 3 data; Decision Summary Report for synthesizing results across all levels into a downloadable PDF [5].	5
6.0	September 14, 2021	Widget for connecting SeqAPASS predictions to empirical toxicity data in the ECOTOX Knowledgebase; Allows users to select species from Level 1 output and chemicals to find relevant toxicity data [5].	6
8.0	November 13, 2024	Protein structural conservation evaluations (Level 4); Generation of 3D protein models using I-TASSER; Integration of iCn3D tool for structural visualization; Domain-specific protein structure generation; Enhanced metrics for protein structure quality [3] [62] [63].	Not specified

Analysis of Major Developmental Milestones

The evolution of SeqAPASS demonstrates a clear trajectory from a foundational sequencing tool to a sophisticated platform integrating structural biology. Version 1.0 established the core three-level analytical framework (primary sequence, functional domain, critical amino acid residues) that remains central to the tool's operation [5] [4]. The subsequent releases (Versions 2.0-4.0) focused on enhancing user control, expanding data sources, and improving interpretability through visualizations and summary reports [5].

A significant leap occurred with Version 5.0, which introduced the Decision Summary Report, enabling researchers to synthesize complex, multi-level data into a unified format for regulatory decision-making [5]. Version 6.0 further strengthened the tool's application in risk assessment by creating a direct bridge to empirical toxicity data via the ECOTOX Knowledgebase [5].

The most recent update, Version 8.0, marks a transformative advancement by incorporating a fourth level of analysis focused on protein structural conservation. This version leverages I-TASSER for 3D protein model generation and iCn3D for visualization and structural alignment, moving beyond sequence-based analysis to directly assess the conservation of protein function through structure [62] [63]. This capability allows for more confident predictions of cross-species susceptibility and opens the door for more advanced bioinformatics applications like molecular docking [64].

SeqAPASS Application Notes and Experimental Protocols

The Multi-Level Analytical Framework of SeqAPASS

SeqAPASS employs a tiered approach to extrapolate toxicity information across species, with each level providing an additional line of evidence toward protein conservation and, consequently, chemical susceptibility [4]. Version 8.0 formalizes a four-level framework, as visualized in the workflow below.

Figure 1: The tiered analytical workflow of SeqAPASS, from initial query to susceptibility prediction. Each level provides increasing resolution and requires more specific knowledge about the chemical-protein interaction.

Detailed Protocol for Cross-Species Susceptibility Analysis

This protocol guides users through a comprehensive cross-species analysis using SeqAPASS Version 8.0, incorporating all four levels of evaluation.

Initial Account Setup and Protein Identification

• Access and Login: Navigate to https://seqapass.epa.gov/seqapass using the Chrome web browser. Select either "Login" for an existing account or follow the instructions to create a new SeqAPASS account, which is required to run, store, access, and customize jobs [7].
• Identify Query Protein: Prior to analysis, identify a protein of interest and a known sensitive species through literature review. The SeqAPASS interface provides dropdown menus under "Identify a Protein Target" with links to external resources (e.g., CompTox Chemicals Dashboard, AOP-Wiki) to assist in target identification [5] [7].

Level 1 Analysis: Primary Amino Acid Sequence Comparison

• Initiate Query: On the "Request SeqAPASS Run" tab, click either "By Species" (recommended if no specific accession is known) or "By Accession." Enter the protein name or accession number and click "Request Run" to submit the query [7].
• Monitor Job Status: Select the "SeqAPASS Run Status" tab to view the status of the submitted run. Completion time varies with tool usage [7].
• View and Interpret Results: Navigate to the "View SeqAPASS Reports" tab, select the completed run, and click "View Report." On the Query Protein Information page, select the radio button for the "Primary" (condensed) or "Full" (expanded) report. The report lists species with a susceptibility prediction of "Yes" or "No," indicating whether the primary protein sequence is sufficiently conserved relative to the query sequence [7].
• Data Export and Visualization:
  • Download Data: Click "Download Table" to save the report as a spreadsheet file.
  • Visualize: Click the plus sign next to "Visualization," then "Visualize Data," to open an interactive BoxPlot in a new tab. Customize the graph by adding/removing taxonomic groups, highlighting specific species subsets (e.g., threatened/endangered), and then export using "Download BoxPlot" [7].

