Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enz...

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Autores principales:	Chioko Nagao, Nozomi Nagano, Kenji Mizuguchi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70f14435166c48d88131aa6ae073cfb4
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spelling	oai:doaj.org-article:70f14435166c48d88131aa6ae073cfb42021-11-18T08:38:32ZPrediction of detailed enzyme functions and identification of specificity determining residues by random forests.1932-620310.1371/journal.pone.0084623https://doaj.org/article/70f14435166c48d88131aa6ae073cfb42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24416252/?tool=EBIhttps://doaj.org/toc/1932-6203Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.Chioko NagaoNozomi NaganoKenji MizuguchiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e84623 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chioko Nagao Nozomi Nagano Kenji Mizuguchi Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
description	Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily.
format	article
author	Chioko Nagao Nozomi Nagano Kenji Mizuguchi
author_facet	Chioko Nagao Nozomi Nagano Kenji Mizuguchi
author_sort	Chioko Nagao
title	Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
title_short	Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
title_full	Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
title_fullStr	Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
title_full_unstemmed	Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
title_sort	prediction of detailed enzyme functions and identification of specificity determining residues by random forests.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/70f14435166c48d88131aa6ae073cfb4
work_keys_str_mv	AT chiokonagao predictionofdetailedenzymefunctionsandidentificationofspecificitydeterminingresiduesbyrandomforests AT nozominagano predictionofdetailedenzymefunctionsandidentificationofspecificitydeterminingresiduesbyrandomforests AT kenjimizuguchi predictionofdetailedenzymefunctionsandidentificationofspecificitydeterminingresiduesbyrandomforests
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Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.

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