Defining the extended substrate specificity of kallikrein 1-related peptidases
Bibliographic record
Abstract
Human kallikrein 1-related peptidases (KLKs) form a subfamily of 15 extracellular (chymo)tryptic-like serine proteases. KLKs 4, 5, 13 and 14 display altered expression/activity in diverse pathological conditions, including cancer. However, their distinct (patho)physiological roles remain largely uncharacterized. As a step toward distinguishing their proteolytic functions, we attempt to define their primary and extended substrate specificities and identify candidate biological targets. Heterologously expressed KLKs 4, 5, 13 and 14 were screened against fluorogenic 7-amino-4-carbamoylmethylcoumarin positional scanning-synthetic combinatorial libraries with amino acid diversity at the P1-P4 positions. Our results indicate that these KLKs share a P1 preference for Arg. However, each KLK exhibited distinct P2-P4 specificities, attributable to structural variations in their surface loops. The preferred P4-P1 substrate recognition motifs based on optimal subsite occupancy were as follows: VI-QSAV-QL-R for KLK4; YFWGPV-RK-NSFAM-R for KLK5; VY-R-LFM-R for KLK13; and YW-KRSAM-HNSPA-R for KLK14. Protein database queries using these motifs yielded many extracellular targets, some of which represent plausible KLK substrates. For instance, cathelicidin, urokinase-type plasminogen activator, laminin and transmembrane protease serine 3 were retrieved as novel putative substrates for KLK4, 5, 13 and 14, respectively. Our findings may facilitate studies on the role of KLKs in (patho)physiology and can be used in the development of selective KLK inhibitors.
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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) | 0.000 | 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".