Spike protein binding prediction with neutralizing antibodies of SARS-CoV-2

  1. Tamina Park
  2. Sang-Yeop Lee
  3. Seil Kim
  4. Mi Jeong Kim
  5. Hong Gi Kim
  6. Sangmi Jun
  7. Seung Il Kim
  8. Bum Tae Kim
  9. Edmond Changkyun Park
  10. Daeui Park

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Abstract

Coronavirus disease 2019 (COVID-19) is a new emerging human infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2, also previously known as 2019-nCoV), originated in Wuhan seafood and animal market, China. Since December 2019, more than 69,000 cases of COVID-19 have been confirmed in China and quickly spreads to other counties. Currently, researchers put their best efforts to identify effective drugs for COVID-19. The neutralizing antibody, which binds to viral capsid in a manner that inhibits cellular entry of virus and uncoating of the genome, is the specific defense against viral invaders. In this study, we investigate to identify neutralizing antibodies that can bind to SARS-CoV-2 Sipke (S) protein and interfere with the interaction between viral S protein and a host receptor by bioinformatic methods. The sequence analysis of S protein showed two major differences in the RBD region of the SARS-CoV-2 S protein compared to SARS-CoV and SARS-CoV related bat viruses (btSARS-CoV). The insertion regions were close to interacting residues with the human ACE2 receptor. Epitope analysis of neutralizing antibodies revealed that SARS-CoV neutralizing antibodies used conformational epitopes, whereas MERS-CoV neutralizing antibodies used a common linear epitope region, which contributes to form the β-sheet structure in MERS-CoV S protein and deleted in SARS-CoV-2 S protein. To identify effective neutralizing antibodies for SARS-CoV-2, the binding affinities of neutralizing antibodies with SARS-CoV-2 S protein were predicted and compared by antibody-antigen docking simulation. The result showed that CR3022 neutralizing antibody from human may have higher binding affinity with SARS-CoV-2 S protein than SARS-CoV S protein. We also found that F26G19 and D12 mouse antibodies could bind to SARS-CoV S protein with high affinity. Our findings provide crucial clues towards the development of antigen diagnosis, therapeutic antibody, and the vaccine against SARS-CoV-2.

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  1. ScreenIT

    SciScore for 10.1101/2020.02.22.951178: (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

    Antibodies
    SentencesResources
    Antibody-antigen docking simulation: Docking simlutation between the RBD of SARS-CoV-2 and certain SARS-CoV and MERS-CoV antibodies were implemented with Rosetta antibody-antigen docking protocols [32]
    MERS-CoV
    suggested: None
    antibody-antigen docking protocols [ 32
    suggested: None
    With the complex structures of RBD and antibody candidates, all-atom relax protocol, docking prepack protocol, and antibody-antigen docking simulation were carried out to calculate the free energy of low-energy binding conformations.
    antibody-antigen docking simulation
    suggested: None
    The distiribution of docking scores displayed as funnel plots using interface RMD (interface RMS) versus the binding score (dG binding) between antibody and antigen (Fig. 4).
    antigen (Fig. 4
    suggested: None
    In addition, 1000 independent docking runs were performed to generate the antibody-antigen models.
    antibody-antigen models.
    suggested: None
    Software and Algorithms
    SentencesResources
    Phylogenetic analysis of SARS-CoV-2 S protein: To comparing of S gene containing S protein among SARS-CoV-2, SARS-CoV, and MERS-CoV strains, the nucleotide sequences of S gene were retrieved from GISAID [19] and ViPR [20].
    ViPR
    suggested: (vipR, RRID:SCR_010685)
    The sequence alignments and phylogenetic analysis were done using MEGA X [21].
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    The nucleotide sequence were codon aligned using ClustalW with default parameters and the phylogenetic tree was inferred using neighbor-joining [22], maximum-likelihood [23], and maximum-parsimony methods [24].
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    Conservation score and epitope mapping of SARS-CoV-2 S protein: The conservation score of amino acid positions on S protein in SARS-CoV-2 was calculated by ConSurf program [27].
    ConSurf
    suggested: (ConSurf Database, RRID:SCR_002320)
    And then the structures were superimposed according to those pairwise alignments using MatchMaker program [29].
    MatchMaker
    suggested: (QTL Matchmaker, RRID:SCR_000741)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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

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  2. Version published to 10.1101/2020.02.22.951178 on bioRxiv