Structural dynamics of the β-coronavirus M pro protease ligand binding sites

  1. Eunice Cho
  2. Margarida Rosa
  3. Ruhi Anjum
  4. Saman Mehmood
  5. Mariya Soban
  6. Moniza Mujtaba
  7. Khair Bux
  8. Sarath Dantu
  9. Alessandro Pandini
  10. Junqi Yin
  11. Heng Ma
  12. Arvind Ramanathan
  13. Barira Islam
  14. Antonia S J S Mey
  15. Debsindhu Bhowmik
  16. Shozeb Haider

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Abstract

β-coronaviruses alone have been responsible for three major global outbreaks in the 21 st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a back-up against the emergence of lethal viral variants. One such target is the main protease (M pro ) that plays an indispensible role in viral replication. The availability of over 270 M pro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all non-redundant ligand-binding sites available for SARS-CoV2, SARS-CoV and MERS-CoV M pro . Extensive adaptive sampling has been used to explore conformational dynamics employing convolutional variational auto encoder-based deep learning, and investigates structural conservation of the ligand binding sites using Markov state models across β-coronavirus homologs. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-coronavirus homologs. This highlights the complexity in targeting all three M pro enzymes with a single pan inhibitor.

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  1. Version published to 10.1101/2021.03.31.437918v2 on bioRxiv
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    SciScore for 10.1101/2021.03.31.437918: (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
    The MSM was built using the PyEMMA v2.5.7 program.
    PyEMMA
    suggested: None
    The conformational clusters were grouped together based on kinetic similarity using the PCCA+ algorithm.70 The PCCA+ algorithm uses the eigenvectors of the MSMs to group together clusters, which are kinetically close, resulting in a set of macrostates.
    MSMs
    suggested: (MSMS, RRID:SCR_003532)

    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 10.1101/2021.03.31.437918v1 on bioRxiv