Computational Proteomics of Biomolecular Interactions in Sequence and Structure Space of the Tyrosine Kinome: Evolutionary Constraints and Protein Conformational Selection Determine Binding Signatures of Cancer Drugs

  • Gennady M. Verkhivker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4578)

Abstract

The emerging insights into kinase function and evolution combined with a rapidly growing number of crystal structures of protein kinases complexes have facilitated a comprehensive structural bioinformatics analysis of sequence–structure relationships in determining the binding function of protein tyrosine kinases. We have found that evolutionary signal derived solely from the tyrosine kinase sequence conservation can not be readily translated into the ligand binding phenotype. However, fingerprinting ligand–protein interactions using in silico profiling of inhibitor binding against protein tyrosine kinases crystal structures can detect a functionally relevant kinase binding signal and reconcile the existing experimental data. In silico proteomics analysis unravels mechanisms by which structural plasticity of the tyrosine kinases is linked with the conformational preferences of cancer drugs Imatinib and Dasatinib in achieving effective drug binding with a distinct spectrum of the tyrosine kinome. While Imatinib binding is highly sensitive to the activation state of the enzyme, the computed binding profile of Dasatinib is remarkably tolerant to the conformational state of ABL. A comprehensive study of evolutionary, structural, dynamic and energetic aspects of tyrosine kinases binding with clinically important class of inhibitors provides important insights into mechanisms of sequence–structure relationships in the kinome space and molecular basis of functional adaptability towards specific binding.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gennady M. Verkhivker
    • 1
    • 2
  1. 1.Department of Pharmaceutical Chemistry, School of Pharmacy, and Center for Bioinformatics, The University of Kansas, 2030 Becker Drive, Lawrence, KS 66047USA
  2. 2.Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, La Jolla CA 92093-0636USA

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