Estimation of Potentials of Mean Force from Nonequilibrium Pulling Simulations Using Both Minh-Adib Estimator and Weighted Histogram Analysis Method
Bibliographic record
Abstract
Nonequilibrium pulling simulations have been a useful approach for investigating a variety of physical and biological problems. The major target in the simulations is to reconstruct reliable potentials of mean force (PMFs) or unperturbed free-energy profiles for quantitatively addressing both equilibrium mechanistic properties and contributions from nonequilibrium processes. While several current nonequilibrium methods were shown to be accurate in computing free-energy profiles in systems with relatively simple dynamics, they have proved to be unsuitable in more complicated systems. To extend the applicability of nonequilibrium sampling, we demonstrate a novel method that combines Minh-Adib's bidirectional estimator with nonlinear WHAM equations to reconstruct and assess PMFs from relatively fast pulling trajectories. We test the method in a one-dimensional model system and in a system of an antibiotic gramicidin-A (gA) channel, which is considered a significant challenge for nonequilibrium sampling. We identify key parameters for efficiently performing pulling simulations to improve and ensure the convergence and accuracy of estimated PMFs. We show that a few pulling trajectories of a relatively fast pulling speed v = 10 Å/ns can return a fair estimate of the PMF of a single potassium ion in gA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".