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Protein structural similarity for extrapolation of toxicity knowledge across species
Citation:
LaLone, C., D. Blatz, AND S. Vliet. Protein structural similarity for extrapolation of toxicity knowledge across species. SETAC Europe virtual meeting, Duluth, MN, May 03 - 07, 2020. https://doi.org/10.23645/epacomptox.12164031
Impact/Purpose:
Technology continues to advance that allows scientists to more rapids understand complicated biology. There are now computer programs that allow drug companies to look at the structure of a chemical and a chemical target found in the body of humans and rapidly determine whether that chemical has the potential to be useful as a drug. That chemical then becomes a candidate for drug development and moves forward with more extensive laboratory testing, which is quite costly. Drug companies use these state-of-the-art computer programs as an initial screening to save them time and money and reduce the chance that they start developing a drug that will not work as intended. These computer tools hold great promise in advancing our understanding of whether or not a chemical could interact with a target in other non-human species as well. This understanding could be useful for identifying chemicals that could cause harm to animals or plants in the environment. The US EPA has developed a tool called Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) that helps to understand which organisms might be most susceptible to a given chemical based on how similar the chemical targets in the body are. For hundreds of species that cannot be tested the SeqAPASS tool provides a yes or no answer to the question of whether or not a particular chemical target exists in another species. However, there is interest in understanding how susceptible one species is compared to another species and being able to provide a quantitative measure of susceptibility across species. Therefore, we have begun to explore and develop a means to make the SeqAPASS predictions more numerical using the computer programs commonly applied to chemical screening in the drug development industry. This presentation will provide the description of how we are applying these methods to many species to screen multiple chemicals.
Description:
Bioinformatic approaches for understanding protein structural similarity are advancing rapidly. In fact, computational “virtual” screening for bioactive molecules using advanced molecular modeling approaches is common practice in the pharmaceutical industry, mitigating required time, costs, and risks for late-stage drug-candidate failure. Typically, these methods are used to understand which structures, from hundreds, can optimally bind to a given protein target to select candidates to move forward in drug development. In principle, these same virtual screening pipelines could be applied in support of chemical safety assessments and used to evaluate a chemical’s interaction with hundreds to thousands of proteins representing both the diversity of toxicologically-relevant protein targets in the body as well as the variations in those proteins among species (e.g., vertebrates, invertebrates, plants, fungi, etc.). Here we describe a pipeline that can be used to 1) fill knowledge gaps in instances where chemical molecular targets are unknown by “virtually” identifying if and how chemical(s) bind to proteins across species and 2) provide quantitative information for chemical-protein interactions in species where limited or no chemical toxicity data exist. Harnessing the power of computing is the future for cross-species chemical safety screening which is why the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was created and continues to evolve. SeqAPASS predicts chemical susceptibility across species based on protein-sequence similarity. The output from this tool provides an initial line of evidence for cross species extrapolation, however currently does not provide a numerical value for how well the chemical is expected to bind to the protein target in species predicted susceptible. Computational advances in drug-discovery are available to take this evaluation further to specifically examine protein structural interactions with chemicals using available x-ray crystallography and advanced bioinformatic methods to predict binding affinity. Such virtual methods are intended significantly reduce the cost associated with current high-throughput in vitro screening methods and address the limitations in using model organisms which are primarily used as surrogates due to convenience or convention, rather than appropriateness.
URLs/Downloads:
DOI: Protein structural similarity for extrapolation of toxicity knowledge across species