High-throughput fluorescent assay for inhibitor screening of proteases from RNA viruses

  1. Bara Cihlova
  2. Andrea Huskova
  3. Jiri Böserle
  4. Radim Nencka
  5. Evzen Boura
  6. Jan Silhan

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Abstract

Spanish flu and other influenza outbreaks, the recent Zika epidemics, and the ongoing COVID-19 pandemic are the most profound examples of severe widespread diseases that are caused by RNA viruses. Perhaps less well known yet dangerous RNA viruses cause deadly diseases such as polio, Ebola, measles, rubella, yellow fever, dengue fever and many others. To combat a particular viral disease by diminishing its spread and number of fatal cases, effective vaccines and antivirals are indispensable. Therefore, quick access to the means of discovery of new treatments for any epidemic outbreak is of great interest and in vitro biochemical assays are the basis of drug discovery. The recent outbreak of the coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demands an affordable and reliable assay for testing antivirals. Here, we developed a quick and inexpensive high-throughput fluorescent assay to test inhibitors of viral proteases. Accordingly, we employed this assay to sample inhibitors for papain-like protease from SARS-CoV-2. In addition, we validated this assay for screening inhibitors of flaviviral protease from the tick-borne encephalitis virus to emphasize a broad range of applications of our approach. This fluorescent high-throughput assay is based on fluorescent energy transfer (FRET) between two distinct fluorescent proteins (eGFP and mCherry) connected via a substrate polypeptide. When the substrate is cleaved, FRET is abolished and the change in fluorescence corresponds to reaction progress. Our data show that this assay can be used for testing the inhibitors in the 96 or 384 well plates format with robust and reproducible outcomes.

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    SciScore for 10.1101/2020.10.27.357418: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The images were quantified using ImageQuant TL.
    ImageQuant
    suggested: (ImageQuant, RRID:SCR_014246)

    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.

  2. Version published to 10.1101/2020.10.27.357418 on bioRxiv