Assessment of Common Simulation Protocols for Simulations of Nanopores, Membrane Proteins, and Channels
Bibliographic record
Abstract
Molecular dynamics (MD) simulation has become a common technique to study biological systems. Transport of small molecules through carbon nanotubes and membrane proteins has been an intensely studied topic, and MD simulations have been able to provide valuable predictions, many of which have later been experimentally proven. Simulations of such systems pose challenges, and unexpected problems in commonly used protocols and methods have been found in the past few years. The two main reasons why some were not found before are that most of these newly discovered errors do not lead to unstable simulations. Furthermore, some of them manifest themselves only after relatively long simulation times. We assessed the reliability of the most common simulations protocols by MD and stochastic dynamics (SD) or Langevin dynamics, simulations of an alpha hemolysin nanochannel embedded in a palmitoyloleoylphosphatidylcholine (POPC) lipid bilayer. Our findings are that (a) reaction field electrostatics should not be used in simulations of such systems, (b) local thermostats should be preferred over global ones since the latter may lead to an unphysical temperature distribution,
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".