High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor
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Abstract
Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (M pro ) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this M pro inhibitor with an inhibition constant ( K i ) of 2.9 µ M [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of M pro forming stable hydrogen bond and hydrophobic interactions. We then used multiple µ s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by M pro , involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits M pro and offers a springboard for further therapeutic design.
The ongoing novel coronavirus pandemic (COVID-19) has prompted a global race towards finding effective therapeutics that can target the various viral proteins. Despite many virtual screening campaigns in development, the discovery of validated inhibitors for SARS-CoV-2 protein targets has been limited. We discover a novel inhibitor against the SARS-CoV-2 main protease. Our integrated platform applies downstream biochemical assays, X-ray crystallography, and atomistic simulations to obtain a comprehensive characterization of its inhibitory mechanism. Inhibiting M pro can lead to significant biomedical advances in targeting SARS-CoV-2 treatment, as it plays a crucial role in viral replication.
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SciScore for 10.1101/2021.03.27.437323: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources ORZ consists of a downloaded set of compounds that are in-stock and available from ZINC ( ZINCsuggested: (Zinc, RRID:SCR_008596)The receptors for OpenEye Chemgauss4 scoring were created by hand based on the known binding region of Mpro (16). C. Computational Workflow: Chemgauss4 docking was performed on Frontera at TACC. OpenEyesuggested: (OpenEye, RRID:SCR_014880)RP is a pilot-enabled runtime system while RAPTOR is a scalable master/worker overlay developed to improve the execution performance of many, short-running tasks encoded as Python functions. Pythonsuggested: (IPython, RRID:SCR_00165…SciScore for 10.1101/2021.03.27.437323: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources ORZ consists of a downloaded set of compounds that are in-stock and available from ZINC ( ZINCsuggested: (Zinc, RRID:SCR_008596)The receptors for OpenEye Chemgauss4 scoring were created by hand based on the known binding region of Mpro (16). C. Computational Workflow: Chemgauss4 docking was performed on Frontera at TACC. OpenEyesuggested: (OpenEye, RRID:SCR_014880)RP is a pilot-enabled runtime system while RAPTOR is a scalable master/worker overlay developed to improve the execution performance of many, short-running tasks encoded as Python functions. Pythonsuggested: (IPython, RRID:SCR_001658)A global nonlinear regression was performed to fit the competitive inhibition equation to the entire data set using GraphPad Prism 9.0, yielding KM, Ki, Vmax, and their associated 95% confidence intervals. I. Crystallization: Crystallization reagents were purchased from Hampton Research (Aliso Viejo, California, USA) GraphPad Prismsuggested: (GraphPad Prism, RRID:SCR_002798)Structure refinement was performed with Phenix.refine from Phenix suite (40) and COOT (41) for manual refinement and Molprobity (42). COOTsuggested: (Coot, RRID:SCR_014222)Molprobitysuggested: (MolProbity, RRID:SCR_014226)After equilibrating the systems by using a similar protocol to that outlined in Ramanathan et al.(43), we carried out production runs using the OpenMM simulation package on Nvidia V100 GPUs using the Argonne Leadership Computing Facility’s OpenMMsuggested: (OpenMM, RRID:SCR_000436)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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