ACE2‐Variants Indicate Potential SARS‐CoV‐2‐Susceptibility in Animals: A Molecular Dynamics Study

  1. Szymon Pach
  2. Trung Ngoc Nguyen
  3. Jakob Trimpert
  4. Dusan Kunec
  5. Nikolaus Osterrieder
  6. Gerhard Wolber

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Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) continues to be a global threat, causing millions of deaths worldwide. SARS‐CoV‐2 is an enveloped virus with spike (S) glycoproteins conferring binding to the host cell‘s angiotensin‐converting enzyme 2 (ACE2), which is critical for cellular entry. The host range of the virus extends well beyond humans and non‐human primates. Natural and experimental infections have confirmed the high susceptibility of cats, ferrets, and Syrian hamsters, whereas dogs, mice, rats, pigs, and chickens are refractory to SARS‐CoV‐2 infection. To investigate the underlying reason for the variable susceptibility observed in different species, we have developed molecular descriptors to efficiently analyse dynamic simulation models of complexes between SARS‐CoV‐2 S and ACE2. Our extensive analyses represent the first systematic structure‐based approach that allows predictions of species susceptibility to SARS‐CoV‐2 infection.

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  1. Version published to 10.1002/minf.202100031
  2. ScreenIT

    SciScore for 10.1101/2020.05.14.092767: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ACE2-RBD-complex simulations were prepared with Maestro 11.7 [Schrödinger, LLC: New York, USA] and carried out with Desmond 5.5.16 All systems were inspected for atom clashes, optimized for H-bonds, filled in 12 Å large padding box with SCP water model17, sodium chloride 0.15 M and sodium ions to keep isotonic and electrostatic neutral conditions.
    Maestro
    suggested: (Maestro, RRID:SCR_016748)
    Desmond
    suggested: (Desmond, RRID:SCR_014575)
    Further analysis was performed with Python 3.719 using MDAnalysis 0.19.3 for the extraction of distances, angles, and hydrogen bonds from trajectories after an equilibration period of 10 ns (resulting in 1800 complex conformations per replicon).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Data processing and transformation was done with pandas 0.25.3.20 Plots were created with seaborn 0.9.0.21 and matplotlib 3.1.1.22
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.14.092767v1 on bioRxiv