ArticleNucleic acids research2018
Kinact: a computational approach for predicting activating missense mutations in protein kinases.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 33 citations in OpenAlex.
- Structure-conditioned self-supervised learning of residue interaction constraints in protein kinases for variant interpretation.Bioinformatics (Oxford, England) · 2026Article
- Discriminating activating, deactivating and resistance variants in protein kinases.Genome medicine · 2025Article
- A structure-based tool to interpret the significance of kinase mutations in clinical next generation sequencing in cancer.Frontiers in oncology · 2025Article
- piscesCSM: prediction of anticancer synergistic drug combinations.Journal of cheminformatics · 2024Article
- 3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors.Journal of cheminformatics · 2023Article
- Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.Cancer medicine · 2023Article
- Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.Cancers · 2023Review
- Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.Journal of molecular biology · 2022Review
- Evaluating hierarchical machine learning approaches to classify biological databases.Briefings in bioinformatics · 2022Article
- Predictive functional, statistical and structural analysis ofFrontiers in molecular biosciences · 2022Article
- mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.Nucleic acids research · 2021Article
- DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations.Protein science : a publication of the Protein Society · 2021Article
- mCSM-membrane: predicting the effects of mutations on transmembrane proteins.Nucleic acids research · 2020Article
- mmCSM-AB: guiding rational antibody engineering through multiple point mutations.Nucleic acids research · 2020Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
- dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction.Scientific reports · 2019Article
- mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.Nucleic acids research · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
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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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.