Simple, scalable, and ultrasensitive tip-based identification of protease substrates
Bibliographic record
Abstract
Proteases are in the center of many diseases, and consequently, proteases and their substrates are important drug targets as represented by an estimated 5–10% of all drugs under development. Mass spectrometry has been an indispensable tool for the discovery of novel protease substrates, particularly through the proteome-scale enrichment of so-called N-terminal peptides representing endogenous protein N termini. Methods such as combined fractional diagonal chromatography (COFRADIC) 1 1The abbreviations used are: COFRADIC, combined fractional diagonal chromatography; ChaFRAtip, charge-based fractional diagonal chromatography in tip format; SCX, strong-cation-exchange chromatography; ChaFRADIC, charge-based fractional diagonal chromatography; TAILS, terminal amine isotopic labeling of substrates; TEAB, triethylammonium bicarbonate; BCA, bicinchoninic acid assay; TNC, theoretical net charge. and, later, terminal amine isotopic labeling of substrates (TAILS) have revealed numerous insights into protease substrates and consensus motifs. We present an alternative and simple protocol for N-terminal peptide enrichment, based on charge-based fractional diagonal chromatography (ChaFRADIC) and requiring only well-established protein chemistry and a pipette tip. Using iTRAQ-8-plex, we quantified on average 2,073 ± 52 unique N-terminal peptides from only 4.3 μg per sample/channel, allowing the identification of proteolytic targets and consensus motifs. This high sensitivity may even allow working with clinical samples such as needle biopsies in the future. We applied our method to study the dynamics of staurosporine-induced apoptosis. Our data demonstrate an orchestrated regulation of specific pathways after 1.5 h, 3 h, and 6 h of treatment, with many important players of homeostasis targeted already after 1.5 h. We additionally observed an early multilevel modulation of the splicing machinery both by proteolysis and phosphorylation. This may reflect the known role of alternative splicing variants for a variety of apoptotic genes, which seems to be a driving force of staurosporine-induced apoptosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".