PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning

  1. Eric W. Bell
  2. Jacob H. Schwartz
  3. Peter L. Freddolino
  4. Yang Zhang

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Abstract

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  1. Version published to 10.1016/j.jmb.2022.167530
  2. ScreenIT

    SciScore for 10.1101/2021.12.02.470917: (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
    These modules include a conjoint triad trained neural network, a STRING database lookup module, and two “interology” based modules: a threading-based module using a modified version of SPRING and a sequence-based module using BLAST.
    STRING
    suggested: (STRING, RRID:SCR_005223)
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    4.2 SARS-CoV-2 Virus and human host protein sequence collection: The SARS-CoV-2 proteome was collected from the UniProtKB pre-release.
    UniProtKB
    suggested: (UniProtKB, RRID:SCR_004426)
    The “gold standard” dataset is comprised interactions listed in the PSICQUIC database annotated as “direct interaction” as of April 2021 between the proteins of either SARS-CoV-1 or SARS-CoV-2 and human proteins, a total of 128 interactions.
    PSICQUIC
    suggested: (PSICQUIC, RRID:SCR_006389)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2021.12.02.470917v1 on bioRxiv