Molecular simulation of multistate peptide dynamics: A comparison between microsecond timescale sampling and multiple shorter trajectories
Bibliographic record
Abstract
Molecular dynamics simulations of the RN24 peptide, which includes a diverse set of structurally heterogeneous states, are carried out in explicit solvent. Two approaches are employed and compared directly under identical simulation conditions. Specifically, we examine sampling by two individual long trajectories (microsecond timescale) and many shorter (MS) uncoupled trajectories. Statistical analysis of the structural properties indicates a qualitative agreement between these approaches. Microsecond timescale sampling gives large uncertainties on most structural metrics, while the shorter timescale of MS simulations results in slight structural memory for beta-structure starting states. Additionally, MS sampling detects numerous transitions on a relatively short timescale that are not observed in microsecond sampling, while long simulations allow for detection of a few transitions on significantly longer timescales. A correlation between the complex free energy landscape and the kinetics of the equilibrium is highlighted by principal component analysis on both simulation sets. This report highlights the increased precision of the MS approach when studying the kinetics of complex conformational change, while revealing the complementary insight and qualitative agreement offered by far fewer individual simulations on significantly longer timescales.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".