Structural mapping of patient-associated KCNMA1 gene variants

Area of Science:

• Neurogenetics
• Molecular Biology
• Biophysics

Background:

• KCNMA1-linked channelopathy causes neurological issues due to altered BK K+ channel activity.
• Functional studies and structural localization of patient-associated KCNMA1 variants are incomplete.
• Systematic assessment of variant pathogenicity and structural impact is needed.

Purpose of the Study:

• Map 82 nonsynonymous KCNMA1 variants within the BK channel protein structure.
• Identify functional clusters (gain-of-function/loss-of-function) and assess variants of uncertain significance (VUSs).
• Develop and validate an ensemble algorithm (KMS) for improved pathogenicity prediction.

Main Methods:

• Structural mapping of KCNMA1 variants using cryoelectron microscopy data.
• Functional classification (GOF/LOF) of variants via electrophysiology.
• Development of a KCNMA1 meta score (KMS) integrating structural and algorithmic predictions.
• Comparison of KMS with existing algorithms (e.g., REVEL) and experimental validation.

Main Results:

• Fifty-three variants mapped structurally, with 21 functionally classified (GOF/LOF).
• LOF variants clustered in functional domains (pore, AC, Ca2+ bowl); GOF variants showed no clustering.
• The KMS algorithm identified novel pathogenic variants (M578T, E656A, D965V) missed by REVEL.
• KMS and REVEL showed discrepancies for 10 VUS residues, highlighting the need for improved prediction.

Conclusions:

• Disease-associated KCNMA1 variants are distributed within critical BK channel functional domains.
• The developed KMS ensemble algorithm enhances pathogenicity prediction for VUSs.
• Integrating structural and functional data improves prediction accuracy for KCNMA1 channelopathies.