Computational protein stability analysis of SCN1A missense variants reveals domain-dependent stability patterns

Abstract

Objective

To determine whether computational protein-stability predictions discriminate pathogenic from benign SCN1A missense variants, and to characterize the structural distribution of predicted destabilization among pathogenic variants.

Methods

On an AlphaFold3-predicted Nav1.1 structure, FoldX, and Rosetta Cartesian ΔΔG were computed for a single ClinVar snapshot of pathogenic/likely-pathogenic (P/LP) and benign/likely-benign (B/LB) missense variants and its extension to ClinVar variants of uncertain significance (VUS) and gnomAD v4.1 variants; membrane-aware RosettaMP was applied to the patch-clamp subgroup. Pathogenic variants were stratified by functional domain.

Results

Pathogenic variants were more destabilizing than benign (FoldX 2.61 vs. 0.31 kcal/mol, p = 1.27 × 10⁻¹¹; ROC-AUC = 0.760), concordant with Rosetta (ROC-AUC = 0.697; ρ = 0.660). Destabilization was domain-dependent: pore (P-loop/selectivity-filter) pathogenic variants were depleted of stability-neutral variants (0.40-fold; Bonferroni-adjusted p = 4.3 × 10⁻⁷), whereas S4 voltage-sensor variants were enriched for them (2.32-fold; p = 0.013). Across ~3300 non-redundant variants, gnomAD-common variants resembled benign controls and VUS were intermediate (mean ΔΔG 0.98 kcal/mol; 18.5% strongly destabilizing), with the domain pattern preserved. Among 64 patch-clamp variants, stability did not separate gain- from loss-of-function, though gain-of-function variants clustered in voltage-sensing domains and were absent from the pore.

Significance

Computational stability analysis thus adds a mechanistic layer complementary to the conventional gating-dysfunction view, distinguishing a destabilized pore-region subset—for which proteostasis impairment is a candidate, though unproven, mechanism—from a structurally tolerated S4 subset whose pathogenicity is stability-independent. As a hypothesis-generating rather than mechanism-defining approach, this stratification prioritizes candidate variants—including the 18.5% of VUS that are strongly destabilizing—for direct functional and surface-expression validation in SCN1A-related epilepsies. 

Plain Language Summary

We used computational modeling to predict how thousands of SCN1A genetic variants influence the stability of the Nav1.1 sodium channel protein. Disease-causing variants tended to destabilize the protein more than benign variants, and variants in the pore region—where ions flow through the channel—were predominantly destabilizing. This is consistent with loss-of-function arising from misfolding and degradation of the channel protein in this subset of variants. By contrast, variants in the voltage-sensing region were often structurally tolerated, indicating that their disease-causing effects likely arise through a different mechanism that requires direct functional measurement to define. Accordingly, the analysis nominates a candidate pore-region subset potentially affected by proteostasis impairment and a complementary stability-neutral subset warranting functional evaluation.