Kinact: a computational approach for predicting activating missense mutations in protein kinases
Open accessAbstract
Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.
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The graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability, dynamics and interactions with other molecules.
“Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [28]
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mCSM-PPI2: predicting the effects of mutations on protein–protein interactions
Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz383 · Cited by 369 · see citing works
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- Authors
- Rodrigues, C. H., Ascher, D. B., & Pires, D. E.
- Affiliations
- The University of Melbourne (AU)
- Fiocruz Minas (BR)
- Corresponding author
- Ascher, David B
- Pires, Douglas EV
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-21
- Volume
- 46
- Issue
- W1
- Pages
- W127-W132
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6031004
- OpenAlex
- W2803713584
- Cited by
- 31
- References
- 53
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 8 · 2019: 5
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc)
- Arca - Repositório Institucional da Fiocruz (submittedVersion)
- Funders
- Jack Brockhoff Foundation (JBF 4186)
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (MR/M026302/1)
- National Health and Medical Research Council (APP1072476)
- University of Melbourne (UOM0017)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein kinases, gain-of-function mutations, constitutive kinase activation, mutation prediction, protein phosphorylation, homology model, cancer driver mutations
- MeSH terms
- Machine Learning, Protein Kinases, Software, Computational Biology, Mutation, Missense, Internet, Structural Homology, Protein
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