Discovery of TMPRSS2 inhibitors from virtual screening

  1. Xin Hu
  2. Jonathan H. Shrimp
  3. Hui Guo
  4. Miao Xu
  5. Catherine Z. Chen
  6. Wei Zhu
  7. Alexey Zakharov
  8. Sankalp Jain
  9. Paul Shinn
  10. Anton Simeonov
  11. Matthew D. Hall
  12. Min Shen

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Abstract

The SARS-CoV-2 pandemic has prompted researchers to pivot their efforts to finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion. TMPRSS2 is an attractive target and has received attention for the development of drugs against SARS and MERS. Starting with comparative structural modeling and binding model analysis, we developed an efficient pharmacophore-based approach and applied a large-scale in silico database screening for small molecule inhibitors against TMPRSS2. The hits were evaluated in the TMPRSS2 biochemical assay and the SARS-CoV-2 pseudotyped particle (PP) entry assay. A number of novel inhibitors were identified, providing starting points for further development of drug candidates for the treatment of COVID-19.

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  1. Version published to 10.1101/2020.12.28.424413v2 on bioRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.12.28.424413: (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
    14 The homology model of TMPRSS2 was built using the threading program I-TASSER.
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)
    The default parameters in AutoDock Vina were used with a grid box defined at the center of bound ligand by 20 × 20 × 20 Å to encompass the entire active site of the protein.
    AutoDock
    suggested: (AutoDock, RRID:SCR_012746)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.12.28.424413v1 on bioRxiv