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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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 |
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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 |
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