Inhibition characteristics of peptide extracts of four medicinal plants on activities of bovine trypsin
Bibliographic record
Abstract
Aqueous extract of leaves of Momordica charantia, Hymenocardia acida, Lawsonia inermis and fruit of Xylopia aethiopica have been used in sub Saharan Africa in the management of many viral diseases. Their medicinal properties had been reported to be due to their high antioxidant activities, but limited information is available whether these properties are also due to inhibition or modulation of proteases important in the pathology of viral infections. We report the inhibitory characteristics of peptides extracted from these medicinal plants against bovine trypsin, a serine protease. Extraction of the peptides was done using standard procedure and their inhibitory activities were measured against bovine trypsin. Aqueous extract of M. charantia, H. acida, L. inermis and X. aethiopica contain 5.7±0.5 mg/ml, 1.0±0.2 mg/ml, 1.8±0.1 mg/ml and 28.3±4.1 mg/ml of peptides, respectively. Using Nα-benzoyl-DL-arginine 4-nitroanilide (BAPNA) as trypsin substrate, a Km and Vmax of 0.34 mM and 0.6 μmole/min/mg protein obtained, were altered in the presence of the peptide extracts suggesting the extracts modulate trypsin activity. The inhibition was either competitive, non-competitive or mixed-type of non-competitive inhibition. Inhibition constant (Ki) ranging from 81 to 831±120 μg/ml were obtained using Dixon plot with the peptide extract from M. charantia being the most potent. We concluded that the medicinal and antiviral properties of the extracts could also be due to inhibition or modulation of proteases involved in the pathology of viral infection.
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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".