# SPARC: a structural pathogenicity algorithm for risk classification of hERG variants

## TL;DR

This paper introduces SPARC, a tool that uses structural metrics to classify the risk of hERG gene mutations causing heart rhythm disorders.

## Contribution

SPARC is a novel semi-automated in silico pipeline that integrates structural metrics to predict hERG variant pathogenicity.

## Key findings

- SPARC identified 260 high-risk hERG variants from 1727 total variants with a score ≥3.25.
- Functional validation confirmed SPARC's predictions, including for variants of uncertain significance.
- SPARC outperformed existing tools like Alpha Missense and Revel in classifying pathogenicity.

## Abstract

Inherited mutations in the KCNH2 gene, which encodes the cardiac hERG potassium channel, are major contributors to arrhythmogenic syndromes such as long QT and short QT syndromes. However, clinical interpretation of the growing number of missense variants – many of which are classified as variants of uncertain significance (VUS) – remains a pressing challenge. Here, we present a semi-automated in silico pipeline for predicting hERG variant pathogenicity, acting as a binary classifier and integrating five structural metrics – residue volume, hydrophobicity, charge, steric clashes, and proximity to pathogenic hotspots – into a composite structural pathogenicity score (SPS) scaled from 1 to 5. Applied to 1727 hERG variants from ClinVar and from a French nationwide cohort, this binary classifier, termed SPARC, identified 260 variants as high risk of pathogenicity with SPS ≥3.25, of which a representative subset from the French cohort was functionally validated using high-throughput automated patch-clamp. Functional phenotyping confirmed the structural predictions, including for several VUS, demonstrating that comprehensive structural scoring can reliably stratify variant pathogenicity. This approach, benchmarked with Alpha Missense and Revel, offers a superior scalable, cost-effective pre-screening tool to guide clinical variant interpretation and prioritization for experimental validation.

Graphical Abstract