Leveraging Protein Quaternary Structure to Identify Oncogenic Driver Mutations
Ryslik, Gregory A
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Ryslik, G. A., Cheng, Y., Modis, Y., & Zhao, H. (2016). Leveraging Protein Quaternary Structure to Identify Oncogenic Driver Mutations. BMC Bioinformatics https://www.repository.cam.ac.uk/handle/1810/254159
Background: Identifying key "driver" mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign "passenger" mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research. Results: We have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/ html/QuartPAC.html. Conclusion: QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.
This work was supported in part by NSF Grant DMS 1106738 (GR, HZ); NIH Grants GM59507 and CA154295 (HZ), and GM102869 (YM); and Wellcome Trust Grant 101908/Z/13/Z (YM).
Wellcome Trust (101908/Z/13/Z)
National Institute of General Medical Sciences (NIGMS) (R01GM102869)
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