Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases

الملخص EN

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.