Density Functional Theory for Protein Transfer Free Energy
Bibliographic record
Abstract
We cast the problem of protein transfer free energy within the formalism of density functional theory (DFT), treating the protein as a source of external potential that acts upon the solvent. Solvent excluded volume, solvent-accessible surface area, and temperature dependence of the transfer free energy all emerge naturally within this formalism, and may be compared with simplified "back of the envelope" models, which are also developed here. Depletion contributions to osmolyte induced stability range from 5 to 10 kBT for typical protein lengths. The general DFT transfer theory developed here may be simplified to reproduce a Langmuir isotherm condensation mechanism on the protein surface in the limits of short-ranged interactions, and dilute solute. Extending the equation of state to higher solute densities results in non-monotonic behavior of the free energy driving protein or polymer collapse. Effective interaction potentials between protein backbone or side chains and TMAO are obtained, assuming a simple backbone/side chain two-bead model for the protein with an effective 6-12 potential with the osmolyte. The transfer free energy δg shows significant entropy: d(δg)/dT ≈ 20 kB for a 100-residue protein. The application of DFT to effective solvent forces for use in implicit-solvent molecular dynamics is also developed. The simplest DFT expressions for implicit-solvent forces contain both depletion interactions and an "impeded-solvation" repulsive force at larger distances.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".