Protease Specificity Profiling by Tandem Mass Spectrometry Using Proteome-Derived Peptide Libraries
Bibliographic record
Abstract
Protease specificity profiling using proteome-derived, database-searchable peptide libraries is a novel approach to define the active site specificity of proteolytic enzymes we call PICS (Proteomic Identification of protease Cleavage Sites). Proteome-derived peptide libraries are generated by trypsin, GluC, or chymotrypsin digestion of biologically relevant proteomes, such as cytosolic lysates, to generate three separate libraries that each differ from the others in their C-terminal amino acid residues according to the protease specificity. Primary amines of all peptides are then chemically protected so that after incubation with a test protease, the neo-N-termini of the prime-side cleavage products with exposed α-amines can be specifically biotinylated, enriched, and identified by liquid chromatography-tandem mass spectrometry. The corresponding nonprime-side sequences are derived bioinformatically. Suited for all protease classes except carboxyproteases and those aminoproteases and dipeptidases requiring a free α-amine for cleavage, PICS simultaneously profiles the specificity of prime and nonprime positions and directly determines scissile peptide bonds of up to hundreds of cleavage site sequences in a single experiment. This wealth of sequence specificity information also allows for the investigation of subsite cooperativity. Herein we describe a simplified procedure to produce PICS peptide libraries, the methods to perform a PICS assay, and a new method of data analysis.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".