Deep Profiling of the Cleavage Specificity and Human Substrates of Snake Venom Metalloprotease HF3 by Proteomic Identification of Cleavage Site Specificity (PICS) Using Proteome Derived Peptide Libraries and Terminal Amine Isotopic Labeling of Substrates (TAILS) N-Terminomics
Bibliographic record
Abstract
venom, induces local hemorrhage and targets extracellular matrix (ECM) components, including collagens and proteoglycans, and plasma proteins. However, the full substrate repertoire of this metalloprotease is unknown. We report positional proteomic studies identifying >2000 N-termini, including neo-N-termini of HF3 cleavage sites in mouse embryonic fibroblast secretome proteins. Terminal amine isotopic labeling of substrates (TAILS) analysis identified a preference for Leu at the P1' position among candidate HF3 substrates including proteins of the ECM and focal adhesions and the cysteine protease inhibitor cystatin-C. Interestingly, 190 unique peptides matched to annotated cleavage sites in the TopFIND N-termini database, suggesting that these cleavages occurred at a site prone to cleavage or might have been generated by other proteases activated upon incubation with HF3, including caspases-3 and -7, cathepsins D and E, granzyme B, and MMPs 2 and 9. Using Proteomic identification of cleavage site specificity (PICS), a tryptic library derived from THP-1 monocytic cells was used as HF3 substrates for identifying protease cleavage sites and sequence preferences in peptides. A total of 799 unique cleavage sites were detected and, in accordance with TAILS analysis using native secreted protein substrates of MEF cells, revealed a clear preference for Leu at P1'. Taken together, these results greatly expand the known substrate degradome of HF3 and reveal potential new targets, which may serve as a basis to better elucidate the complex pathophysiology of snake envenomation.
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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".