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| Home > Publications database > Discriminating activating, deactivating and resistance variants in protein kinases. |
| Journal Article | DKFZ-2025-02238 |
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2025
BioMed Central
London
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Please use a persistent id in citations: doi:10.1186/s13073-025-01564-z
Abstract: Distinguishing whether genetic variants in protein kinases cause gain or loss of function is critical in clinical genetics. In particular, gain (and not loss)-of-function variants are often immediately amenable to treatment by inhibitors, making their identification a potential boon to personalised medicine. Most existing computational methods for variant pathogenicity prediction simply distinguish damaging from benign variants and provide no further functional insights. Here, we present a data-driven approach that differentiates activating, deactivating, and resistance variants.To train and evaluate our method, we curated a dataset of 2505 variants (375 activating, 1028 deactivating, 98 resistance and 1004 neutral) across 441 kinases from the literature and public databases. Each variant was represented as a vector of sequence, evolutionary and structural features, which we then used to train machine learning models to distinguish among the four types of variants. The resulting predictors achieved excellent performance (mean AUC = 0.941). We tested a selection of variants by over-expression in T-REx-293 cells followed by gene expression or biochemical tests.Applying the predictors to uncharacterised variants, we observed a strong enrichment of activating mutations in cancer genomes, deactivating variants in hereditary disease, and few of either in variants from healthy individuals. We experimentally validated several predicted activating variants from cancer samples. For p.Ser97Asn in PIM1, phosphorylation events suggested increased activity. For p.Ala84Thr in MAP2K3, gene expression and mitochondrial staining showed a reduction in mitochondrial function, the opposite effect of MAP2K3 deletions. We provide an online application that enables users to analyse any kinase-domain variant, obtain prediction scores and explore known nearby variants in other kinases.Our predictors, together with the rapid experimental validations, demonstrates a feasible strategy for identifying activating variants in kinases in a time frame that would enable clinical decisions.
Keyword(s): Humans (MeSH) ; Protein Kinases: genetics (MeSH) ; Protein Kinases: metabolism (MeSH) ; Protein Kinases: chemistry (MeSH) ; Genetic Variation (MeSH) ; Mutation (MeSH) ; Computational Biology: methods (MeSH) ; Machine Learning (MeSH) ; Cancer genomics ; Gain-of-function ; Genetic variants ; Loss-of-function ; Machine learning ; Precision medicine ; Protein kinases ; Resistance ; Variant pathogenicity prediction ; Protein Kinases
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