A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.

ABSTRACT:

Background

Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical.

Results

We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data

SUBMITTER: Ryslik GA 

PROVIDER: S-EPMC4227039 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature