Marine and coastal organisms: a source of biomedically relevant dipeptidyl peptidase IV inhibitors
Bibliographic record
Abstract
Dipeptidyl peptidase IV (DPP-IV, EC 3.4.14.5), also known as CD26, is a serine aminopeptidase that preferentially cleaves Xaa-Pro or Xaa-Ala dipeptides from the N-terminus of peptides leading to their biological activation or inactivation. The enzyme is an homodimer and each subunit is formed by an αβ-hydrolase domain and a β-propeller domain. It has an important role in multiple physiological functions, including the regulation of glucose metabolism, being one of the current targets for the treatment of type 2 diabetes mellitus. It is also up-regulated in rheumatoid arthritis, psoriasis, colitis, multiple sclerosis and transplant rejection. This enzyme also regulates immune system responses mediated by CD4+ T lymphocytes; furthermore, DPP-IV activity is dysregulated in pathologies like thyroid, ovarian, lung, skin, prostate cancers and central nervous system tumors. In clinical practice, DPP-IV inhibitors have several beneficial effects such as anti-hyperglycemia and pancreatic islet protection, immune regulation, cardiovascular and renal protection, anticancer effects, and anti-inflammation. Thus, this enzyme evolved as a target of attention for the development of more efficient pathology diagnostics, and for the development of inhibitors to treat type 2 diabetes mellitus and cancer. Marine and coastal organisms are an abundant source of different bioactive molecules including peptidases and peptidases inhibitors of almost all mechanistic classes, being serine class the most studied. In the present contribution, we review the strategies used to identify and characterize DPP-IV inhibitors from marine and coastal organisms. We show that marine biodiversity is an important, promising, and still unexplored source of inhibitors of dipeptidyl peptidase IV, which may have biomedical applications in human diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".