Cargando…

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Corral-Corral, Ricardo, Beltrán, Jesús A., Brizuela, Carlos A., Del Rio, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151554/
https://www.ncbi.nlm.nih.gov/pubmed/28991206
http://dx.doi.org/10.3390/molecules22101673
_version_ 1783357177119899648
author Corral-Corral, Ricardo
Beltrán, Jesús A.
Brizuela, Carlos A.
Del Rio, Gabriel
author_facet Corral-Corral, Ricardo
Beltrán, Jesús A.
Brizuela, Carlos A.
Del Rio, Gabriel
author_sort Corral-Corral, Ricardo
collection PubMed
description Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.
format Online
Article
Text
id pubmed-6151554
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-61515542018-11-13 Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure Corral-Corral, Ricardo Beltrán, Jesús A. Brizuela, Carlos A. Del Rio, Gabriel Molecules Article Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins. MDPI 2017-10-09 /pmc/articles/PMC6151554/ /pubmed/28991206 http://dx.doi.org/10.3390/molecules22101673 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Corral-Corral, Ricardo
Beltrán, Jesús A.
Brizuela, Carlos A.
Del Rio, Gabriel
Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title_full Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title_fullStr Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title_full_unstemmed Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title_short Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure
title_sort systematic identification of machine-learning models aimed to classify critical residues for protein function from protein structure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6151554/
https://www.ncbi.nlm.nih.gov/pubmed/28991206
http://dx.doi.org/10.3390/molecules22101673
work_keys_str_mv AT corralcorralricardo systematicidentificationofmachinelearningmodelsaimedtoclassifycriticalresiduesforproteinfunctionfromproteinstructure
AT beltranjesusa systematicidentificationofmachinelearningmodelsaimedtoclassifycriticalresiduesforproteinfunctionfromproteinstructure
AT brizuelacarlosa systematicidentificationofmachinelearningmodelsaimedtoclassifycriticalresiduesforproteinfunctionfromproteinstructure
AT delriogabriel systematicidentificationofmachinelearningmodelsaimedtoclassifycriticalresiduesforproteinfunctionfromproteinstructure