Probing the substrate specificities of matriptase, matriptase‐2, hepsin and DESC1 with internally quenched fluorescent peptides
Bibliographic record
Abstract
Type II transmembrane serine proteases are an emerging class of proteolytic enzymes involved in tissue homeostasis and a number of human disorders such as cancer. To better define the biochemical functions of a subset of these proteases, we compared the enzymatic properties of matriptase, matriptase-2, hepsin and DESC1 using a series of internally quenched fluorogenic peptide substrates containing o-aminobenzoyl and 3-nitro-tyrosine. We based the sequence of the peptides on the P4 to P4' activation sequence of matriptase (RQAR-VVGG). Positions P4, P3, P2 and P1' were substituted with nonpolar (Ala, Leu), aromatic (Tyr), acid (Glu) and basic (Arg) amino acids, whereas P1 was fixed to Arg. Of the four type II transmembrane serine proteases studied, matriptase-2 was the most promiscuous, and matriptase was the most discriminating, with a distinct specificity for Arg residues at P4, P3 and P2. DESC1 had a preference similar to that of matriptase, but with a propensity for small nonpolar amino acids (Ala) at P1'. Hepsin shared similarities with matriptase and DESC1, but was markedly more permissive at P2. Matriptase-2 manifested broader specificities, as well as substrate inhibition, for selective internally quenched fluorescent substrates. Lastly, we found that antithrombin III has robust inhibitory properties toward matriptase, matriptase-2, hepsin and DESC1, whereas plasminogen activator inhibitor-1 and alpha(2)-antiplasmin inhibited matriptase-2, hepsin and DESC1, and to a much lesser extent, matriptase. In summary, our studies revealed that these enzymes have distinct substrate preferences.
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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) | 0.001 | 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".