Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

  1. Genevieve Kunkel
  2. Mohammad Madani
  3. Simon J. White
  4. Paulo H. Verardi
  5. Anna Tarakanova

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  1. Version published to 10.1016/j.bpj.2021.11.009
  2. ScreenIT

    SciScore for 10.1101/2021.08.19.456973: (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
    We use the Python programming library ProDy (56) to construct ANM models along protein alpha carbons, form associated Hessian matrices (topological description), and conduct NMA (diagonalization of the Hessian) in Cartesian space.
    Python
    suggested: (IPython, RRID:SCR_001658)
    This data, in combination with starting structure coordinates, is used as input for a layered hierarchical agglomerative clustering algorithm that utilizes functions from the Scikit-learn programming library (61).
    Scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Sequence and structure analysis: One-to-One sequence comparisons are made using the BLAST Needleman-Wunsch Global Alignment software through the Blastp protein-protein webserver, where the wild type (WT) sequence is the subject sequence and the mutant is the query sequence (63).
    Blastp
    suggested: (BLASTP, RRID:SCR_001010)
    The BLAST tool gives an estimation of similarity between query and subject sequences.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    Multiple sequence alignment is performed on all presented sequences using the Clustal-Omega webserver on its default settings (64).
    Clustal-Omega
    suggested: None
    Structural alignments and root mean square deviation (RMSD) calculations are performed using the “super” tool within the PyMol alignment software suite (
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)

    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.

    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.

  3. Version published to 10.1101/2021.08.19.456973v1 on bioRxiv