Effects of temperature and <scp>pH</scp> on the structure of a metalloprotease from <i>Lactobacillus fermentum</i><scp>R6</scp> isolated from <scp>H</scp>arbin dry sausages and molecular docking between protease and meat protein
Bibliographic record
Abstract
BACKGROUND: Microbial protease can interact with meat protein in fermented meat products at a certain pH and temperature. To investigate the effects of various pH values and temperatures on the structural characteristics of Lactobacillus fermentum R6 protease, which was isolated from Harbin dry sausages, spectroscopy techniques and molecular dynamics were utilized to evaluate structural changes. RESULTS: The protease exhibited a stable spatial structure at pH 7 and 40 °C, and the extension of the protease structure was also promoted. Although the structure of the protease could be changed or destroyed by pH 8 and 70 °C, it was mainly determined by the changes of secondary and tertiary structures such as α-helix, β-sheet, β-turn and random coil. In addition, carbonyl vibration, -NH vibration, C-H stretching vibration and disulphide bonds were present in L. fermentum R6 protease under various pH and temperature conditions. Molecular docking showed that the protease can interact with myosin light chain, myosin heavy chain, actin and myoglobin. CONCLUSION: The protease can maintain stable structure and interact with meat protein, which reflected certain application prospects in the fermentation of Harbin dry sausages. © 2021 Society of Chemical Industry.
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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 | 0.000 |
| 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".