Evidence map›Paper›PMID 36699404›Full record

ArticleBioinformatics advances2022

PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure.

Thomas D Townsley, James T Wilson, Harrison Akers, Timothy Bryant, Salvador Cordova, T L Wallace, Kirk K Durston, Joseph E Deweese

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Thomas D TownsleyDepartment of Computational Sciences, College of Computing & Technology, Lipscomb University, Nashville, TN 37204, USA.ORCID https://orcid.org/0000-0002-0877-0523
James T WilsonDepartment of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA.
Harrison AkersDepartment of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA.
Timothy BryantDepartment of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA.
Salvador CordovaFMS Foundation, Canandaigua, NY 14424, USA.
T L WallaceDepartment of Computational Sciences, College of Computing & Technology, Lipscomb University, Nashville, TN 37204, USA.
Kirk K DurstonDepartment of Research and Publications, Digital Strategies, Langley, BC V2Y 1N5, Canada.
Joseph E DeweeseDepartment of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, Lipscomb University, Nashville, TN 37204, USA.ORCID https://orcid.org/0000-0001-9683-6723
Lipscomb University · USCanandaigua VA Medical Center · USFreed–Hardeman University · USMeharry Medical College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: AlphaFold has been a major advance in predicting protein structure, but still leaves the problem of determining which sub-molecular components of a protein are essential for it to carry out its function within the cell. Direct coupling analysis predicts two- and three-amino acid contacts, but there may be essential interdependencies that are not proximal within the 3D structure. The problem to be addressed is to design a computational method that locates and ranks essential non-proximal interdependencies within a protein involving five or more amino acids, using large, multiple sequence alignments (MSAs) for both globular and intrinsically unstructured proteins. Results: We developed PSICalc (Protein Subdomain Interdependency Calculator), a laptop-friendly, pattern-discovery, bioinformatics software tool that analyzes large MSAs for both structured and unstructured proteins, locates both proximal and non-proximal inter-dependent sites, and clusters them into pairwise (second order), third-order and higher-order clusters using a k-modes approach, and provides ranked results within minutes. To aid in visualizing these interdependencies, we developed a graphical user interface that displays these subdomain relationships as a polytree graph. To demonstrate, we provide examples of both proximal and non-proximal interdependencies documented for eukaryotic topoisomerase II including between the unstructured C-terminal domain and the N-terminal domain. Availability and implementation: https://github.com/jdeweeselab/psicalc-package. Supplementary information: Supplementary data are available at

Identifiers

PMID36699404
PMCPMC9710643
OpenAlexW4292202632

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.