An exploration of the SARS-CoV-2 spike receptor binding domain (RBD) – a complex palette of evolutionary and structural features

  1. Dwipanjan Sanyal
  2. Sourav Chowdhury
  3. Vladimir N. Uversky
  4. Krishnananda Chattopadhyay

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Abstract

SARS-CoV-2 spike protein (S) is associated with the entry of virus inside the host cell by recruiting its loop dominant receptor binding domain (RBD) and interacting with the host ACE2 receptor. Our study deploying a two-tier approach encompassing evolutionary and structural analysis provides a comprehensive picture of the RBD, which could be of potential use for better understanding the RBD and address its druggability issues. Resorting to an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence space. Guided by structure network analysis and Monte Carlo simulation we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility of the binding cleft. We further deployed fuzzy C-means clustering by plugging the evolutionary and structural features of discrete structure blocks of RBD to understand which structure blocks share maximum overlap based on their evolutionary and structural features. Deploying this multi-tier interlinked approach, which essentially distilled the evolutionary and structural features of RBD, we highlight discrete region, which could be a potential druggable pocket thereby destabilizing the structure and addressing evolutionary routes.

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    SciScore for 10.1101/2020.05.31.126615: (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
    This enzyme dataset was further used in Clustal Omega in order to obtain multiple sequence alignment (MSA) (11, 12).
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    On Python 3 we deployed EVcoupling to assess the quantitative effects of mutations in RBD(23).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Analysis and Representation: Majority of evolutionary and structural analysis were done with Python3.
    Python3
    suggested: None
    Protein models were represented using PyMol.
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)
    Structure network analysis using Bio3D package was carried out on RStudio.
    Bio3D
    suggested: None
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.05.31.126615v1 on bioRxiv