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Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled recepto...

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Detalles Bibliográficos
Autores principales: Baldessari, Filippo, Capelli, Riccardo, Carloni, Paolo, Giorgetti, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260681/
https://www.ncbi.nlm.nih.gov/pubmed/32489528
http://dx.doi.org/10.1016/j.csbj.2020.05.003
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author Baldessari, Filippo
Capelli, Riccardo
Carloni, Paolo
Giorgetti, Alejandro
author_facet Baldessari, Filippo
Capelli, Riccardo
Carloni, Paolo
Giorgetti, Alejandro
author_sort Baldessari, Filippo
collection PubMed
description We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity.
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spelling pubmed-72606812020-06-01 Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors Baldessari, Filippo Capelli, Riccardo Carloni, Paolo Giorgetti, Alejandro Comput Struct Biotechnol J Research Article We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity. Research Network of Computational and Structural Biotechnology 2020-05-15 /pmc/articles/PMC7260681/ /pubmed/32489528 http://dx.doi.org/10.1016/j.csbj.2020.05.003 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Baldessari, Filippo
Capelli, Riccardo
Carloni, Paolo
Giorgetti, Alejandro
Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title_full Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title_fullStr Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title_full_unstemmed Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title_short Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors
title_sort coevolutionary data-based interaction networks approach highlighting key residues across protein families: the case of the g-protein coupled receptors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260681/
https://www.ncbi.nlm.nih.gov/pubmed/32489528
http://dx.doi.org/10.1016/j.csbj.2020.05.003
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