Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved...

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Autores principales:	Cristina Marino Buslje, Elin Teppa, Tomas Di Doménico, José María Delfino, Morten Nielsen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/91080b0bdff54c6b99c29c6764821018
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spelling	oai:doaj.org-article:91080b0bdff54c6b99c29c67648210182021-11-18T05:50:52ZNetworks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.1553-734X1553-735810.1371/journal.pcbi.1000978https://doaj.org/article/91080b0bdff54c6b99c29c67648210182010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21079665/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.Cristina Marino BusljeElin TeppaTomas Di DoménicoJosé María DelfinoMorten NielsenPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 11, p e1000978 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Cristina Marino Buslje Elin Teppa Tomas Di Doménico José María Delfino Morten Nielsen Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
description	Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.
format	article
author	Cristina Marino Buslje Elin Teppa Tomas Di Doménico José María Delfino Morten Nielsen
author_facet	Cristina Marino Buslje Elin Teppa Tomas Di Doménico José María Delfino Morten Nielsen
author_sort	Cristina Marino Buslje
title	Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
title_short	Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
title_full	Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
title_fullStr	Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
title_full_unstemmed	Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
title_sort	networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/91080b0bdff54c6b99c29c6764821018
work_keys_str_mv	AT cristinamarinobuslje networksofhighmutualinformationdefinethestructuralproximityofcatalyticsitesimplicationsforcatalyticresidueidentification AT elinteppa networksofhighmutualinformationdefinethestructuralproximityofcatalyticsitesimplicationsforcatalyticresidueidentification AT tomasdidomenico networksofhighmutualinformationdefinethestructuralproximityofcatalyticsitesimplicationsforcatalyticresidueidentification AT josemariadelfino networksofhighmutualinformationdefinethestructuralproximityofcatalyticsitesimplicationsforcatalyticresidueidentification AT mortennielsen networksofhighmutualinformationdefinethestructuralproximityofcatalyticsitesimplicationsforcatalyticresidueidentification
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Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

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