气候变化领域集成服务门户

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target while remaining amenable to variable degrees of protein characterization, in the context of available information about the chemical/protein interaction and the molecular target itself. To accommodate this flexibility in the analysis, 3 levels of evaluation were developed. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of orthologs); the second level evaluates sequence similarity within selected functional domains (eg, ligand-binding domain); and the third level of analysis compares individual amino acid residue positions of importance for protein conformation and/or interaction with the chemical upon binding. Each level of the SeqAPASS analysis provides additional evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further evaluation, selection of appropriate species for testing, extrapolation of empirical toxicity data, and/or assessment of the cross-species relevance of adverse outcome pathways. Three case studies are described herein to demonstrate application of the SeqAPASS tool: the first 2 focused on predictions of pollinator susceptibility to molt-accelerating compounds and neonicotinoid insecticides, and the third on evaluation of cross-species susceptibility to strobilurin fungicides. These analyses illustrate challenges in species extrapolation and demonstrate the broad utility of SeqAPASS for risk-based decision making and research.