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

  1. Austin Clyde
  2. Stephanie Galanie
  3. Daniel W. Kneller
  4. Heng Ma
  5. Yadu Babuji
  6. Ben Blaiszik
  7. Alexander Brace
  8. Thomas Brettin
  9. Kyle Chard
  10. Ryan Chard
  11. Leighton Coates
  12. Ian Foster
  13. Darin Hauner
  14. Vilmos Kertesz
  15. Neeraj Kumar
  16. Hyungro Lee
  17. Zhuozhao Li
  18. Andre Merzky
  19. Jurgen G. Schmidt
  20. Li Tan
  21. Mikhail Titov
  22. Anda Trifan
  23. Matteo Turilli
  24. Hubertus Van Dam
  25. Srinivas C. Chennubhotla
  26. Shantenu Jha
  27. Andrey Kovalevsky
  28. Arvind Ramanathan
  29. Martha S. Head
  30. Rick Stevens

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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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  1. Version published to 10.1101/2021.03.27.437323v2 on bioRxiv
  2. ScreenIT

    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
    SentencesResources
    ORZ consists of a downloaded set of compounds that are in-stock and available from ZINC (
    ZINC
    suggested: (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.
    OpenEye
    suggested: (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.
    Python
    suggested: (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 Prism
    suggested: (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).
    COOT
    suggested: (Coot, RRID:SCR_014222)
    Molprobity
    suggested: (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
    OpenMM
    suggested: (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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  3. Version published to 10.1101/2021.03.27.437323v1 on bioRxiv