Structure-functional prediction and analysis of cancer mutation effects in protein kinases

Comput Math Methods Med. 2014;2014:653487. doi: 10.1155/2014/653487. Epub 2014 Apr 8.

Abstract

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal "low" activity state to the "active" state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes.

MeSH terms

  • Algorithms
  • Amino Acid Motifs
  • Computational Biology / methods
  • Computer Simulation
  • Humans
  • Models, Molecular
  • Mutation*
  • Neoplasms / genetics*
  • Neoplasms / metabolism
  • Polymorphism, Single Nucleotide
  • Protein Kinases / genetics*
  • Software

Substances

  • Protein Kinases