Interactions of Glycine Betaine with Proteins: Insights from Volume and Compressibility Measurements
Bibliographic record
Abstract
We report the first application of volume and compressibility measurements to characterization of interactions between cosolvents (osmolytes) and globular proteins. Specifically, we measure the partial molar volumes and adiabatic compressibilities of cytochrome c, ribonuclease A, lysozyme, and ovalbumin in aqueous solutions of the stabilizing osmolyte glycine betaine (GB) at concentrations between 0 and 4 M. The fact that globular proteins do not undergo any conformational transitions in the presence of GB provides an opportunity to study the interactions of GB with proteins in their native states within the entire range of experimentally accessible GB concentrations. We analyze our resulting volumetric data within the framework of a statistical thermodynamic model in which each instance of GB interaction with a protein is viewed as a binding reaction that is accompanied by release of four water molecules. From this analysis, we calculate the association constants, k, as well as changes in volume, ΔV(0), and adiabatic compressibility, ΔK(S0), accompanying each GB-protein association event in an ideal solution. By comparing these parameters with similar characteristics determined for low-molecular weight analogues of proteins, we conclude that there are no significant cooperative effects involved in interactions of GB with any of the proteins studied in this work. We also evaluate the free energies of direct GB-protein interactions. The energetic properties of GB-protein association appear to scale with the size of the protein. For all proteins, the highly favorable change in free energy associated with direct protein-cosolvent interactions is nearly compensated by an unfavorable free energy of cavity formation (excluded volume effect), yielding a modestly unfavorable free energy for the transfer of a protein from water to a GB/water mixture.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".