Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2

  1. Henrik Wiechers
  2. Benjamin Eltzner
  3. Kanti V. Mardia
  4. Stephan F. Huckemann

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Motivation

Reconstructions of structure of biomolecules, for instance via X-ray crystallography or cryo-EM frequently contain clashes of atomic centers. Correction methods are usually based on simulations approximating biophysical chemistry, making them computationally expensive and often not correcting all clashes.

Results

We propose a computationally fast data-driven statistical method yielding suites free from within-suite clashes: From such a clash free training data set, devising mode hunting after torus PCA on adaptive cutting average linkage tree clustering (MINTAGE), we learn RNA suite shapes. With classification based on multiscale structure enhancement (CLEAN), for a given clash suite we determine its neighborhood on a mesoscopic scale involving several suites. As corrected suite we propose the Fréchet mean on a torus of the largest classes in this neighborhood. We validate CLEAN MINTAGE on a benchmark data set, compare it to a state of the art correction method and apply it, as proof of concept, to two exemplary suites adjacent to helical pieces of the frameshift stimulation element of SARS-CoV-2 which are difficult to reconstruct. In contrast to a recent reconstruction proposing several different structure models, CLEAN MINTAGE unanimously proposes structure corrections within the same clash free class for all suites.

Code Availability

https://gitlab.gwdg.de/henrik.wiechers1/clean-mintage-code

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.08.06.455406: (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

    No key resources detected.

    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.

  2. Version published to 10.1101/2021.08.06.455406v1 on bioRxiv