Mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.

ABSTRACT: A new algorithm and Web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole-genome and whole-exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes

SUBMITTER: Meyer MJ 

PROVIDER: S-EPMC4833594 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature