Determination of Hansen solubility parameters of water-soluble proteins using UV–vis spectrophotometry
Bibliographic record
Abstract
Determination of solubility parameters by dissolution tests are difficult for some valuable molecules, such as proteins, where the quantities available are small, UV-vis spectroscopy can determine dissolved concentrations of even small amounts of material, but accurate determination of dissolution is difficult in relatively poor solvents, due to difficulty with constructing a reliable calibration curve. In this work we report a new simple procedure to determine the relative dissolution of proteins in various solvents using UV vis spectroscopy for the determination of Hansen solubility parameters (HSP) of proteins. This method allows qualitative determination of the amounts of BSA dissolved in various solvents. The amounts of BSA dissolved in each solvent, can then be used to rank solvents as good or bad for HSP calculation purpose, which gives more reliable ranking than observation alone in dissolution tests. To be able to evaluate the HSP of any solid material, the solubility of the tested material in a range of solvents needs to be determined. Solvents are then scored as good or bad based on observations and from the known properties of those solvents, the HSP of the solute molecule in question can be calculated. Here, bovine serum albumin (BSA) was used as a model protein, but the procedure reported here can be applied to any soluble protein or other macromolecule with a clear UV/vis adsorption peak. This procedure requires the tested material to be highly soluble in water, therefore eliminating the need for the preparation of many standard solutions of BSA in different solvents. Only a set of standard solutions of protein in water is required. UV-vis spectroscopy was used to analyze the remaining solid resuspended in water after centrifugation of solutions of BSA dissolved in other solvents to separate any undissolved protein. The HSP of BSA obtained via this procedure was compared to values previously obtained for BSA using other methods. A very good agreement between the HSP obtained in this work with the that reported by Houen et al. which used amino acid analysis for the estimation of the solubility of BSA in various organic solvents.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".