ArticleProceedings of the National Academy of Sciences of the United States of America2019
Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Prevalence of Cytoplasmic Actin Mutations in Diffuse Large B-Cell Lymphoma and Multiple Myeloma: A Functional Assessment Based on Actin Three-Dimensional Structures.International journal of molecular sciences · 2020Pooled it
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.Nature communications · 2025Article
- A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.bioRxiv : the preprint server for biology · 2024Article
- Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.Molecular cancer research : MCR · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors.Journal of cheminformatics · 2023Article
- Naturally mutagenic sequence diversity in a human type II topoisomerase.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Predicting functional effect of missense variants using graph attention neural networks.Nature machine intelligence · 2022Article
- Convergent Alterations of a Protein Hub Produce Divergent Effects within a Binding Site.ACS chemical biology · 2022Article
- Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes.Nucleic acids research · 2022Article
- Coarse-graining protein structures into their dynamic communities with DCI, a dynamic community identifier.Bioinformatics (Oxford, England) · 2022Article
- Known allosteric proteins have central roles in genetic disease.PLoS computational biology · 2022Article
- Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.PLoS biology · 2021Article
- Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- ZWINT: A potential therapeutic biomarker in patients with glioblastoma correlates with cell proliferation and invasion.Oncology reports · 2020Article
- Computational methods for detecting cancer hotspots.Computational and structural biotechnology journal · 2020Review
- Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: UsingComputational and structural biotechnology journal · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue-residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.
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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.