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

July 23, 2026Epilepsia OpenOpen Access

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Key Points

The aim is to evaluate if computational predictions can differentiate between pathogenic and benign SCN1A missense variants and to analyze structural destabilization patterns.
Utilized AlphaFold3-predicted Nav1.1 structure for stability calculations using FoldX and Rosetta.
Evaluated pathogenic/benign variants from ClinVar and gnomAD databases.
Applied membrane-aware RosettaMP to a patch-clamp subgroup and stratified variants by functional domain.
Enrichment of destabilizing S4 voltage-sensor variants (2.32-fold; p = 0.013).
Among ~3300 variants, gnomAD-common variants resembled benign controls, and 18.5% of VUS were identified as strongly destabilizing (mean ΔΔG 0.98 kcal/mol).
Stability analysis did not differentiate between gain- and loss-of-function variants within patch-clamp variants.

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Presents computational analysis distinguishing pathogenic from benign SCN1A variants, suggesting functional implications.

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Shim et al. (2026) studied this question.

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