ArticleBMC bioinformatics2014
A graph theoretic approach to utilizing protein structure to identify 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 20 papers.
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Who cites it
20 citing papers in PubMed, 30 citations in OpenAlex.
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Faltwerk: a library for spatial exploratory data analysis of protein structures.Bioinformatics advances · 2023Article
- PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities.Cell systems · 2020Article
- A review of mathematical representations of biomolecular data.Physical chemistry chemical physics : PCCP · 2020Review
- MathDL: mathematical deep learning for D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Computational methods for detecting cancer hotspots.Computational and structural biotechnology journal · 2020Review
- eVIDENCE: a practical variant filtering for low-frequency variants detection in cell-free DNA.Scientific reports · 2019Article
- 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
- AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.Journal of chemical information and modeling · 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
- Comparison of algorithms for the detection of cancer drivers at subgene resolution.Nature methods · 2017Article
- Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression.PLoS computational biology · 2017Article
- NAPS: Network Analysis of Protein Structures.Nucleic acids research · 2016Article
- Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes.Scientific reports · 2016Article
- mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.Human mutation · 2016Article
- Leveraging protein quaternary structure to identify oncogenic driver mutations.BMC bioinformatics · 2016Article
- SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering.BMC bioinformatics · 2015Article
- Identifying overlapping mutated driver pathways by constructing gene networks in cancer.BMC bioinformatics · 2015Article
- A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.BMC bioinformatics · 2014Article
- Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach.Genome medicine · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIt is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of approaches have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach.
resultsWe have designed and implemented GraphPAC (Graph Protein Amino acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC discovers clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html.
conclusionGraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when 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 OpenQuestion graph.