Atomistic Simulations of Wimley–White Pentapeptides: Sampling of Structure and Dynamics in Solution
Bibliographic record
Abstract
Wimley-White pentapeptides (Ac-WLXLL) can be used as a model system to study lipid-protein interactions as they bind to lipid/water interfaces, like many antimicrobial peptides, and thermodynamic experimental data on their interactions with lipids are available, making them useful for both force field and method testing and development. Here we present a detailed simulation study of Wimley-White (WW) peptides in bulk water to investigate sampling, conformations, and differences due to the different X residue with an eye to future simulations at the lipid/water interface where sampling problems so far have hindered free energy calculations to reproduce the experimental thermodynamic data. We investigate the conformational preferences and slowest relaxation time of WW peptides in bulk water by building Markov State Models (MSM) from Molecular Dynamics (MD) simulation data. We show that clustering based on binning of backbone ϕ, ψ dihedrals in combination with the community detection algorithm of Blondel et al. provides a quick way of building MSM from large data sets. Our results show that in some cases, implied times even in these small peptides range from 224 to 547 ns. The implications of these slow transitions on determining the potential of mean force profiles of peptide interactions with a lipid bilayer are discussed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".