High throughput protease profiling comprehensively defines active site specificity for thrombin and ADAMTS13
Bibliographic record
Abstract
Abstract We have combined random 6 amino acid substrate phage display with high throughput sequencing to comprehensively define the active site specificity of the serine protease thrombin and the metalloprotease ADAMTS13. The substrate motif for thrombin was determined by >6,700 cleaved peptides, and was highly concordant with previous studies. In contrast, ADAMTS13 cleaved only 96 peptides (out of >10 7 sequences), with no apparent consensus motif. However, when the hexapeptide library was substituted into the P3-P3′ interval of VWF73, an exosite-engaging substrate of ADAMTS13, 1670 unique peptides were cleaved. ADAMTS13 exhibited a general preference for aliphatic amino acids throughout the P3-P3′ interval, except at P2 where Arg was tolerated. The cleaved peptides assembled into a motif dominated by P3 Leu, and bulky aliphatic residues at P1 and P1′. Overall, the P3-P2′ amino acid sequence of von Willebrand Factor appears optimally evolved for ADAMTS13 recognition. These data confirm the critical role of exosite engagement for substrates to gain access to the active site of ADAMTS13, and define the substrate recognition motif for ADAMTS13. Combining substrate phage display with high throughput sequencing is a powerful approach for comprehensively defining the active site specificity of proteases.
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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 | 0.001 | 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".