ArticleBMC bioinformatics2014
A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.Cell systems · 2020Article
- 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
- A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.Scientific reports · 2019Article
- Article
- Computational Approaches to Prioritize Cancer Driver Missense Mutations.International journal of molecular sciences · 2018Review
- Comparison of algorithms for the detection of cancer drivers at subgene resolution.Nature methods · 2017Article
- 3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets.Genome medicine · 2017Article
- Leveraging protein quaternary structure to identify oncogenic driver mutations.BMC bioinformatics · 2016Article
- DriverDBv2: a database for human cancer driver gene research.Nucleic acids research · 2016Article
- Comprehensive assessment of cancer missense mutation clustering in protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach.Genome medicine · 2014Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCurrent research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical.
resultsWe have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html.
conclusionSpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.