From Protein Sequence to Structure: The Next Frontier in Cross‐Species Extrapolation for Chemical Safety Evaluations

Abstract

Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSERgenerated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data.

Projection of Interspecific Competition (PIC) Matrices: A Conceptual Framework for Inclusion in Population Risk Assessments

“…Many molecular targets of chemicals (i.e., MIEs) are proteins for which amino acid sequence information exists for thousands of species in publicly accessible databases, such as the National Center for Bioinformatics Information (NCBI, 2024). An open-access tool developed by US Environmental Protection Agency scientists called SeqAPASS can be used to systematically mine the NCBI protein database and evaluate the relative sequence and structural conservation of different proteins across species (LaLone et al, 2013(LaLone et al, , 2016(LaLone et al, , 2023. Because protein structure determines function, the degree of conservation of a given MIE across species can be predictive of their relative susceptibility to chemicals that interact with the protein.…”

Emerging contaminants: A One Health perspective

“…With thousands of new synthetic chemicals entering the environment, many not thoroughly tested, there is a need to intensify research on ECs and create a comprehensive public database detailing their sources and environmental behavior. We must advance adverse outcome pathways 454 and cross-species extrapolation approaches 593 , 594 to understand chemical attributes that target particularly susceptible species and advance the precision of environmental assessments for ECs. 595 Doing so promises to inform chemical substitutions in commerce without regrets 596 and the sustainable molecular design of less hazardous substances.…”

Towards Precision Ecotoxicology: Leveraging Evolutionary Conservation of Pharmaceutical and Personal Care Product Targets to Understand Adverse Outcomes Across Species and Life Stages

“…An example of this includes capitalizing on the biomedical and drug development/ discovery fields, where the evaluation of chemical-biomolecule interactions is paramount to understand which particular structures, out of hundreds, can bind to a given protein target to identify candidates to move forward in drug development. In principle, these same automated virtual pipelines could be applied in support of chemical safety assessments considering interactions across species (LaLone et al, 2023;Margiotta-Casaluci et al, 2023). In fact, advances in precision medicine and precision environmental health science provide robust foundations from which precision ecotoxicology can develop (Brooks, 2022).…”

“…This knowledge can inform the identification of tDOA for specific MIEs (e.g., ecdysone receptor involved in molting in invertebrate species) or early key events in an AOP. Beyond these approaches, there are more sophisticated computational molecular models that have been applied and advanced through drug discovery, specifically moving toward protein structure‐based evaluations of chemical protein interactions across species (Cheng et al, 2021; LaLone et al, 2023; McRobb et al, 2014). The field of bioinformatics continues to evolve and is likely to play a prominent role in understanding and predicting the effects of PPCPs, other anthropogenic chemicals, and natural toxins on the diversity of species, particularly as global efforts are moving toward a nonanimal testing regulatory agenda for chemical safety evaluations (LaLone et al, 2021; Langan et al, 2024).…”

“…Fortunately, there have been recent and significant advances in structural biology, with deep learning methods yielding protein structure predictions from various tools such as AlphaFold (Jumper et al, 2021), RoseTTAFold (Baek et al, 2021), and RGN2 (Chowdhury et al, 2022). These structures can be used to support molecular docking experiments for a variety of protein targets expressed across diverse taxa, therefore offering a quantitative approach to accurately characterize MIEs beyond molecular target sequence conservation (LaLone et al, 2023). Results from molecular docking experiments can be further validated by generating empirical data, such as those from receptor binding assays, as well as other chemical proteomics approaches to identify molecular targets (Hall & Peng, 2020).…”