Bridge: A Graph-Based Algorithm to Analyze Dynamic H-Bond Networks in Membrane Proteins
Bibliographic record
Abstract
Membrane proteins that function as transporters or receptors must communicate with both sides of the lipid bilayer in which they sit. This long distance communication enables transporters to move protons or other ions and small molecules across the bilayer and receptors to transmit an external signal to the cell. Hydrogen bonds, hydrogen-bond networks, and lipid-protein interactions are essential for the motions and functioning of the membrane protein and, consequently, of outmost interest to structural biology and numerical simulations. We present here Bridge, an algorithm tailored for efficient analyses of hydrogen-bond networks in membrane transporter and receptor proteins. For channelrhodopsin, a membrane protein whose functioning involves proton-transfer reactions, Bridge identifies extensive networks of protein-water hydrogen bonds and an unanticipated network that can bridge transiently two proton donors across a distance of ∼20 Å. Graphs of the protein hydrogen bonds reveal rapid propagation of structural changes within hydrogen-bond networks of mutant transporters and identify protein groups potentially important for the proton transfer activity. The algorithm is made available as a plugin for PyMol.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".