Gene-specific machine learning model EpiPred identifies likely pathogenic variants in the epilepsy-related gene STXBP1.

STXBP1 variants are a frequent cause of early-onset developmental and epileptic encephalopathies and related neurodevelopmental disorders, but the clinical interpretation of these variants remains a major challenge. Most reported STXBP1 missense variants are classified as variants of uncertain significance (VUS), complicating diagnosis, counseling, and patient eligibility for precision therapies. Here, we developed EpiPred, a gene-specific machine learning classifier that predicts the pathogenicity of STXBP1 missense variants and tests these predictions using empirical evidence from well-established cellular assays. Trained on a curated set of pathogenic and benign variants, EpiPred outperformed global prediction tools in accuracy, sensitivity, and specificity. We validated the model's predictions using variant effect assays that measure protein abundance, solubility, stability, and interaction with the SNARE complex partner syntaxin 1. These biochemical readouts aligned closely with model outputs and enabled reclassification of several possibly misdiagnosed variants, which warrant further validation and clinical reevaluation. We deployed EpiPred in an interactive web application that allows clinicians, researchers, and patients to explore predictions for all possible STXBP1 missense variants. By identifying likely pathogenic STXBP1 variants, including those that may respond to emerging therapies such as protein stabilizers. By coupling gene-calibrated machine learning with orthogonal variant-effect assays and public deployment, EpiPred provides a transferable framework for VUS resolution, trial enrichment, and precision diagnosis across clinically actionable Mendelian disease genes.