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

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

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Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, 2010 Becker Drive, Lawrence, KS 66047, USA ; Department of Biotechnology, Institute of Life Sciences, Bhubaneswar, India.
School of Computational Sciences and Crean School of Health and Life Sciences, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USA ; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.


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.

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