title: Leveraging protein quaternary structure to identify oncogenic driver mutations.
abstract: 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 .
  
  
  CONCLUSIONS
  QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. gregory.ryslik@yale.edu.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Ryslik
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
authors.forename: Yuwei
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Department of Medicine, University of Cambridge, MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
authors.forename: Yorgo
authors.initials: Y
authors.lastname: Modis
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.|Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D009154:Mutation; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D020836:Protein Structure, Quaternary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D014584:User-Computer Interface
chemical_list: D011506:Proteins