Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome: e1003087

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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Bibliographic Details
Published in:PLoS computational biology 2013-06, Vol.9 (6)
Main Authors: Bryant, Drew H, Moll, Mark, Finn, Paul W, Kavraki, Lydia E
Format: Article
Language:English
Subjects:
Affinity
Binding
Binding sites
Combinatorial analysis
Comparative analysis
Experiments
Genomes
Inhibitors
Kinases
Labeling
Proteins
Residues
Scholarships & fellowships
Similarity
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Summary: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.
ISSN:1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003087