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Updated: Jan 13, 2026
Identification of Kinase-substrate Pairs Using High Throughput Screening
Published on: August 29, 2015
Discriminating activating, deactivating and resistance variants in protein kinases
Gurdeep Singh1, Torsten Schmenger1, Juan Carlos Gonzalez-Sanchez1
1BioQuant & Biochemistry Center, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, 69121, Germany.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
Insights
Identifying kinase variants that increase protein function is key for personalized medicine. Our new computational method accurately predicts activating, deactivating, and resistance variants, aiding clinical decisions.
Area of Science:
- Genomics
- Computational Biology
- Biochemistry
Background:
- Distinguishing gain-of-function (GOF) from loss-of-function (LOF) genetic variants in protein kinases is crucial for clinical genetics and personalized medicine.
- GOF variants, unlike LOF variants, are often treatable with inhibitors, highlighting the need for precise variant classification.
- Current computational tools primarily predict variant pathogenicity (damaging vs. benign) without providing specific functional insights.
Purpose of the Study:
- To develop and validate a data-driven computational approach for differentiating kinase variants into activating (GOF), deactivating (LOF), and resistance types.
- To provide functional insights into genetic variants beyond simple pathogenicity prediction.
- To facilitate the identification of therapeutically targetable kinase variants.
Main Methods:
- Curated a dataset of 2505 variants across 441 kinases, categorized as activating, deactivating, resistance, or neutral.
- Utilized sequence, evolutionary, and structural features to train machine learning models for variant classification.
- Achieved high predictive performance with a mean Area Under the Curve (AUC) of 0.941.
- Experimentally validated predictions using cell-based assays (over-expression, gene expression, biochemical tests).
Main Results:
- Observed significant enrichment of activating mutations in cancer genomes and deactivating variants in hereditary diseases.
- Experimentally validated predicted activating variants in cancer samples, observing increased kinase activity.
- Demonstrated experimentally that a predicted variant in MAP2K3 caused reduced mitochondrial function, contrary to the effect of deletions.
- Developed an online application for analyzing kinase-domain variants and exploring known nearby variants.
Conclusions:
- The developed predictors, combined with rapid experimental validation, offer a feasible strategy for timely identification of activating kinase variants.
- This approach can significantly aid in making clinical decisions for patients with kinase-related diseases.
- The method advances the potential for personalized medicine by enabling rapid functional characterization of genetic variants.
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