Cyclic Peptidic Furin Inhibitors Developed by Combinatorial Chemistry
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Furin is a human serine protease responsible for activating numerous physiologically relevant cell substrates and is also involved in the development of various pathological conditions, including inflammatory diseases, cancers, and viral and bacterial infections. Therefore, compounds with the ability to inhibit furin’s proteolytic action are regarded as potential therapeutics. Here we took the combinatorial chemistry approach (library consisting of 2000 peptides) to obtain new, strong, and stable peptide furin inhibitors. The extensively studied trypsin inhibitor SFTI-1 was used as a leading structure. A selected monocylic inhibitor was further modified to finally yield five mono- or bicyclic furin inhibitors with values of K i in the subnanomolar range. Inhibitor 5 was the most active ( K i = 0.21 nM) and significantly more proteolytically resistant than the reference furin inhibitor described in the literature. Moreover, it reduced furin-like activity in PANC-1 cell lysate. Detailed analysis of furin–inhibitor complexes using molecular dynamics simulations is also reported.
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) | 0.000 | 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) | 0.001 | 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".