Engineering Nutritious Proteins: Improvement of Stability in the Designer Protein MB-1 via Introduction of Disulfide Bridges
Bibliographic record
Abstract
Protein design is currently used for the creation of new proteins with desirable traits. In this laboratory the focus has been on the synthesis of proteins with high essential amino acid content having potential applications in animal nutrition. One of the limitations faced in this endeavor is achieving stable proteins despite a highly biased amino acid content. Reported here are the synthesis and characterization of two disulfide-bridged mutants derived from the MB-1 designer protein. Both mutants outperformed their parent protein MB-1 with their bridge formed, as shown by circular dichroism, size exclusion chromatography, thermal denaturation, and proteolytic degradation experiments. When the disulfide bridges were cleaved, the mutants' behavior changed: the mutants significantly unfolded, suggesting that the introduction of Cys residues was deleterious to MB-1-folding. In an attempt to compensate for the mutations used, a Tyr62-Trp mutation was performed, leading to an increase in bulk and hydrophobicity in the core. The Trp-containing disulfide-bridged mutants did not behave as well as the original MB-1Trp, suggesting that position 62 might not be adequate for a compensatory mutation.
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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) | 0.001 | 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) | 0.000 | 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".