Protein Transfer Free Energy Obeys Entropy-Enthalpy Compensation
Bibliographic record
Abstract
We have found significant entropy-enthalpy compensation for the transfer of a diverse set of two-state folding proteins from water into water containing a diverse set of cosolutes, including osmolytes, denaturants, and crowders. In extracting thermodynamic parameters from experimental data, we show the potential importance of accounting for the cosolute concentration-dependence of the heat capacity change upon unfolding, as well as the potential importance of the temperature-dependence of the heat capacity change upon unfolding. We introduce a new Monte Carlo method to estimate the experimental uncertainty in the thermodynamic data and use this to show by bootstrapping methods that entropy-enthalpy compensation is statistically significant, in spite of large, correlated scatter in the data. We show that plotting the data at the transition midpoint provides the most accurate experimental values by avoiding extrapolation errors due to uncertainty in the heat capacity, and that this representation exhibits the strongest evidence of compensation. Entropy-enthalpy compensation is still significant at lab temperature however. We also find that compensation is still significant when considering variations due to heat capacity models, as well as typical measurement discrepancies lab-to-lab when such data is available. Extracting transfer entropy and enthalpy along with their uncertainties can provide a valuable consistency check between experimental data and simulation models, which may involve tests of simulated unfolded ensembles and/or models of the transfer free energy; we include specific applications to cold shock protein and protein L.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".