Structures of Human DPP7 Reveal the Molecular Basis of Specific Inhibition and the Architectural Diversity of Proline-Specific Peptidases
Bibliographic record
Abstract
Proline-specific dipeptidyl peptidases (DPPs) are emerging targets for drug development. DPP4 inhibitors are approved in many countries, and other dipeptidyl peptidases are often referred to as DPP4 activity- and/or structure-homologues (DASH). Members of the DASH family have overlapping substrate specificities, and, even though they share low sequence identity, therapeutic or clinical cross-reactivity is a concern. Here, we report the structure of human DPP7 and its complex with a selective inhibitor Dab-Pip (L-2,4-diaminobutyryl-piperidinamide) and compare it with that of DPP4. Both enzymes share a common catalytic domain (α/β-hydrolase). The catalytic pocket is located in the interior of DPP7, deep inside the cleft between the two domains. Substrates might access the active site via a narrow tunnel. The DPP7 catalytic triad is completely conserved and comprises Ser162, Asp418 and His443 (corresponding to Ser630, Asp708 and His740 in DPP4), while other residues lining the catalytic pockets differ considerably. The "specificity domains" are structurally also completely different exhibiting a β-propeller fold in DPP4 compared to a rare, completely helical fold in DPP7. Comparing the structures of DPP7 and DPP4 allows the design of specific inhibitors and thus the development of less cross-reactive drugs. Furthermore, the reported DPP7 structures shed some light onto the evolutionary relationship of prolyl-specific peptidases through the analysis of the architectural organization of their domains.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".