Pharmacoinformatics and cellular studies of algal peptides as functional molecules to modulate type-2 diabetes markers
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
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.
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 | 0.000 |
| 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) | 0.000 | 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