In silico assessment of self-assembly and bioactivity profiles of peptides from lentil protein
Bibliographic record
Abstract
Peptides are versatile biomolecules with diverse biological functions, including self-assembly into nanostructures such as nanoparticles, nanosheets, and nanotubes, which have applications in bioadhesives, hydrogels, and drug delivery systems. Lentil proteins, known for their antioxidant, antifungal, antihypertensive, and antidiabetic properties, are promising candidates for such biomaterial applications. This study aimed to computationally explore the self-assembly properties of lentil peptides derived from lentil protein-tannic acid (LPTA) interactions, previously identified through mass spectrometry. A database of 1,146 peptides was analyzed using peptide self-aggregation prediction, bioactivity profiling, physicochemical characterization, 3D structures modeling, and molecular dynamics simulations. The results identified 44 amyloid-like peptides, of which seven were non-toxic and poorly soluble, including peptide 1137 (YVIVNDSCYHQLVSHWLNT), peptide 494 (IVHIAKQL) and peptide 711 (LVVVPQNF). These peptides demonstrated a range of bioactivities, including antihypertensive, antidiabetic, antimicrobial, antimalarial, quorum sensing, anticancer, and antioxidant activities. Peptide 1137 exhibited moderate selfassembly, balanced bioactivity, and high structural stability, making it the most promising candidate. Poor solubility, attributed to the presence of tryptophan (W) and histidine (H), was observed. In conclusion, this study underscores the potential of lentilderived peptides as biomaterials, emphasizing the benefits of poor solubility for controlled bioactive release. Future work should validate these computational predictions experimentally, optimize safety, solubility and stability, and explore advanced biomaterial applications in tissue engineering and drug delivery systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".