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Utilizing protein structure to identify non-random somatic mutations
Gregory A Ryslik1, Yuwei Cheng, Kei-Hoi Cheung
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu
Background:
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.
Results:
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: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html.
Conclusion:
Our algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.
Insights
We developed iPAC, an algorithm that uses protein 3D structure to find cancer-driving mutations. This method identifies new mutation clusters in known cancer genes and discovers clusters in previously unidentified proteins.
Area of Science:
- Genomics
- Proteomics
- Cancer Biology
Background:
- Human cancers arise from accumulated somatic mutations in oncogenes and tumor suppressors.
- Identifying key "driver" mutations in oncogenes is crucial for targeted cancer therapies.
- Current methods for detecting mutation clusters overlook protein spatial structures.
Purpose of the Study:
- To develop an algorithm that incorporates protein tertiary structure for enhanced identification of somatic mutation clusters.
- To improve the power of mutation detection methods by considering 3D protein architecture.
Main Methods:
- Developed the iPAC (identification of Protein Amino acid Clustering) algorithm.
- Integrated protein tertiary structure data with mutation databases (PDB, COSMIC).
- Applied the algorithm to identify non-random somatic mutation clusters.
Main Results:
- iPAC successfully identified novel mutation clusters in known cancer proteins like KRAS and PI3KCA.
- The algorithm detected mutation clusters in proteins not identified by existing methods, including EGFR and EIF2AK2.
- Demonstrated improved power in detecting mutation clustering by utilizing 3D protein structure.
Conclusions:
- The iPAC algorithm represents an advancement in identifying oncogenic driver mutations.
- Incorporating tertiary protein structure significantly enhances the detection of nonrandom somatic residue mutation clusters.
