Structural Insights of SARS-CoV-2 Spike Protein from Delta and Omicron Variants

  1. Ali Sadek
  2. David Zaha
  3. Mahmoud Salama Ahmed

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Abstract

Given the continuing heavy toll of the COVID-19 pandemic and the emergence of the Delta (B.1.617.2) and Omicron (B.1.1.529) variants, the WHO declared both as variants of concern (VOC). There are valid concerns that the latest Omicron variant might have increased infectivity and pathogenicity. In addition, the sheer number of S protein mutations in the Omicron variant raise concerns of potential immune evasion and resistance to therapeutics such as monoclonal antibodies. However, structural insights that underpin the potential increased pathogenicity are unknown. Here we adopted an artificial intelligence (AI)-based approach to predict the structural changes induced by mutations of the Delta and Omicron variants in the spike (S) protein using Alphafold. This was followed by docking the human angiotensin-converting enzyme 2 (ACE2) with the predicted S proteins for Wuhan-Hu-1, Delta, and Omicron variants. Our in-silico structural analysis indicates that S protein for Omicron variant has a higher binding affinity to ACE-2 receptor, compared to Wuhan-Hu-1 and Delta variants. In addition, the recognition sites of the receptor binding domains for Delta and Omicron variants showed lower electronegativity compared to Wuhan-Hu-1. Importantly, further molecular insights revealed significant changes induced at fusion protein (FP) site, which may mediate enhanced viral entry. These results represent the first computational analysis of structural changes associated with Omicron variant using Alphafold, Collectively, our results highlight potential structural basis for enhanced pathogenicity of the Omicron variant, however further validation using X-ray crystallography and cryo-EM are warranted.

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    SciScore for 10.1101/2021.12.08.471777: (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
    The top models were selected for generation of electrostatic potential map and visualization using Pymol (version 2.0.6 by Schrödinger).
    Pymol
    suggested: (PyMOL, RRID:SCR_000305)

    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.

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

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  2. Version published to 10.1101/2021.12.08.471777v1 on bioRxiv