Effective Simulations of Gas Diffusion Through Kinetically Accessible Tunnels in Multisubunit Proteins: O<sub>2</sub> Pathways and Escape Routes in T-state Deoxyhemoglobin
Bibliographic record
Abstract
The diffusion of small gases to special binding sites within polypeptide matrices pivotally defines the biochemical specificity and reactivity of proteins. We investigate here explicit O(2) diffusion in adult human hemoglobin (HbA) as a case study employing the recently developed temperature-controlled locally enhanced sampling (TLES) method and vary the parameters to greatly increase the simulation efficiency. The method is carefully validated against standard molecular dynamics (MD) simulations and available experimental structural and kinetic data on ligand diffusion in T-state deoxyHbA. The methodology provides a viable alternative approach to traditional MD simulations and/or potential of mean force calculations for: (i) characterizing kinetically accessible diffusion tunnels and escape routes for light ligands in porous proteins; (ii) very large systems when realistic simulations require the inclusion of multiple subunits of a protein; and (iii) proteins that access short-lived conformations relative to the simulation time. In the case of T-state deoxyHbA, we find distinct ligand diffusion tunnels consistent with the experimentally observed disparate Xe cavities in the α- and β-subunits. We identify two distal barriers including the distal histidine (E7) that control access to the heme. The multiple escape routes uncovered by our simulations call for a review of the current popular hypothesis on ligand escape from hemoglobin. Larger deviations from the crystal structure during simulated diffusion in isolated α- and β-subunits highlight the dampening effects of subunit interactions and the importance of including all subunits of multisubunit proteins to map realistic kinetically accessible diffusion tunnels and escape routes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".