A Coarse-Grained Simulation Approach for Protein Molecular Conformation Dynamics
Bibliographic record
Abstract
Coarse-grained molecular dynamics simulation is widely accepted for assessment of a large complex biological system, but it may also lead to a misleading conclusion. The challenge is to simulate protein structural dynamics (such as folding-unfolding behavior) due to the lack of a necessary backbone flexibility. This study developed a standard coarse-grained model directly from the protein atomic structure and amino acid coarse-grained FF (such as MARTINI FF v2.2). The atomic structure is used as a parent template to set up the coarse model, which naturally gives a better representation of the initial conditions. We have formulated a computational algorithm to set up protein coarse-grained coordinates and force field topology (such as bonds, angles, and dihedrals). The model was validated by a systematic all atom and coarse-grained simulation of a system containing protein human serum albumin and the drug paclitaxel in a water bath. The bonded force constants were optimized locally by neighboring residue-free energy data and globally by history matching against all atom simulation. The coarse-grained model was then applied for several other proteins and justified its general reliability for modeling protein conformations dynamics. We arrived at such a conclusion with great satisfaction because it describes the initial conditions accurately, applies only standard bonded force constants, and provides a significant backbone flexibility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".