Determining the degree of denaturation of bovine serum albumin using a new UV analysis technique
Bibliographic record
Abstract
There is a lack of fast and inexpensive analysis methods to study the conformational changes and the degree of denaturation of proteins quantitatively. As such, a novel analytical technique is developed based on the ultraviolet-visible (UV-Vis) absorption spectrum of proteins, and a mathematical modeling of the results. The phenomenon behind this technique is the shift of the absorption peak of amino acid residues of BSA such as tyrosine, phenylalanine, and tryptophan as the protein unfolds and these residues are exposed to the solvent. However, the portion of the peak that is shifted is miniscule and it can be enhanced by using the proposed technique in this paper. As an example, we also show how this technique was applied for evaluating the temperature effects on thermal denaturation of bovine serum albumin (BSA) protein. A degree of denaturation curve as a function of time was obtained at three different temperatures using this technique. The results are reproducible and consistent with those reported in the literature. This technique is especially recommended for analyses where several tests are needed quickly, and the amount of sample is limited. Among the applications, it can be used for evaluation of disinfection through assessing the degree of denaturation for pathogens proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".