ArticleBMC bioinformatics2016
Leveraging protein quaternary structure to identify oncogenic driver mutations.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Convergent Alterations of a Protein Hub Produce Divergent Effects within a Binding Site.ACS chemical biology · 2022Article
- Computational methods for detecting cancer hotspots.Computational and structural biotechnology journal · 2020Review
- Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Functional characterization of 3D protein structures informed by human genetic diversity.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundIdentifying 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.
resultsWe 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 .
conclusionQuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.
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Registered trials
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