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High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor.
Clyde, Austin; Galanie, Stephanie; Kneller, Daniel W; Ma, Heng; Babuji, Yadu; Blaiszik, Ben; Brace, Alexander; Brettin, Thomas; Chard, Kyle; Chard, Ryan; Coates, Leighton; Foster, Ian; Hauner, Darin; Kertesz, Vilmos; Kumar, Neeraj; Lee, Hyungro; Li, Zhuozhao; Merzky, Andre; Schmidt, Jurgen G; Tan, Li; Titov, Mikhail; Trifan, Anda; Turilli, Matteo; Van Dam, Hubertus; Chennubhotla, Srinivas C; Jha, Shantenu; Kovalevsky, Andrey; Ramanathan, Arvind; Head, Martha S; Stevens, Rick.
  • Clyde A; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Galanie S; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Kneller DW; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Ma H; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
  • Babuji Y; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Blaiszik B; Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
  • Brace A; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Brettin T; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Chard K; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Chard R; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Coates L; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Foster I; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Hauner D; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Kertesz V; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Kumar N; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Lee H; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Li Z; Computing Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Merzky A; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Schmidt JG; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Tan L; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Titov M; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Trifan A; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Turilli M; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Van Dam H; Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
  • Chennubhotla SC; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Jha S; Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
  • Kovalevsky A; Department of Computer Science, University of Chicago, Chicago, Illinois 60615, United States.
  • Ramanathan A; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
  • Head MS; Computational Biology Group, Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
  • Stevens R; National Virtual Biotechnology Laboratory, Washington, District of Columbia 20585, United States.
J Chem Inf Model ; 62(1): 116-128, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1521685
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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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 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Protease Inhibitors / COVID-19 Type of study: Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2022 Document Type: Article Affiliation country: Acs.jcim.1c00851

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Protease Inhibitors / COVID-19 Type of study: Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2022 Document Type: Article Affiliation country: Acs.jcim.1c00851