ArticleBMC bioinformatics2011
Characterization of pathogenic germline mutations in human protein kinases.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Structure-conditioned self-supervised learning of residue interaction constraints in protein kinases for variant interpretation.Bioinformatics (Oxford, England) · 2026Article
- How Far Are We from the Rapid Prediction of Drug Resistance Arising Due to Kinase Mutations?ACS omega · 2021Article
- KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily.BMC genomics · 2016Article
- Prediction and prioritization of rare oncogenic mutations in the cancer Kinome using novel features and multiple classifiers.PLoS computational biology · 2014Article
- wKinMut: an integrated tool for the analysis and interpretation of mutations in human protein kinases.BMC bioinformatics · 2013Article
- Emerging methods in protein co-evolution.Nature reviews. Genetics · 2013Review
- Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.Nucleic acids research · 2013Article
- Prioritization of pathogenic mutations in the protein kinase superfamily.BMC genomics · 2012Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.
resultsPathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.
conclusionsDisease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.
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