in silico Assessment of Antibody Drug Resistance to Bamlanivimab of SARS-CoV-2 Variant B.1.617

  1. Leili Zhang
  2. Tien Huynh
  3. Binquan Luan

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Abstract

The highly infectious SARS-CoV-2 variant B.1.617 with double mutations E484Q and L452R in the receptor binding domain (RBD) of SARS-CoV-2’s spike protein is worrisome. Demonstrated in crystal structures, the residues 452 and 484 in RBD are not in direct contact with interfacial residues in the angiotensin converting enzyme 2 (ACE2). This suggests that albeit there are some possibly nonlocal effects, the E484Q and L452R mutations might not significantly affect RBD’s binding with ACE2, which is an important step for viral entry into host cells. Thus, without the known molecular mechanism, these two successful mutations (from the point of view of SARS-CoV-2) can be hypothesized to evade human antibodies. Using in silico all-atom molecular dynamics (MD) simulation as well as deep learning (DL) approaches, here we show that these two mutations significantly reduce the binding affinity between RBD and the antibody LY-CoV555 (also named as Bamlanivimab) that was proven to be efficacious for neutralizing the wide-type SARS-CoV-2. With the revealed molecular mechanism on how L452R and E484K evade LY-CoV555, we expect that more specific therapeutic antibodies can be accordingly designed and/or a precision mixing of antibodies can be achieved in a cocktail treatment for patients infected with the variant B.1.617.

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    SciScore for 10.1101/2021.05.12.443826: (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
    MD simulations: We carried out all-atom MD simulations for the complex of RBD and the Fab of LY-CoV555 (PDB code: 7KMG) using the NAMD2.13 package28 running on the IBM Power Cluster.
    NAMD2.13
    suggested: None

    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 10.1101/2021.05.12.443826v1 on bioRxiv