Implementation of the Forward–Reverse Method for Calculating the Potential of Mean Force Using a Dynamic Restraining Protocol
Bibliographic record
Abstract
We present a new sampling and analysis scheme for calculating the potential of mean force (PMF) of systems studied by steered molecular dynamics simulations. This scheme, which we call the bin-passing method, is based on the forward-reverse (FR) method (due to I. Kosztin and co-workers, Kosztin et al. J. Chem. Phys. 2006, 124(6), 064106) and arguments based on the second law of thermodynamics. Applying the bin-passing method results in enhanced sampling, better separation of the reversible and irreversible work distributions, and faster convergence to the underlying PMF of the system under study. Post-simulation analysis is performed using a purpose-built software that we have made publicly available at https://github.com/1particle/bin-passing_analyzer under the terms of the GNU General Public License (version 3). Three examples are provided, for systems of varying sizes and complexities, to demonstrate the efficiency of this method and the quality of the results: for the dissociation PMF of NaCl in water, the bin-passing method obtains PMFs in excellent agreement with that obtained for the same system and using the same force-field through static (equilibrium) methods. The bin-passing method gives a very symmetric PMF for passage of a single water molecule through a DPPC bilayer, and the resultant PMF leads to permeability values in better agreement with experiments than those obtained through previous simulation studies. Finally, we consider the interaction of the antimicrobial peptide HHC-36 with two model membranes and employ the bin-passing method to obtain the PMFs for peptide adsorption to the membranes. The characteristics of these PMFs are consistent with the qualities established for the HHC-36 peptide through in vivo and in vitro experiments, as a non-toxic strong antimicrobial agent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".