UshEffect-3D: Structure-informed Classification of USH2A Missense Variants for Inherited Retinal Disease

Abstract

USH2A encodes Usherin, a 5,202-residue multi-domain protein whose missense variation is a major cause of Usher syndrome type II. Interpretation remains challenging because the protein lacks a full-length experimental structure and thousands of reported missense variants remain unresolved. In an attempt to address this gap, we developed UshEffect-3D, a gene-specific machine-learning-based variant effect predictor that integrates sequence-based evolutionary measures, structure-based biochemical properties, local interaction terms, and predicted protein stability changes to prioritise USH2A missense variants of unknown significance (VUS). To ensure that the structure-derived features were extracted from reliably modelled predicted structures, the AlphaFold2 models of 47 annotated Usherin domains were compared against experimental structures of representative domain types. The predicted structures showed strong fold concordance with representative experimental structures, with 43 domains achieving TM-scores of at least 0.5. Among binary classifiers trained using a residue position-grouped cross-validation strategy, Logistic Regression was selected as the most stable performer and achieved an MCC of 0.75, AUC of 0.96, sensitivity of 0.93 and specificity of 0.81 on a group-aware held-out test set. On a benchmarking task, UshEffect-3D performed comparably to six general-purpose variant-effect predictors evaluated on the held out test set, scoring the highest on every balanced metric but no statistically resolvable advantage over any major competitors such as PolyPhen-2, VESPAl, ESM-1b and AlphaMissense. Feature ablation displayed that sequence derived evolutionary constraints were the sole detectable source of discrimination - withholding the conservation features reduced cross-validated MCC by 0.35 (paired 95% CI -0.45 to -0.25), whereas removing structural descriptors and predicted stability change negligibly affected model performance. Applying our model to the 2,639 ClinVar VUS, we reclassified 981 (37.2%) as likely pathogenic highlighting the extent of disease-relevant missense variation yet to be uncovered. Thus, UshEffect-3D serves as an interpretable protein-specific framework and a supplementary ranked resource that can be used to flag unresolved variants for further clinical and experimental review.