Volumetric Physics of Polypeptide Coil–Helix Transitions
Bibliographic record
Abstract
Volumetric properties of proteins bear directly on their biological functions in hyperbaric environments and are useful in general as a biophysical probe. To gain insight into conformation-dependent protein volume, we developed an implicit-solvent atomic chain model that transparently embodies two physical origins of volume: (1) a fundamental geometric term capturing the van der Waals volume of the protein and the particulate, finite-size nature of the water molecules, modeled together by the volume encased by the protein’s molecular surface, and (2) a physicochemical term for other solvation effects, accounted for by empirical proportionality relationships between experimental partial molar volumes and solvent-accessible surface areas of model compounds. We tested this construct by Langevin dynamics simulations of a 16-residue polyalanine. The simulated trajectories indicate an average volume decrease of 1.73 ± 0.1 Å 3 /residue for coil–helix transition, ∼80% of which is caused by a decrease in geometric void/cavity volume, and a robust positive activation volume for helical hydrogen bond formation originating from the transient void created by an approaching donor–acceptor pair and nearby atoms. These findings are consistent with prior experiments with alanine-rich peptides and offer an atomistic analysis of the observed overall volume changes. The results suggest, in general, that hydrostatic pressure likely stabilizes helical conformations of short peptides but slows the process of helix formation. In contrast, hydrostatic pressure is more likely to destabilize natural globular proteins because of the void volume entrapped in their folded structures. The conceptual framework of our model thus affords a coherent physical rationalization for experiments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".