Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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<sup>3</sup>/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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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 it