Benchmarking Rapid TLES Simulations of Gas Diffusion in Proteins: Mapping O<sub>2</sub> Migration and Escape in Myoglobin as a Case Study
Bibliographic record
Abstract
Standard molecular dynamics (MD) simulations of gas diffusion consume considerable computational time and resources even for small proteins. To combat this, temperature-controlled locally enhanced sampling (TLES) examines multiple diffusion trajectories per simulation by accommodating multiple noninteracting copies of a gas molecule that diffuse independently, while the protein and water molecules experience an average interaction from all copies. Furthermore, gas migration within a protein matrix can be accelerated without altering protein dynamics by increasing the effective temperature of the TLES copies. These features of TLES enable rapid simulations of gas diffusion within a protein matrix at significantly reduced (∼98%) computational cost. However, the results of TLES and standard MD simulations have not been systematically compared, which limits the adoption of the TLES approach. We address this drawback here by benchmarking TLES against standard MD in the simulation of O2 diffusion in myoglobin (Mb) as a case study since this model system has been extensively characterized. We find that 2 ns TLES and 108 ns standard simulations map the same network of diffusion tunnels in Mb and uncover the same docking sites, barriers, and escape portals. We further discuss the influence of simulation time as well as the number of independent simulations on the O2 population density within the diffusion tunnels and on the sampling of Mb's conformational space as revealed by principal component analysis. Overall, our comprehensive benchmarking reveals that TLES is an appropriate and robust tool for the rapid mapping of gas diffusion in proteins when the kinetic data provided by standard MD are not required. Furthermore, TLES provides explicit ligand diffusion pathways, unlike most rapid methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".