Physical Sciences Division
Published November 18, 2021 | Version v1
Journal article Open

High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Creators

  • 1. Oak Ridge National Laboratory
  • 2. Argonne National Laboratory
  • 3. University of Chicago
  • 4. Pacific Northwest National Laboratory
  • 5. Rutgers University
  • 6. Los Alamos National Laboratory

Description

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 noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) 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 Mpro inhibitor with an inhibition constant (Ki) of 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Notes

Due to the large number of authors, only the first 20 and the University of Chicago authors are included on the above author list. Please download the article for the complete list of authors.

Data availability

The computational data produced in this study are freely available for academic use. The 2D version of the data set, containing the docking scores and 2D molecular structures, is hosted publicly by a third party provider, FigShare: https://doi.org/10.6084/m9.figshare.14745234. The rest of the data, including all the 3D resulting structures from docking, is hosted by Argonne's Leadership Computing Center and accessible via a Globus endpoint with documentation hosted by GitHub: https://doi.org/10.26311/BFKY-EX6P. The authors are confident that the data will be persistent across FigShare, GitHub, ALCF, and Globus. Experimental data discussed in this paper are shared in the Supporting Information. The room-temperature crystal structure determined is deposited in the Protein Data Bank (PDB ID: 7LTJ).

The OpenEye Scientific software used for docking (FRED) is available via an academic license for users. The workflow outlined in this paper, along with specific hyperparameters for the docking protocol, is available here: https://github.com/aclyde11/Model-generation. The MD simulations in this study were carried out using the OpenMM toolkit (https://openmm.org/, and the autoencoder was implemented using PyTorch (a href="http://pytorch.org/">http://pytorch.org/); the hyperparameter settings as well as specifics of how the models were trained are described in the Methods section. Python notebooks for running pyANCA are available here: https://csb.pitt.edu/anca/index.html.

Files

clyde-et-al-2021-high-throughput-virtual-screening-and-validation-of-a-sars-cov-2-main-protease-noncovalent-inhibitor.pdf

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Additional details

Identifiers

DOI
10.1021/acs.jcim.1c00851
Other
oai:uchicago.tind.io:13348

Funding

U.S. Department of Energy
17-SC-20-SC
U.S. Department of Energy
Computational Sciences Graduate Fellowship
U.S. Department of Energy
DE-AC02-06CH11357
U.S. Department of Energy
DE-AC05-00OR22725

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Computer Science