Computational investigation of Betalain derivatives as natural inhibitor against food borne bacteria
Bibliographic record
Abstract
Natural organic pigments such as carotenoids, betalains, anthocyanins, and carminic acid are notably found as safer food preservatives compared to other harmful synthetic chemicals. Due to glycosylation and acylation, betalains exhibit a broad-spectrum antimicrobial functionality with protection against degenerative diseases. Thus, betalains have been investigated as a potential bacterial inhibitor for food preservative applications. Initially, 36 betalain derivatives have been taken for primary screening using molecular docking. Afterward, the top ten ligands are taken for further study and analysis. The results of Prediction of Activity Spectrum of Substances (PASS) assured the antibacterial capabilities of betalains, and Lipinski's rule-of-five ensures the acceptability of the selected ligands as antibacterial inhibitors. The bacterial pathogens, such as C. botulinum (3FIE), E. coli (2ZWK), and S. typhi (3UU2) are selected for molecular docking by these betalain pigments. Furthermore, ADMET investigations and QSAR studies are performed to check insights into the bacterial inhibition process. Most active and common binding sides were observed at GLY159, ASN165, and SER166 for C. botulinum, at ASP8, LYS40, and TRP50 for E. coli; and at ARG37, GLN5, and ARG74 for S. typhi. The present study clearly shows an excellent insight towards the invention of plant-based new organic inhibitors to face the challenges of bacterial-resistant common food preservatives.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".