Predicting the Damaging Potential of Uncharacterized KCNQ1 and KCNE1 Variants
Bibliographic record
Abstract
Voltage-gated potassium channels Kv7.1, encoded by the gene KCNQ1, play critical roles in various physiological processes. In cardiomyocytes, the complex Kv7.1-KCNE1 mediates the slow component of the delayed rectifier potassium current that is essential for the action potential repolarization. Over 1000 KCNQ1 missense variants, many of which are associated with long QT syndrome, are reported in ClinVar and other databases. However, over 600 variants are of uncertain clinical significance (VUS), have conflicting interpretations of pathogenicity, or lack germline information. Computational prediction of the damaging potential of such variants is important for the diagnostics and treatment of cardiac disease. Here, we collected 1750 benign and pathogenic missense variants of Kv channels from databases ClinVar, Humsavar, and Ensembl Variation and tested 26 bioinformatics tools in their ability to identify known pathogenic or likely pathogenic (P/LP) variants. The best-performing tool, AlphaMissense, predicted the pathogenicity of 195 VUSs in Kv7.1. Among these, 79 variants of 66 wildtype residues (WTRs) are also reported as P/LP variants in sequentially matching positions of at least one hKv7.1 paralogue. In available cryoEM structures of Kv7.1 with activated and deactivated voltage-sensing domains, 52 WTRs form intersegmental contacts with WTRs of ClinVar-listed variants, including 21 WTRs with P/LP variants. ClinPred and paralogue annotation methods consistently predicted that 21 WTRs of KCNE1 have 34 VUSs with damaging potential. Among these, 8 WTRs are contacting 23 Kv7.1 WTRs with 13 ClinVar-listed variants in the AlphaFold3 model. Analysis of intersegmental contacts in CryoEM and AlphaFold3 structures suggests atomic mechanisms of dysfunction for some VUSs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".