Quantum Chemical Refinement and Property Analysis of SARS-CoV-2 Inhibitors. The goal of the proposed work is to employ high-throughput quantum mechanics (QM)-based calculations for the refinement and property analysis of SARS-CoV-2 inhibitors. The proposed calculations will be performed on the basis of the classical docking results provided by our collaborator Jeremy Smith in the Biosciences Division of ORNL. In these ongoing studies, emphasis is placed on known drug compounds that can be rapidly subjected to an experiment-theory feedback loop. The biochemical community and pharmaceutical industry typically resort to force field-based or empirical docking approaches for such computational screening studies. These methods have achieved remarkable success despite the fact that classical approaches are known to lack accuracy due to the neglect of electronic structure effects such as charge polarization and delocalized pi-conjugated systems. Force field parameters further lack transferability especially in the description of ligand-protein and peptide-protein interactions. Until recently, the inclusion of quantum mechanical electronic structure in high-throughput drug screening was deemed computationally intractable, due to the enormous computational resources required even for density functional theory (DFT) calculations. The poor scaling of most quantum chemical methods further exacerbates the situation. We will use our linear-scaling “fragment molecular orbital density functional tight-binding” (FMO-DFTB) quantum method as implemented in the GAMESS quantum chemistry code for the high-throughput refinement and re-evaluation of potential COVID-19 drug inhibitors previously identified by classical empirical or force field-based docking schemes from the SWEETLEAD database. The proposed work will be used to evaluate and refine the classical docking schemes, identify key interactions between the residues of the virus’ spike (S) proteins and inhibitor molecules, and allow the inclusion of solvation effects. The deliverables will be a shortlist of promising known drug compounds that can be rapidly deployed as COVID-19 inhibitors, a database of ligand-residue pair interactions for future de novo drug design, and the database of DFTB-optimized SWEETLEAD structures and isomer energies.
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