In Silico Analysis of Bioactive Peptides Produced from Underutilized Sea Cucumber By-Products—A Bioinformatics Approach
Bibliographic record
Abstract
Bioinformatic tools are widely used in predicting potent bioactive peptides from food derived materials. This study was focused on utilizing sea cucumber processing by-products for generating antioxidant and ACE inhibitory peptides by application of a range of in silico techniques. Identified peptides using LC−MS/MS were virtually screened by PepRank technique followed by in silico proteolysis simulation with representative digestive enzymes using BIOPEP-UWMTM data base tool. The resultant peptides after simulated digestion were evaluated for their toxicity using ToxinPred software. All digestive resistance peptides were found to be non-toxic and displayed favorable functional properties indicating their potential for use in a wide range of food applications, including hydrophobic and hydrophilic systems. Identified peptides were further assessed for their medicinal characteristics by employing SwissADME web-based application. Our findings provide an insight on potential use of undervalued sea cucumber processing discards for functional food product development and natural pharmaceutical ingredients attributed to the oral drug discovery process.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".