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Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics

MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of the...

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Autores principales: Machat, Mohamed, Langenfeld, Florent, Craciun, Daniela, Sirugue, Léa, Labib, Taoufik, Lagarde, Nathalie, Maria, Maxime, Montes, Matthieu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652110/
https://www.ncbi.nlm.nih.gov/pubmed/34247232
http://dx.doi.org/10.1093/bioinformatics/btab511
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author Machat, Mohamed
Langenfeld, Florent
Craciun, Daniela
Sirugue, Léa
Labib, Taoufik
Lagarde, Nathalie
Maria, Maxime
Montes, Matthieu
author_facet Machat, Mohamed
Langenfeld, Florent
Craciun, Daniela
Sirugue, Léa
Labib, Taoufik
Lagarde, Nathalie
Maria, Maxime
Montes, Matthieu
author_sort Machat, Mohamed
collection PubMed
description MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. RESULTS: Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. AVAILABILITYAND IMPLEMENTATION: All data are available online at http://datasetmachat.drugdesign.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-86521102021-12-08 Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics Machat, Mohamed Langenfeld, Florent Craciun, Daniela Sirugue, Léa Labib, Taoufik Lagarde, Nathalie Maria, Maxime Montes, Matthieu Bioinformatics Original Papers MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. RESULTS: Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. AVAILABILITYAND IMPLEMENTATION: All data are available online at http://datasetmachat.drugdesign.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-07-11 /pmc/articles/PMC8652110/ /pubmed/34247232 http://dx.doi.org/10.1093/bioinformatics/btab511 Text en © The Author(s) 2021. 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 Papers
Machat, Mohamed
Langenfeld, Florent
Craciun, Daniela
Sirugue, Léa
Labib, Taoufik
Lagarde, Nathalie
Maria, Maxime
Montes, Matthieu
Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title_full Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title_fullStr Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title_full_unstemmed Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title_short Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
title_sort comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652110/
https://www.ncbi.nlm.nih.gov/pubmed/34247232
http://dx.doi.org/10.1093/bioinformatics/btab511
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