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pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations

[Image: see text] The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand...

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Autores principales: Gopi, Soundhararajan, Devanshu, Devanshu, Rajasekaran, Nandakumar, Anantakrishnan, Sathvik, Naganathan, Athi N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977024/
https://www.ncbi.nlm.nih.gov/pubmed/31984271
http://dx.doi.org/10.1021/acsomega.9b03371
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author Gopi, Soundhararajan
Devanshu, Devanshu
Rajasekaran, Nandakumar
Anantakrishnan, Sathvik
Naganathan, Athi N.
author_facet Gopi, Soundhararajan
Devanshu, Devanshu
Rajasekaran, Nandakumar
Anantakrishnan, Sathvik
Naganathan, Athi N.
author_sort Gopi, Soundhararajan
collection PubMed
description [Image: see text] The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances—a measure of the extent of percolation on perturbation—and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers.
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spelling pubmed-69770242020-01-24 pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations Gopi, Soundhararajan Devanshu, Devanshu Rajasekaran, Nandakumar Anantakrishnan, Sathvik Naganathan, Athi N. ACS Omega [Image: see text] The strength of intraprotein interactions or contact network is one of the dominant factors determining the thermodynamic stabilities of proteins. The nature and the extent of connectivity of this network also play a role in allosteric signal propagation characteristics upon ligand binding to a protein domain. Here, we develop a server for rapid quantification of the strength of an interaction network by employing an experimentally consistent perturbation approach previously validated against a large data set of 375 mutations in 19 different proteins. The web server can be employed to predict the extent of destabilization of proteins arising from mutations in the protein interior in experimentally relevant units. Moreover, coupling distances—a measure of the extent of percolation on perturbation—and overall perturbation magnitudes are predicted in a residue-specific manner, enabling a first look at the distribution of energetic couplings in a protein or its changes upon ligand binding. We show specific examples of how the server can be employed to probe for the distribution of local stabilities in a protein, to examine changes in side chain orientations or packing before and after ligand binding, and to predict changes in stabilities of proteins upon mutations of buried residues. The web server is freely available at http://pbl.biotech.iitm.ac.in/pPerturb and supports recent versions of all major browsers. American Chemical Society 2020-01-09 /pmc/articles/PMC6977024/ /pubmed/31984271 http://dx.doi.org/10.1021/acsomega.9b03371 Text en Copyright © 2020 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Gopi, Soundhararajan
Devanshu, Devanshu
Rajasekaran, Nandakumar
Anantakrishnan, Sathvik
Naganathan, Athi N.
pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title_full pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title_fullStr pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title_full_unstemmed pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title_short pPerturb: A Server for Predicting Long-Distance Energetic Couplings and Mutation-Induced Stability Changes in Proteins via Perturbations
title_sort pperturb: a server for predicting long-distance energetic couplings and mutation-induced stability changes in proteins via perturbations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977024/
https://www.ncbi.nlm.nih.gov/pubmed/31984271
http://dx.doi.org/10.1021/acsomega.9b03371
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