Utilizing Protein Structure to Identify Non-Random Somatic Mutations

TL;DR

This paper introduces iPAC, a new algorithm that leverages 3D protein structures to more effectively identify non-random somatic mutation clusters in cancer-related proteins, surpassing existing methods.

Contribution

The novel iPAC algorithm incorporates protein tertiary structure to improve detection of mutation clusters, revealing new insights in cancer genomics.

Findings

Identifies new mutation clusters in known cancer proteins like KRAS and PI3KCa.

Detects clusters in proteins not identified by previous methods, such as EGFR.

Enhances understanding of mutation patterns by considering 3D protein structure.

Abstract

Motivation: Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key "driver" mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose a new methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering. Results: We have developed a novel algorithm, iPAC: identification of Protein Amino acid Clustering, for the identification of non-random somatic mutations in proteins that takes into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KCa. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology.