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In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool.
Citation:
Doering, J., S. Lee, K. Kristiansen, L. Evenseth, M. Barron, I. Sylte, AND C. LaLone. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 166(1):131-145, (2018). https://doi.org/10.1093/toxsci/kfy186
Impact/Purpose:
This is a paper that used case studies for two classes of commercial pesticides to demonstrate that substitutions in identities of key amino acids in chemical target proteins cause no change in protein interactions with chemicals if the amino acids share the same functional properties and have comparable size. These findings were used to improve a computational tool that allows automated predictions of chemical susceptibility among species based on identities of key amino acids in their protein targets. This work supports aims of CSS AOPDD 17.01.01 Task 1.1c towards the development of tools that address challenges of species extrapolation of chemical susceptibilities to guide more objective human and ecological screening-level hazard assessments.
Description:
The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functional domain sequence (Level 2), or individual amino acid residues at key positions (Level 3) of the protein target of a chemical in a known sensitive species to sequences of other species and calculates sequence similarity metrics to predict potential cross-species chemical susceptibility. Level 3 analyses offer the greatest resolution for extrapolation of chemical susceptibility across specific species. However, a lack of understanding of whether specific amino acid substitutions at key positions in proteins affect interaction with chemicals made manual interpretation of Level 3 analyses time consuming and potentially inconsistent. Therefore, this study used in silico site-directed mutagenesis coupled with docking simulations of computational models for acetylcholinesterase (AChE) and ecdysone receptor (EcR) to investigate how specific amino acid substitutions impact protein-chemical interaction. This study found that computationally derived substitutions in identities of key amino acids caused no change in chemical interaction with a protein if residues share the same side chain functional properties and have comparable molecular dimensions, while differences in these characteristics can reduce protein-chemical interaction. These findings were considered in the development of automated Level 3 susceptibility predictions, which were incorporated in SeqAPASS v.3.0, and enable automatically generated species-specific predictions of chemical susceptibility. These predictions were shown to agree with Level 1 and 2 predictions of AChE and EcR for more than 90 % of investigated species, but also identified dramatic species-specific differences in chemical susceptibility that align with results from standard toxicity tests. The consistency of automated predictions of susceptibility across Levels 1, 2 and 3 of SeqAPASS v.3.0 and agreement with results of standard toxicity tests provides a compelling line-of-evidence for use of this tool in deriving screening level species-specific chemical susceptibility predictions across broad taxonomic groups applicable to addressing challenges in species extrapolation for human and ecological screening-level hazard assessment.
