Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity
Bibliographic record
Abstract
Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa–Pro-type peptide-like structures from several crystal structures of DPP-IV ligand–protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa–Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa–Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa–Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure–activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R 2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q 2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".