Bioinformatics Advance Access published online on May 30, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm277
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Exploring sequence-structure relationships in the tyrosine kinome space : functional classification of the binding specificity mechanisms for cancer therapeutics
Department of Pharmaceutical Chemistry, School of Pharmacy, Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, KS 66047-1620, USA Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0392, USA
To whom correspondence should be addressed. Prof. Gennady Verkhivker, E-mail: verk{at}ku.edu, gverkhivker{at}san.rr.com
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Abstract |
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Motivation: Evolutionary and structural conservation patterns shared by more than 500 of identified protein kinases have led to complex sequence–structure relationships of cross-reactivity for kinase inhibitors. Understanding the molecular basis of binding specificity for protein kinases family, which is the central problem in discovery of cancer therapeutics, remains challenging as the inhibitor selectivity is not readily interpreted from chemical proteomics studies, neither it is easily discernable directly from sequence or structure information. We present an integrated view of sequence–structure–binding relationships in the tyrosine kinome space in which evolutionary analysis of the kinases binding sites is combined with computational proteomics profiling of the inhibitor–protein interactions. This approach provides a functional classification of the binding specificity mechanisms for cancer agents targeting protein tyrosine kinases.
Results: The proposed functional classification of the kinase binding specificities explores mechanisms in which structural plasticity of the tyrosine kinases and sequence variation of the binding site residues are linked with conformational preferences of the inhibitors in achieving effective drug binding. The molecular basis of binding specificity for tyrosine kinases may be largely driven by conformational adaptability of the inhibitors to an ensemble of structurally different conformational states of the enzyme, rather being determined by their phylogenetic proximity in the kinome space or differences in the interactions with the variable binding site residues. This approach provides a fruitful functional linkage between structural bioinformatics analysis and disease by unravelling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.
Associate Editor: Prof. Anna Tramontano
Received on April 5, 2007; revised on April 5, 2007; accepted on May 16, 2007
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