Physicochemical characterisation, molecular docking, and drug-likeness evaluation of hypotensive peptides encrypted in flaxseed proteome
Bibliographic record
Abstract
In this study, hypotensive peptides derived from mature flaxseed protein sequences were predicted in silico using BIOPEP-UWM with nine proteases, three each from digestive, plant and microbial sources. The physicochemical properties of 2256 ACE-inhibitory peptides and 267 renin-inhibitory peptides (including seven (7) peptides with dual inhibitory activities against both ACE and renin enzymes) were assessed in silico using the ‘Peptides’ package of R. The hypotensive peptides showed relatively low molecular weight (mol. wt.) range (132 = mol. wt. ≤ 442 Da); broad range of isoelectric point (3.61 = pI ≤ 12.50); both high (>2) and low (≤2) Boman indices, and a variety of hydrophobicity indices (hydrophilic, hydrophobic and amphipathic properties). Following this, the seven peptides with dual ACE and renin inhibitory activities were selected for molecular docking with the respective enzyme receptors. The binding energies of the seven hypotensive peptides with ACE and renin respectively ranged from −36.82 to −25.94 kJ/mol, and −33.05 to −27.61 kJ/mol; and compared well with values recorded for inhibitor drugs, captopril (−26.78 kJ/mol) and aliskiren (−34.73 kJ/mol). The seven peptides inhibited ACE through hydrogen bonds, electrostatic and hydrophobic interactions; and renin, mainly through hydrogen bonds and hydrophobic interactions. In silico prediction of adsorption, digestion, metabolism, excretion and toxicity (ADME/Tox) profile based on physicochemical properties and Lipinski's rule-of-five showed that the peptides were non-toxic and had desirable drug-like properties (flexibility, lipophilicity, molecular weight, gastrointestinal absorption, and bioavailability). This study provides insight into the molecular interactions of hypotensive peptides with their physiological targets, and the potential to develop the bioactive peptides from flaxseed proteins.
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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) | 0.001 | 0.001 |
| 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".