Structural refinement of the hERG1 pore and voltage‐sensing domains with ROSETTA‐membrane and molecular dynamics simulations
Bibliographic record
Abstract
The hERG1 gene (Kv11.1) encodes a voltage-gated potassium channel. Mutations in this gene lead to one form of the Long QT Syndrome (LQTS) in humans. Promiscuous binding of drugs to hERG1 is known to alter the structure/function of the channel leading to an acquired form of the LQTS. Expectably, creation and validation of reliable 3D model of the channel have been a key target in molecular cardiology and pharmacology for the last decade. Although many models were built, they all were limited to pore domain. In this work, a full model of the hERG1 channel is developed which includes all transmembrane segments. We tested a template-driven de-novo design with ROSETTA-membrane modeling using side-chain placements optimized by subsequent molecular dynamics (MD) simulations. Although backbone templates for the homology modeled parts of the pore and voltage sensors were based on the available structures of KvAP, Kv1.2 and Kv1.2-Kv2.1 chimera channels, the missing parts are modeled de-novo. The impact of several alignments on the structure of the S4 helix in the voltage-sensing domain was also tested. Herein, final models are evaluated for consistency to the reported structural elements discovered mainly on the basis of mutagenesis and electrophysiology. These structural elements include salt bridges and close contacts in the voltage-sensor domain; and the topology of the extracellular S5-pore linker compared with that established by toxin foot-printing and nuclear magnetic resonance studies. Implications of the refined hERG1 model to binding of blockers and channels activators (potent new ligands for channel activations) are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".