Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling

  1. Marco Zaccaria
  2. Luigi Genovese
  3. Brigitte E. Lawhorn
  4. William Dawson
  5. Andrew S. Joyal
  6. Jingqing Hu
  7. Patrick Autissier
  8. Takahito Nakajima
  9. Welkin E. Johnson
  10. Ismael Fofana
  11. Michael Farzan
  12. Babak Momeni

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Abstract

Ab initio quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.

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  1. Version published to 10.1098/rsif.2023.0614
  2. Version published to 10.1101/2021.12.01.470748v2 on bioRxiv
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    SciScore for 10.1101/2021.12.01.470748: (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
    Structure relaxations are performed by optimizing the crystal geometry with the OpenMM package using the AMBER FF14SB force field [9].
    OpenMM
    suggested: (OpenMM, RRID:SCR_000436)

    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.

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  4. Version published to 10.1101/2021.12.01.470748v1 on bioRxiv