Level 2 Analysis: Functional Domain Conservation

• Initiate Domain Analysis: From the Level One Query Protein Information page, click the plus sign next to the "Level Two" header. Click "Select Domain" to populate a list of functional domains from the NCBI Conserved Domain Database. Select the relevant domain and click "Request Domain Run" [7].
• Access and View Results: Click "Refresh Level Two and Three Runs." Under "View Level Two Data," select the completed domain accession and click "View Level Two data." Level 2 reports display information similar to Level 1 but with added context on the specific protein domain analyzed [7].

Level 3 Analysis: Critical Amino Acid Residue Comparison

• Literature Review for Critical Residues: Identify specific amino acid residues critical for chemical-protein interaction through literature review. Use the "Reference Explorer" tool (under the Level Three Query Menu) to generate a predefined Boolean string for querying literature databases like Google Scholar [7].
• Setup and Run: In the Level Three Query Menu, select a template sequence for alignment. Enter a user-defined run name, select the desired taxonomic group, and click "Request Residue Run." Repeat for all taxonomic groups of interest [7].
• View and Visualize Results: Click "Refresh Level Two and Three Runs" and select the completed job. On the Level Three Template Protein Information page, enter or select the critical amino acid positions and click "Update Report." The results can be viewed in primary/full reports or as a customizable heat map, which displays the alignment and conservation status of each critical residue across species [7].

Level 4 Analysis: Protein Structural Modeling and Alignment (Advanced)

• Request Level 4 Access: Level 4 is intended for advanced users. Users must request Level 4 access to generate protein structures [63].
• Generate 3D Protein Models: Using the integrated I-TASSER tool, submit sequences to generate 3D protein structural models across species. SeqAPASS Version 8.0 provides metrics to describe the quality of the generated structures [3] [63].
• Perform Structural Alignment and Analysis: Use the integrated iCn3D tool to visualize the generated protein structures and perform structural alignment and superposition analyses. This provides a direct comparison of protein folds and active site geometries across species [63].
• Data Integration: Integrate the Level 4 structural data with the results from Levels 1-3 as an additional, high-resolution line of evidence for evaluating protein conservation and predicting chemical susceptibility [63].

Final Data Synthesis and Reporting

• Generate Decision Summary Report: Throughout the analysis, click "Push Level to DS Report" from any results page. The "DS Report" tab compiles data from all analysis levels into a combined view. This final, downloadable (.pdf) summary report allows for a holistic evaluation of susceptibility predictions across all levels and species simultaneously [7].

Successful cross-species susceptibility analysis requires both the SeqAPASS platform and a suite of external data resources and analytical tools. The following table details key components of the research toolkit.

Table 2: Key Research Reagent Solutions for SeqAPASS Analysis

Tool/Resource	Type	Function in Analysis
NCBI Protein Database	Data Repository	The primary source for over 153 million protein sequences from more than 95,000 organisms, used by SeqAPASS for all sequence comparisons [3].
I-TASSER	Computational Tool	Integrated in SeqAPASS v8.0 for generating 3D protein structural models from amino acid sequences, enabling Level 4 structural evaluations [62] [63].
iCn3D	Visualization Software	Integrated in SeqAPASS v8.0 for visualizing 3D protein structures, performing structural alignments, and analyzing structural conservation across species [63].
ECOTOX Knowledgebase	Data Repository	An EPA database curated with empirical toxicity data. A widget in SeqAPASS v6.0+ allows direct linking of sequence-based predictions to existing toxicity results for validation [5].
CompTox Chemicals Dashboard	Data Repository	An EPA resource used to help identify potential protein targets of chemicals and access related toxicological data [3] [5].
AOP-Wiki	Knowledge Repository	A database of Adverse Outcome Pathways (AOPs). Used to identify key protein targets (Molecular Initiating Events) within a pathway and frame the taxonomic applicability of an AOP [5].
Google Scholar / Reference Explorer	Literature Database	Used within the SeqAPASS Level 3 protocol to identify scientific literature defining the critical amino acid residues involved in chemical-protein interactions [7].
BLASTp / COBALT	Algorithm	Standalone versions of these sequence alignment algorithms are used in the backend of SeqAPASS to perform the primary amino acid sequence comparisons and alignments [5].

