A Mass Spectrometry-Cleavable N-Terminal Tagging Strategy for System-Level Protease Activity Profiling
Bibliographic record
Abstract
Proteolysis is one of the most important protein post-translational modifications (PTMs) that influences the functions, activities, and structures of nearly all proteins during their lifetime. This irreversible PTM is regulated and catalyzed by proteases through hydrolysis reaction in the process of protein maturation or degradation. The identification of proteolytic substrates is pivotal in understanding the specificity of proteases and the physiological role of proteolytic process. However, tracking these biological phenomena in native cells is very difficult due to their low abundances. Currently, no efficient methods are available to identify proteolytic products from large-scale samples. To facilitate the targeted identification of low-abundant proteolytic products, we devise a strategy incorporating a novel biotinylated reagent PFP (pentafluorophenyl)-Rink-biotin to specifically target, enrich and identify proteolytic N-terminus. Within the PFP-Rink-biotin reagent, an MS-cleavable feature is designed to assist in the unambiguous confirmation of the enriched proteolytic N-termini. This is the first reported study of identifying proteolytic N-terminus by MS-cleavable feature widely adopted in studying low-abundant protein PTMs and cross-linking/MS. The proof-of-concept study was performed with multiple standard proteins whose N-terminus were successfully modified, enriched and identified by a signature ion (SI) in the MS/MS fragmentation, along with the determination of N-terminal peptide sequences by multistage tandem MS of the complementary fragment generated after the cleavage of MS-cleavable bond. For large-scale application, the enrichment and identification of protein N-termini from Escherichia.coli cells were demonstrated along with a bioinformatics workflow. We believe this method can be very useful to locate proteolytic products in native cellular environment with high confidence.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".