Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation
Bibliographic record
Abstract
Missense mutations compromise protein fitness by altering stability and function, which can lead to various clinical disease states. The potassium ion channel KCNQ1 underlies the majority of congenital long QT syndrome (LQTS) cases, one of the most common genetic arrhythmia syndromes. During genetic testing for LQTS, variants of uncertain significance (VUS) confound diagnosis and clinical management. KCNQ1 protein fitness metrics enable mechanistic classification of variants, directly informing the molecular basis for dysfunction and providing clinical interpretation of variants linked to LQTS and other channelopathies. We developed structure-aware random forest classifier models to predict seven metrics of KCNQ1 fitness, four functional electrophysiology measurements (peak current density, voltage-dependence, gating kinetics), and three trafficking values measured by flow cytometry. Our trained models outperformed AlphaMissense in predicting protein fitness, enhancing interpretation of ClinVar VUS and variants classified as ambiguous by AlphaMissense. We demonstrate the classifiers distinguish benign and pathogenic variants from ClinVar and gnomAD and identify systematic patterns of dysfunction and mistrafficking along the functionally critical S4 helix. Our method advances variant effect prediction with a mechanistic classifier that reliably links missense mutations in KCNQ1 to their specific disease-causing mechanisms. As a resource for precision medicine approaches for LQTS or other KCNQ1 channelopathies, we provide the predictions and scores for all KCNQ1 missense variants across the structured region of the protein.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".