Minimizing the human and economic costs of the COVID-19 pandemic and of future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. Yet drug discovery is a notoriously slow process. An accurate and streamlined computational method for predicting potential drug target sites could reduce the time and cost of experimental screenings and serve a critical purpose in accelerating drug development.<br/>To this end, we introduce a unique, computational pipeline for the rapid identification and characterization of binding sites in the proteins of novel viruses as well as the core chemical components with which these sites interact. We combine molecular-level structural modeling of proteins with data science clustering and cheminformatic techniques in a computationally efficient manner. We leverage the PDBspheres method, based on experimental crystal structures from PDB, to implement structural modeling of SARS-CoV-2 proteins and produce protein-ligand binding data. Using this data, we first group protein residues based on their ligand contacts and select groups that form relevant binding sites. We then group the ligands that bind to each site based on their chemical structure and function, revealing the site-specific, key chemical features involved in binding.<br/>Similarities between our results, experimental data, and other computational studies provide support for the effectiveness of our predictive framework. We find that the composition of structural models for an individual binding site may be used as a simple heuristic for determining which binding sites will make more effective drug targets. While we present here a demonstration of our tool on SARS-CoV-2, our process is generalizable and can be applied to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate homology models can be constructed.

Computational pipeline to identify potential drug targets and interacting chemotypes in SARS-CoV-2

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ACS Fall 2022

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