The evolution of SeqAPASS from Version 1.0 to 8.0 demonstrates a consistent commitment to advancing the science of cross-species extrapolation. The tool has grown from a sequence comparison utility into a comprehensive platform that integrates primary sequence, functional domain, critical residue, and now 3D structural data. Each version release has directly addressed user needs, enhancing the robustness, flexibility, and interpretability of its predictions [3] [5]. The introduction of protein structural modeling in Version 8.0 represents a significant leap forward, bridging the gap between sequence conservation and functional conservation. As the tool continues to evolve, it solidifies its role as an indispensable resource for researchers, regulators, and drug development professionals aiming to maximize the use of existing data, prioritize testing, and ultimately conduct more efficient and defensible chemical safety assessments for a wide spectrum of species.

The advent of sophisticated protein structure prediction tools has revolutionized biomedical research, enabling the investigation of molecular interactions in species where experimental structures are unavailable. This application note examines the complementary strengths of two leading protein structure prediction tools—AlphaFold (a deep learning-based system) and I-TASSER (an iterative threading assembly refinement method)—within the specific context of cross-species susceptibility research using the SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) platform. We provide detailed protocols for generating and integrating protein structural models to enhance predictions of chemical susceptibility across diverse species, supported by comparative performance data and practical implementation workflows. This integrated approach provides a powerful framework for supporting ecological risk assessments, drug discovery, and chemical safety evaluations.

The Challenge of Cross-Species Extrapolation in Chemical Safety

Chemical safety evaluations and ecological risk assessments face a fundamental challenge: toxicity data for countless species are very limited, while testing resources are constrained globally [3] [5]. Regulatory decisions must often extrapolate from a few model organisms (e.g., humans, mice, rats, zebrafish) to thousands of non-target species with little or no toxicity information [3]. The SeqAPASS tool addresses this challenge by using protein sequence and structural conservation as a line of evidence to predict intrinsic susceptibility across species [4] [15]. The underlying principle is that sensitivity to a chemical depends partly on the presence and conservation of specific protein targets with which chemicals interact [3].

The Role of Protein Structure Prediction in Toxicological Research

While sequence similarity provides an initial line of evidence, protein structural conservation offers deeper insights into chemical-protein interactions that determine susceptibility [15]. Until recently, structural models were unavailable for many species. Advances in computational tools like AlphaFold and I-TASSER have dramatically expanded opportunities to generate reliable protein structures across diverse species, enabling more confident predictions of chemical susceptibility [15]. These tools employ distinct methodologies with complementary strengths that can be harnessed within the SeqAPASS framework to enhance cross-species extrapolation.

Tool Comparison and Performance Analysis

AlphaFold employs an end-to-end deep learning approach that integrates multiple sequence alignments, evolutionary coupling, and structural physical constraints to predict protein structures [65] [66]. Recent versions have demonstrated remarkable accuracy in reproducing experimentally determined structures.

I-TASSER (Iterative Threading ASSembly Refinement) utilizes iterative threading to identify structural templates from the Protein Data Bank, followed by fragment assembly and atomic-level refinement through molecular dynamics simulations [67] [66]. The recently developed D-I-TASSER hybrid pipeline combines iterative threading assembly simulations with multi-source deep learning potentials, demonstrating particular strength with multidomain proteins [66].

