Pharmacoinformatics and cellular studies of algal peptides as functional molecules to modulate type-2 diabetes markers
Bibliographic record
Abstract
Novel dietary strategies are urgently needed to address the limitations of current management and treatment options of Type-2 Diabetes (T2D). Marine algae-derived peptides (MAP) represent a promising avenue, although, their potential remains mostly underexplored. This study employs pharmacoinformatics and in vitro methods to evaluate the antidiabetic properties of MAP and provide new insights their mechanisms to mitigate the prevalence of T2D. Through a systematic search and predictive modeling, peptides were identified and assessed for bioactivity, toxicity, and drug-likeness. Furthermore, molecular docking simulations with protein targets related to T2D identified binding sites that be used to optimize the activity of MAP. The structure-activity relationship profile of MAP reveals 13 candidates with probable activity (Pa) scores >0.4, indicative of insulin promoter. The peptide FDGIP (P13;Phe-Asp-Gly-Ile-Pro) from Caulerpa lentillifera had the best in silico assessment value compared to 50 other peptides and its activity was confirmed by in vitro data (e.g.EC50 60.4 and 57.9 for α-amylase and α-glucosidase inhibitions). Interestingly, in 3T3-L1 cells, P13 exhibited inhibitory activities against transcription factors and hormones (MAPK8-JNK1/PPARGC1A/Ghrelin/GLP-1/CPT-1) that can regulate blood sugar and decrease as anti-diabetes. P13 then appears to be a peptide with antidiabetic action that may be used in the formulation foods to manage T2D.
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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) | 0.000 | 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".