Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants

  1. Ye-Fan Hu
  2. Jing-Chu Hu
  3. Hua-Rui Gong
  4. Antoine Danchin
  5. Ren Sun
  6. Hin Chu
  7. Ivan Fan-Ngai Hung
  8. Kwok Yung Yuen
  9. Kelvin Kai-Wang To
  10. Bao-Zhong Zhang
  11. Thomas Yau
  12. Jian-Dong Huang

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Abstract

It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online .

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  1. Version published to 10.3389/fimmu.2022.861050
  2. ScreenIT

    SciScore for 10.1101/2022.01.19.477009: (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
    Genetic and antigenic difference calculation: We selected 1521 SARS-CoV-2 lineages using PANGO (v.3.1.15) updated on December 6, 2021 (https://cov-lineages.org/).
    PANGO
    suggested: None
    For genetic distances, we used Molecular Evolutionary Genetics Analysis (MEGA) X to calculate the pairwise distances among Spike protein amino acid sequences in the SARS-CoV-2 variants using a Poisson model.
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  3. Version published to 10.1101/2022.01.19.477009v1 on bioRxiv