Profiling the Substrate Specificity of Viral Protease VP4 by a FRET-Based Peptide Library Approach
Bibliographic record
Abstract
Knowing the substrate specificity of a protease is useful in determining its physiological substrates, developing robust assays, and designing specific inhibitors against the enzyme. In this work, we report the development of a combinatorial peptide library method for systematically profiling the substrate specificity of endopeptidases. A fluorescent donor (Edans) and quencher (Dabcyl) pair was added to the C- and N-termini of a support-bound peptide. Protease cleavage of the peptide removed the N-terminal quencher, resulting in fluorescent beads, which were isolated and individually sequenced by partial Edman degradation and mass spectrometry (PED-MS) to reveal the peptide sequence, as well as the site of proteolytic cleavage. The method was validated with bovine trypsin and Escherichia coli leader peptidase and subsequently applied to determine the substrate specificity of a viral protease, VP4, derived from the blotched snakehead virus (BSNV). The results show that VP4 cleaves peptides with a consensus sequence of (Abu/Ala/Pro)-X-Ala downward arrowX, in agreement with the previously observed cleavage sites in its protein substrates. Resynthesis and a solution-phase assay of several representative sequences against VP4 confirmed the library screening results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".