Substrate Cleavage Analysis of Furin and Related Proprotein Convertases
Bibliographic record
Abstract
We present the data and the technology, a combination of which allows us to determine the identity of proprotein convertases (PCs) related to the processing of specific protein targets including viral and bacterial pathogens. Our results, which support and extend the data of other laboratories, are required for the design of effective inhibitors of PCs because, in general, an inhibitor design starts with a specific substrate. Seven proteinases of the human PC family cleave the multibasic motifs R-X-(R/K/X)-R downward arrow and, as a result, transform proproteins, including those from pathogens, into biologically active proteins and peptides. The precise cleavage preferences of PCs have not been known in sufficient detail; hence we were unable to determine the relative importance of the individual PCs in infectious diseases, thus making the design of specific inhibitors exceedingly difficult. To determine the cleavage preferences of PCs in more detail, we evaluated the relative efficiency of furin, PC2, PC4, PC5/6, PC7, and PACE4 in cleaving over 100 decapeptide sequences representing the R-X-(R/K/X)-R downward arrow motifs of human, bacterial, and viral proteins. Our computer analysis of the data and the follow-on cleavage analysis of the selected full-length proteins corroborated our initial results thus allowing us to determine the cleavage preferences of the PCs and to suggest which PCs are promising drug targets in infectious diseases. Our results also suggest that pathogens, including anthrax PA83 and the avian influenza A H5N1 (bird flu) hemagglutinin precursor, evolved to be as sensitive to PC proteolysis as the most sensitive normal human proteins.
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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 | 0.000 |
| 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".