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Updated: Jul 15, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Mapping Structural Distribution and Gating-Property Impacts of Disease-Associated Missense Mutations in Voltage-Gated
Amin Akbari Ahangar1, Eslam Elhanafy1, Hayden Blanton1
1Department of Biomolecular Sciences, School of Pharmacy, University of Mississippi.
Abstract:
Thousands of voltage-gated sodium (Nav) channel variants contribute to a variety of disorders, including epilepsy, autism, cardiac arrhythmia, and pain disorders. Yet variant effects of more mutations remain unclear. The conventional gain-of-function (GoF) or loss-of-function (LoF) classifications is frequently employed to interpret of variant effects on function and guide precision therapy for sodium channelopathies. Our study challenges this binary classification by analyzing 525 mutations associated with 34 diseases across 366 electrophysiology studies, revealing that diseases with similar phenotypic effects can stem from unique molecular mechanisms. Our results show a high biophysical agreement (86%) between homologous disease-associated variants in different Nav genes, significantly surpassing the 60% phenotype (GoFo/LoFo) agreement among homologous mutants, suggesting the need for more nuanced disease categorization and treatment based on specific gating-property changes. Using UniProt data, we mapped over 2,400 disease-associated missense variants across nine human Nav channels and identified three clusters of mutation hotspots. Our findings indicate that mutations near the selectivity filter generally diminish the maximal current amplitude, while those in the fast inactivation region lean towards a depolarizing shift in half-inactivation voltage in steady-state activation, and mutations in the activation gate commonly enhance persistent current. In contrast to mutations in the PD, those within the VSD exhibit diverse impacts and subtle preferences on channel activity. This study shows great potential to enhance prediction accuracy for variant effects based on the structural context, laying the groundwork for targeted drug design in precision medicine.
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