Computational Models for Understanding of Structure, Function and Pharmacology of the Cardiac Potassium Channel Kv11.1 (hERG)
Bibliographic record
Abstract
The rapid delayed rectifier current IKr is one of the major K+ currents involved in repolarization of the human cardiac action potential. Various inherited or drug-induced forms of the long QT syndrome (LQTS) in humans are linked to functional and structural modifications in the IKr conducting channels. IKr is carried by the potassium channel Kv11.1 encoded by the gene KCNH2 (commonly referred to as human ether-a-go-go-related gene or hERG) [1, 2]. The first necessary step for predicting emergent drug effects on the heart is determining and modeling the binding thermodynamics and kinetics of primary and major off-target drug interactions with subcellular targets. The bulk of drugs that target hERG channels are known to have complex interactions at the atomic scale. Accordingly, one of the goals for this review is to provide comprehensive guide in the universe of computational models aiming to refine our understanding of structure-function relations in Kv11.1 and its isoforms. The special emphasis is placed on the mapping of drug binding sites and tentative mechanisms of channel inhibition and activation by drugs. An overview over recent structural models and mapping of binding sites for blockers and activators of IKr current along with the discussion on agreements and discrepancies among different models is presented. There is an apparent reciprocity or feedback loop between drug binding and action potential of the cardiac myocytes. Thus one has to connect drug binding to a particular receptor so that its functional consequences impact on the action potential duration. The natural pathway is to develop multi-scale models that connect between receptor and cellular scales. The potential for such multi-scale model development is discussed through the lens of common gating models. Accordingly, the second part of this review covers an ongoing development of the kinetic models of gating transitions and cardiac ion currents carried by hERG channels with and without drug bound.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".