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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23754939/?tool=EBI |
| _version_ | 1818459369217458176 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T23:13:15Z |
| format | Article |
| id | doaj.art-df26e93c156b4981883f02a9c0109818 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-14T23:13:15Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-df26e93c156b4981883f02a9c01098182022-12-21T22:44:09ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0196e100308710.1371/journal.pcbi.1003087Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome.Drew H BryantMark MollPaul W FinnLydia E KavrakiThe 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23754939/?tool=EBI |
| spellingShingle | Drew H Bryant Mark Moll Paul W Finn Lydia E Kavraki Combinatorial clustering of residue position subsets predicts inhibitor affinity across the human kinome. PLoS Computational Biology |
| title | 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_short | 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23754939/?tool=EBI |
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