Two different regimes in alcohol-induced coil–helix transition: effects of 2,2,2-trifluoroethanol on proteins being either independent of or enhanced by solvent structural fluctuations
Bibliographic record
Abstract
Inhomogeneous distribution of constituent molecules in a mixed solvent has been known to give remarkable effects on the solute, e.g., conformational changes of biomolecules in an alcohol-water mixture. We investigated the general effects of 2,2,2-trifluoroethanol (TFE) on proteins/peptides in a mixture of water and TFE using melittin as a model protein. Fluctuations and Kirkwood-Buff integrals (KBIs) in the TFE-H2O mixture, quantitative descriptions of inhomogeneity, were determined by small-angle X-ray scattering investigation and compared with those in the aqueous solutions of other alcohols. The concentration fluctuation for the mixtures ranks as methanol < ethanol ≪ TFE < tert-butanol < 1-propanol, indicating that the inhomogeneity of molecular distribution in the TFE-H2O mixture is unexpectedly comparable to those in the series of mono-ols. On the basis of the concentration dependence of KBIs between the TFE molecules, it was found that a strong attraction between the TFE molecules is not necessarily important to induce helix conformation, which is inconsistent with the previously proposed mechanism. To address this issue, by combining the KBIs and the helix contents reported by the experimental spectroscopic studies, we quantitatively evaluated the change in the preferential binding parameter of TFE to melittin attributed to the coil-helix transition. As a result, we found two different regimes on TFE-induced helix formation. In the dilute concentration region of TFE below ∼2 M, where the TFE molecules are not aggregated among themselves, the excess preferential binding of TFE to the helix occurs due to the direct interaction between them, namely independent of the solvent fluctuation. In the higher concentration region above ∼2 M, in addition to the former effect, the excess preferential binding is significantly enhanced by the solvent fluctuation. This scheme should be held as general cosolvent effects of TFE on proteins/peptides.
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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 | 0.000 |
| 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 | 0.001 |
| 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".