Insights on cross-species transmission of SARS-CoV-2 from structural modeling

  1. João P. G. L. M. Rodrigues
  2. Susana Barrera-Vilarmau
  3. João M. C. Teixeira
  4. Marija Sorokina
  5. Elizabeth Seckel
  6. Panagiotis L. Kastritis
  7. Michael Levitt

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the ongoing global pandemic that has infected more than 31 million people in more than 180 countries worldwide. Like other coronaviruses, SARS-CoV-2 is thought to have been transmitted to humans from wild animals. Given the scale and widespread geographical distribution of the current pandemic and confirmed cases of cross-species transmission, the question of the extent to which this transmission is possible emerges, as well as what molecular features distinguish susceptible from non-susceptible animal species. Here, we investigated the structural properties of several ACE2 orthologs bound to the SARS-CoV-2 spike protein. We found that species known not to be susceptible to SARS-CoV-2 infection have non-conservative mutations in several ACE2 amino acid residues that disrupt key polar and charged contacts with the viral spike protein. Our models also allow us to predict affinity-enhancing mutations that could be used to design ACE2 variants for therapeutic purposes. Finally, our study provides a blueprint for modeling viral-host protein interactions and highlights several important considerations when designing these computational studies and analyzing their results.

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  1. Version published to 10.1371/journal.pcbi.1008449
  2. Version published to 10.1101/2020.06.05.136861v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.05.136861: (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 sequences were aligned with MAFFT version 7 [27,28], using the alignment method FFT-NS-i (Standard).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Modeling of ACE2 Orthologs: The modeling of ACE2 orthologs was carried out using MODELLER 9.24 [29] and custom Python scripts (available here: https://github.com/joaorodrigues//ace2-animal-models/).
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This result highlights the limitations of our models and stresses the need for experimental validation for all our predictions. In summary, our protocol combines structural, sequence, and binding data to create a structure-based framework to understand SARS-CoV-2 susceptibility across different animal species. Our models help rationalize the impact of naturally-occurring ACE2 mutations on SARS-CoV-2 RBD binding and explain why certain species are not susceptible to infection with the virus. In addition, we propose possible affinity-enhancing mutants that can help guide engineering efforts for the development of ACE2-based antiviral therapeutics. Our protocol and models can easily be replicated using freely-available tools and web servers and serve as a blueprint for future modeling studies on protein interactions where data is available for a large number of homologues. Finally, to prevent human-to-animal transmission, we recommend following the World Organization for Animal Health guidelines: people infected with COVID-19 should limit contact with their pets, as well as with other animals (including humans).

    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.

    About SciScore

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  4. Version published to 10.1101/2020.06.05.136861v1 on bioRxiv