In Silico Analysis of Bioactive Peptides Produced from Underutilized Sea Cucumber By-Products—A Bioinformatics Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it