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PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710643/ https://www.ncbi.nlm.nih.gov/pubmed/36699404 http://dx.doi.org/10.1093/bioadv/vbac058 |
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author | Townsley, Thomas D Wilson, James T Akers, Harrison Bryant, Timothy Cordova, Salvador Wallace, T L Durston, Kirk K Deweese, Joseph E |
author_facet | Townsley, Thomas D Wilson, James T Akers, Harrison Bryant, Timothy Cordova, Salvador Wallace, T L Durston, Kirk K Deweese, Joseph E |
author_sort | Townsley, Thomas D |
collection | PubMed |
description | 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 Bioinformatics Advances online. |
format | Online Article Text |
id | pubmed-9710643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97106432023-01-24 PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure Townsley, Thomas D Wilson, James T Akers, Harrison Bryant, Timothy Cordova, Salvador Wallace, T L Durston, Kirk K Deweese, Joseph E Bioinform Adv Original Paper 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 Bioinformatics Advances online. Oxford University Press 2022-08-18 /pmc/articles/PMC9710643/ /pubmed/36699404 http://dx.doi.org/10.1093/bioadv/vbac058 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Townsley, Thomas D Wilson, James T Akers, Harrison Bryant, Timothy Cordova, Salvador Wallace, T L Durston, Kirk K Deweese, Joseph E PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title | PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title_full | PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title_fullStr | PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title_full_unstemmed | PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title_short | PSICalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
title_sort | psicalc: a novel approach to identifying and ranking critical non-proximal interdependencies within the overall protein structure |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710643/ https://www.ncbi.nlm.nih.gov/pubmed/36699404 http://dx.doi.org/10.1093/bioadv/vbac058 |
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