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Molecules 2017, 22(10), 1673; doi:10.3390/molecules22101673

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

1
Department of Biochemistry and Structural Biology, Instituto de Fisiologa Celular, Universidad Nacional Autónoma de México, México D.F. 04510, Mexico
2
Computer Science Department, CICESE Research Center, Ensenada, Baja California 22860, Mexico
*
Author to whom correspondence should be addressed.
Received: 14 August 2017 / Revised: 24 September 2017 / Accepted: 24 September 2017 / Published: 9 October 2017
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Abstract

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. View Full-Text
Keywords: protein structure; functional residues; machine learning protein structure; functional residues; machine learning
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MDPI and ACS Style

Corral-Corral, R.; Beltrán, J.A.; Brizuela, C.A.; Del Rio, G. Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure. Molecules 2017, 22, 1673.

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