Quantitative Performance Metrics

Table 1: Comparative Performance of Structure Prediction Tools

Metric	AlphaFold	I-TASSER	D-I-TASSER	Notes
Z-score (Apelin)	-4.21 [65]	-2.06 [65]	N/A	Lower Z-score indicates higher quality
Z-score (FX06)	-4.72 [65]	-4.46 [65]	N/A	Consistent advantage for AlphaFold
TM-score (multidomain proteins)	Baseline	N/A	12.9% higher than AlphaFold2 [66]	Demonstrated advantage for complex proteins
CASP15 performance (FM domains)	Baseline	N/A	18.6% higher TM-score [66]	Community-wide blind assessment
CASP15 performance (multidomain)	Baseline	N/A	29.2% higher TM-score [66]	Significant advantage for multidomain targets
IDP performance	Limited [68]	Limited [68]	N/A	Both struggle with intrinsically disordered proteins
Human proteome coverage	~76% of human proteome [66]	N/A	81% of domains, 73% of full-chain [66]	Complementary coverage

Table 2: Tool Selection Guidelines for Cross-Species Applications

Research Scenario	Recommended Tool	Rationale
Single-domain proteins	AlphaFold	Superior Z-scores and model quality [65]
Multidomain proteins	D-I-TASSER	Enhanced domain-splitting and assembly protocol [66]
Template-rich targets	I-TASSER	Robust threading and assembly approach [67]
Template-poor targets	AlphaFold	Deep learning excels without homologs [65]
Rapid screening	AlphaFold	Streamlined pipeline with high accuracy [65]
Functional annotation	I-TASSER	Integrated function inference [67]
IDP characterization	Specialized MD required [68]	Both tools show limitations for disordered proteins [68]

Case Study: HCV Core Protein Modeling

A comparative study on hepatitis C virus core protein (HCVcp) modeling revealed that Robetta and trRosetta outperformed AlphaFold2 for initial prediction, while among template-based tools, MOE outperformed I-TASSER [67]. However, molecular dynamics simulations proved essential for refining all predicted structures to achieve reliably folded models [67]. This highlights that prediction tool performance can be target-dependent, and refinement is often necessary for biologically relevant structures.

Integrated Protocol for Cross-Species Susceptibility Assessment

This protocol outlines the integration of AlphaFold and I-TASSER structural models within the SeqAPASS workflow for cross-species susceptibility predictions.

Stage 1: Protein Target Identification and Sequence Acquisition

• Identify Query Protein: Using the SeqAPASS interface, select a protein target with known importance for chemical susceptibility (e.g., honey bee nicotinic acetylcholine receptor for neonicotinoid insecticides) [3] [5].

• Acquire Reference Sequence: Obtain the reference protein sequence for a sensitive species through:

  • NCBI Protein database (accession number)
  • CompTox Chemicals Dashboard (for ToxCast assay targets)
  • Manual FASTA entry of known susceptible species [5]

• Define Taxonomic Scope: Determine the range of species for extrapolation based on assessment goals (e.g., aquatic species, pollinators, endangered species) [3].

Stage 2: Primary Sequence-Based Filtering with SeqAPASS

• Level 1 Analysis - Primary Amino Acid Sequence Comparison:

  • Submit reference sequence to SeqAPASS Level 1 analysis
  • Set appropriate E-value cutoff (default = 0.001)
  • Review sequence similarity metrics across taxa
  • Download preliminary susceptibility predictions [5]

• Level 2 Analysis - Functional Domain Conservation:

  • Identify known functional domains (e.g., ligand-binding domains)
  • Evaluate domain-specific conservation patterns
  • Refine susceptibility predictions based on domain presence/absence [4]

Stage 3: Structural Model Generation and Comparison

• Parallel Structure Prediction:

  • AlphaFold Protocol:

    • Input target sequence into AlphaFold2 or AlphaFold3
    • Generate multiple models (if option available)
    • Download PDB file of highest-ranked model
    • Record confidence metrics (pLDDT per residue)

  • I-TASSER Protocol:

    • Submit sequence to I-TASSER server
    • Select comprehensive analysis mode
    • Retrieve top 5 models by C-score
    • Download associated template and function predictions [15]

• Structural Quality Assessment:

  • Calculate Z-scores for both models using validation servers
  • Generate Ramachandran plots to identify steric clashes
  • Compare global fold similarity using TM-score
  • Select optimal model for further analysis [65]

Stage 4: Critical Residue Evaluation and Integration

• Level 3 Analysis - Critical Amino Acid Residue Comparison:

  • Identify key residues for chemical binding from literature or mutagenesis studies
  • Map conserved residues to structural models
  • Use SeqAPASS Level 3 to evaluate residue conservation across species [5]

