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Open Access Highly Accessed Methodology article

Utilizing protein structure to identify non-random somatic mutations

Gregory A Ryslik1*, Yuwei Cheng2, Kei-Hoi Cheung23, Yorgo Modis4 and Hongyu Zhao12*

Author Affiliations

1 Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

2 Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

3 , Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA

4 Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA

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BMC Bioinformatics 2013, 14:190  doi:10.1186/1471-2105-14-190

Published: 13 June 2013



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 an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering.


We have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking 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 PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: webcite.


Our algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.