Diversity of ACE2 and its interaction with SARS-CoV-2 receptor binding domain

  1. Jessie Low-Gan
  2. Ruiqi Huang
  3. Gabrielle Warner
  4. Abigail Kelley
  5. Duncan McGregor
  6. Vaughn Smider

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Abstract

COVID-19, the clinical syndrome caused by the SARS-CoV-2 virus, has rapidly spread globally causing tens of millions of infections and over a million deaths. The potential animal reservoirs for SARS-CoV-2 are currently unknown, however sequence analysis has provided plausible potential candidate species. SARS-CoV-2 binds to the angiotensin I converting enzyme 2 (ACE2) to enable its entry into host cells and establish infection. We analyzed the binding surface of ACE2 from several important animal species to begin to understand the parameters for the ACE2 recognition by the SARS-CoV-2 spike protein receptor binding domain (RBD). We employed Shannon entropy analysis to determine the variability of ACE2 across its sequence and particularly in its RBD interacting region, and assessed differences between various species’ ACE2 and human ACE2. As cattle are a known reservoir for coronaviruses with previous human zoonotic transfer, and has a relatively divergent ACE2 sequence, we compared the binding kinetics of bovine and human ACE2 to SARS-CoV-2 RBD. This revealed a nanomolar binding affinity for bovine ACE2 but an approximate ten-fold reduction of binding compared to human ACE2. Since cows have been experimentally infected by SARS-CoV-2, this lower affinity sets a threshold for sequences with lower homology to human ACE2 to be able to serve as a productive viral receptor for SARS-CoV-2.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.25.354548: (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

    Antibodies
    SentencesResources
    Protein purification: Human and bovine ACE2 proteins were produced as fusion proteins to human IgG1 Fc according to our published methods for monoclonal antibody purification[2-4].
    human IgG1
    suggested: None
    Software and Algorithms
    SentencesResources
    Phylogenetic trees and pairwise percent identities were obtained with Clustal Omega (47) and visualized in UGENE (http://ugene.net).
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    Using a de novo python script, these residues were extracted into a separate “sequence” and an additional, specific multiple sequence alignment was constructed.
    python
    suggested: (IPython, RRID:SCR_001658)
    Structural analyses were performed in Visual Molecular Dynamics (VMD, https://www.ks.uiuc.edu/Research/vmd/).
    https://www.ks.uiuc.edu/Research/vmd/
    suggested: (Visual Molecular Dynamics, RRID:SCR_001820)
    Antigen-binding curves and EC50 values were generated and calculated using four parameter logistic regression in GraphPad Prism 8 (GraphPad Software, Inc.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.25.354548 on bioRxiv