A mesoscopic model for protein enzymatic dynamics in solution
Bibliographic record
Abstract
A multi-scale, coarse-grained description of protein conformational dynamics in a solvent is presented. The focus of the paper is on the description of the conformational motions that may accompany enzyme catalysis as the enzyme executes a catalytic cycle, starting with substrate binding and ending with product release and return to the original unbound enzyme. The protein is modeled by a network of beads representing amino acid residues, the solvent is described by multiparticle collision dynamics, and substrate binding and unbinding events are modeled stochastically by conformation-dependent transitions that modify the bonding in the network to correspond to the different binding states of the protein. The solvent dynamics is coupled to that of the protein and hydrodynamic interactions, which are important for the large-scale protein motions, are taken into account. The multi-scale model is used to study the dynamics of the adenylate kinase enzyme in solution. A potential function that describes the different binding and conformational states of the protein and accounts for partial unfolding during the catalytic cycle is constructed as a network built from elastic network and soft potential links. The conformational dynamics of the protein as it undergoes cyclic enzymatic dynamics, as well as its translational diffusion and orientational motion, are investigated using both multiparticle collision dynamics and dynamics that suppresses hydrodynamic coupling. Hydrodynamic interactions are found to have important effects on the large scale conformational motions of the protein and significantly affect the translational diffusion coefficients and orientational correlation times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".