Abstract Purpose Variants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects. Methods We catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to a baseline of standard SVMs. Results MTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0.729 ± 0.029, AU-ROC 0.757 ± 0.039) over baseline (mean balanced accuracy 0.645 ± 0.041, AU-ROC 0.710 ± 0.074). We can obtain meaningful predictions even for channels with few known variants ( KCNC1, KCNQ5 ). Conclusion Our model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders.

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning