Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases. (8th April 2014)

• Record Type: Journal Article
• Authors: Dixit, Anshuman; Verkhivker, Gennady M.
• Other Names: García-Sosa Alfonso T. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2014(2014)
• Publication Date: 2014-04-08
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2014/653487
• Languages: English
• ISSNs: 1748-670X

Abstract

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.