Characterization of drug binding within the HCN1 channel pore
Bibliographic record
Abstract
Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels mediate rhythmic electrical activity of cardiac pacemaker cells, and in neurons play important roles in setting resting membrane potentials, dendritic integration, neuronal pacemaking, and establishing action potential threshold. Block of HCN channels slows the heart rate and is currently used to treat angina. However, HCN block also provides a promising approach to the treatment of neuronal disorders including epilepsy and neuropathic pain. While several molecules that block HCN channels have been identified, including clonidine and its derivative alinidine, lidocaine, mepivacaine, bupivacaine, ZD7288, ivabradine, zatebradine, and cilobradine, their low affinity and lack of specificity prevents wide-spread use. Different studies suggest that the binding sites of these inhibitors are located in the inner vestibule of HCN channels, but the molecular details of their binding remain unknown. We used computational docking experiments to assess the binding sites and mode of binding of these inhibitors against the recently solved atomic structure of human HCN1 channels, and a homology model of the open pore derived from a closely related CNG channel. We identify a possible hydrophobic groove in the pore cavity that plays an important role in conformationally restricting the location and orientation of drugs bound to the inner vestibule. Our results also help explain the molecular basis of the low-affinity binding of these inhibitors, paving the way for the development of higher affinity molecules.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".