Bioinformatics analysis of adhesin-binding potential and ADME/Tox profile of anti-Helicobacter pylori peptides derived from wheat germ proteins
Bibliographic record
Abstract
Anti-adhesive activity of wheat germ-derived peptides, which is considered as one of the promising strategies for preventing Helicobacter pylori infection, was investigated. The underlying mechanism of anti-adhesive action was due to peptides acting as receptor analogues and binding to H. pylori adhesin proteins. However, there is lack of information on the nature and strength of this molecular interaction as well as the participating species and drug-likeness of the food-derived bioactive peptides. In this study, the biostability and ADME/Tox (absorption, distribution, metabolism, excretion and toxicity) profile of the anti-adhesive peptides were analyzed using bioinformatic tools, and their binding potential to H. pylori 's adhesins estimated by molecular docking. Binding is facilitated by mostly hydrogen bonding and hydrophobic interaction occurring in the active site of the adhesin proteins with affinities ranging from -6.0 to -7.4 and -6.0 to -7.8 kcal/mol for BabA and SabA, respectively. The results indicate highly possible binding capabilities of the peptides to adhesin proteins. Out of 16 peptides studied, 14 bound in the vicinity of the active site of BabA and SabA whereas two different peptides demonstrated allosteric binding. The most hydrophobic peptide, P210 showed strong binding affinity for both BabA and SabA and, therefore, predicted to be the most promising peptide for further development in the prevention, management and treatment of H. pylori infection. The selected peptides were shown to be non-toxic, and to have high potential of localized effect of interfering with bacterial adherence. This work provides insights into the anti-adhesive mechanism of peptides and new evidence demonstrating bioactive peptides as promising nutraceutical candidates for preventing H. pylori infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".