Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures.

ABSTRACT: Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue-residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of p

SUBMITTER: Kumar S 

PROVIDER: S-EPMC6754584 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature