Quantitative Multiplex Substrate Profiling of Peptidases by Mass Spectrometry
Bibliographic record
Abstract
Proteolysis is an integral component of life and has been implicated in many disease processes. To improve our understanding of peptidase function, it is imperative to develop tools to uncover substrate specificity and cleavage efficiency. Here, we combine the quantitative power of tandem mass tags (TMTs) with an established peptide cleavage assay to yield quantitative Multiplex Substrate Profiling by Mass Spectrometry (qMSP-MS). This assay was validated with papain, a well-characterized cysteine peptidase, to generate cleavage efficiency values for hydrolysis of 275 unique peptide bonds in parallel. To demonstrate the breath of this assay, we show that qMSP-MS can uncover the substrate specificity of minimally characterized intramembrane rhomboid peptidases, as well as define hundreds of proteolytic activities in complex biological samples, including secretions from lung cancer cell lines. Importantly, our qMSP-MS library uses synthetic peptides whose termini are unmodified, allowing us to characterize not only endo- but also exo-peptidase activity. Each cleaved peptide sequence can be ranked by turnover rate, and the amino acid sequence of the best substrates can be used for designing fluorescent reporter substrates. Discovery of peptide substrates that are selectively cleaved by peptidases which are active at the site of disease highlights the potential for qMSP-MS to guide the development of peptidase-activating drugs for cancer and infectious disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".