Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, co...

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Autores principales:	Drew H Bryant, Mark Moll, Paul W Finn, Lydia E Kavraki
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/df26e93c156b4981883f02a9c0109818
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spelling	oai:doaj.org-article:df26e93c156b4981883f02a9c01098182021-11-18T05:52:07ZCombinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.1553-734X1553-735810.1371/journal.pcbi.1003087https://doaj.org/article/df26e93c156b4981883f02a9c01098182013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23754939/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.Drew H BryantMark MollPaul W FinnLydia E KavrakiPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 6, p e1003087 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Drew H Bryant Mark Moll Paul W Finn Lydia E Kavraki Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
description	The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.
format	article
author	Drew H Bryant Mark Moll Paul W Finn Lydia E Kavraki
author_facet	Drew H Bryant Mark Moll Paul W Finn Lydia E Kavraki
author_sort	Drew H Bryant
title	Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
title_short	Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
title_full	Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
title_fullStr	Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
title_full_unstemmed	Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
title_sort	combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/df26e93c156b4981883f02a9c0109818
work_keys_str_mv	AT drewhbryant combinatorialclusteringofresiduepositionsubsetspredictsinhibitoraffinityacrossthehumankinome AT markmoll combinatorialclusteringofresiduepositionsubsetspredictsinhibitoraffinityacrossthehumankinome AT paulwfinn combinatorialclusteringofresiduepositionsubsetspredictsinhibitoraffinityacrossthehumankinome AT lydiaekavraki combinatorialclusteringofresiduepositionsubsetspredictsinhibitoraffinityacrossthehumankinome
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Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.

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