• Structural Alignment and Binding Site Analysis:

  • Superpose AlphaFold and I-TASSER models
  • Compare binding site architectures
  • Note significant variations that may affect chemical interactions [15]

Stage 5: Molecular Dynamics Refinement

• System Preparation:

  • Solvate protein structures in appropriate water model
  • Add ions to physiological concentration
  • Neutralize system charge [67]

• Simulation Protocol:

  • Energy minimization (5,000-10,000 steps)
  • Solvent equilibration with protein restraints
  • Production MD simulation (100-200 ns)
  • Replica exchange MD for intrinsically disordered regions [68]

• Trajectory Analysis:

  • Calculate root mean square deviation (RMSD) to monitor stability
  • Determine root mean square fluctuation (RMSF) of Cα atoms
  • Compute radius of gyration to assess compactness
  • Use MM/PBSA for binding free energy calculations [67] [65]

Stage 6: Integrated Susceptibility Prediction

• Synthesize Evidence:

  • Combine SeqAPASS sequence conservation data
  • Integrate structural conservation from both models
  • Account for binding site accessibility and chemical compatibility

• Generate Final Report:

  • Use SeqAPASS Decision Summary Report feature
  • Create customized box-plot graphics for cross-taxa susceptibility
  • Export publication-quality visualizations [5]

Research Reagent Solutions

Table 3: Essential Computational Tools for Integrated Structural Analysis

Tool/Resource	Type	Primary Function	Access Information
SeqAPASS	Web Application	Cross-species protein conservation analysis	https://seqapass.epa.gov/seqapass/ [3]
AlphaFold	Web Server/Software	Deep learning-based structure prediction	https://alphafoldserver.com/ or ColabFold [65]
I-TASSER	Web Server/Software	Template-based structure modeling and function prediction	https://zhanggroup.org/I-TASSER/ [15]
D-I-TASSER	Web Server/Software	Hybrid deep learning and physics-based modeling	https://zhanggroup.org/D-I-TASSER/ [66]
GROMACS	Software Suite	Molecular dynamics simulations	https://www.gromacs.org/ [67]
NCBIBLAST+	Command Line Tool	Local sequence alignment searches	https://blast.ncbi.nlm.nih.gov/ [5]
ClusPro	Web Server	Protein-peptide docking	https://cluspro.org/ [65]
HPEPDOCK	Web Server	Peptide-protein docking	http://huanglab.phys.hust.edu.cn/hpepdock/ [65]

The integration of AlphaFold and I-TASSER structural models within the SeqAPASS framework represents a significant advancement in cross-species susceptibility prediction. While AlphaFold generally provides superior accuracy for single-domain proteins, I-TASSER (particularly D-I-TASSER) shows advantages for complex multidomain proteins [65] [66]. The complementary nature of these tools enables researchers to generate more reliable structural models across diverse species, enhancing confidence in predictions of chemical susceptibility. Molecular dynamics simulations remain an essential refinement step, particularly for resolving structural ambiguities and modeling flexible regions [67] [68]. This integrated approach provides a robust, computationally-driven framework for ecological risk assessment, drug discovery, and chemical safety evaluation, ultimately supporting more informed regulatory decisions while reducing animal testing requirements.

Conclusion

SeqAPASS represents a pivotal advancement in computational toxicology, offering a scientifically robust and efficient framework for predicting chemical susceptibility across the vast diversity of species. By leveraging publicly available protein data through a tiered evaluation of sequence, functional domain, and structural conservation, it provides a critical line of evidence for cross-species extrapolation. This tool directly supports the global shift toward New Approach Methodologies by reducing reliance on animal testing, enabling rapid prioritization of chemicals, and informing the selection of ecologically relevant species for further assessment. The future of SeqAPASS and the field lies in the deeper integration of high-fidelity protein structural models, advanced molecular docking simulations, and richer toxicokinetic data. For biomedical and clinical research, these developments promise to enhance the prediction of off-target drug effects, improve the understanding of species-specific toxicities in pre-clinical models, and ultimately contribute to the development of safer pharmaceuticals and chemicals.

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