Comparative Molecular Profiling and Bioactivity Analysis of Algerian Propolis: Antioxidant, Antibacterial Activities, and In Silico NRF2-KEAP1 Pathway Modulation
Bibliographic record
Abstract
Propolis, a natural bee-derived product rich in diverse phytochemicals with potential therapeutic benefits, remains underexplored in Algeria. This study investigated the molecular profile, antioxidant capacity, and antibacterial activity of propolis sourced from two bioclimatically distinct Algerian regions (humid subtropical Batna and hot desert Biskra) using electrospray ionization mass spectrometry, ultra-high-performance liquid chromatography with diode array detection, and gas chromatography–mass spectrometry. Significant regional variations were observed, with propolis extract 2 (PE2) exhibiting a higher bioactive content, including a constituent not previously reported in propolis. Antioxidant assays (2,2-diphenyl-1-picrylhydrazyl, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid), ferric reducing antioxidant power, and phenanthroline) demonstrated that PE2 consistently outperformed propolis extract 1 and the reference standards (DPPH IC50: 27.74 µg/mL; FRAP: 5.16 µg/mL). Antibacterial testing demonstrated potent bactericidal effects, particularly for PE2, with minimum inhibitory concentration values equivalent to the minimum bactericidal concentrations required against Staphylococcus aureus ATCC 25923 (18.75 µg/mL) and Escherichia coli ATCC 25922 (133 µg/mL). Molecular docking identified nine bioactive compounds with high KEAP1 binding affinity, with 1,3-O-caffeoyl-dihydrocaffeoylglycerol (first time reported in propolis) showing the strongest binding affinity (−11.02 Kcal/mol). In silico pharmacokinetic predictions further verified its drug-like properties. These findings suggest the tested Algerian propolis samples, as a source of natural alternative antioxidants and antimicrobials, provide a basis for future research in drug discovery and development.
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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 | 0.000 |
| 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".