title: Exploring sequence-structure relationships in the tyrosine kinome space: functional classification of the binding specificity mechanisms for cancer therapeutics.
abstract: BACKGROUND
  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 than 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 unraveling the molecular basis of kinase selectivity for the prominent kinase drugs (Imatinib, Dasatinib and Erlotinib) which is consistent with structural and proteomics experiments.
authors.affiliation: Department of Pharmaceutical Chemistry, School of Pharmacy, Center for Bioinformatics, The University of Kansas, Lawrence, KS 66047-1620, USA. verk@ku.edu
authors.forename: Gennady M
authors.initials: GM
authors.lastname: Verkhivker
mesh_terms: D000595:Amino Acid Sequence; D000970:Antineoplastic Agents / Q000737:chemistry / Q000627:therapeutic use; D001665:Binding Sites; D003198:Computer Simulation; D016503:Drug Delivery Systems / Q000379:methods; D006801:Humans; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009363:Neoplasm Proteins / Q000737:chemistry / Q000145:classification; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D020543:Proteome / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D000970:Antineoplastic Agents; D009363:Neoplasm Proteins; D020543:Proteome; D011505:Protein-Tyrosine Kinases