N‐ and C‐terminal degradomics: new approaches to reveal biological roles for plant proteases from substrate identification
Bibliographic record
Abstract
Proteolysis is an irreversible post-translational modification that regulates many intra- and intercellular processes, including essential go/no-go decisions during cell proliferation, development and cell death. Hundreds of protease-coding genes have been identified in plants, but few have been linked to specific substrates. Conversely, proteolytic processes are frequently observed in plant biology but rarely have they been ascribed to specific proteases. In mammalian systems, unbiased system-wide proteomics analyses of protease activities have recently been tremendously successful in the identification of protease substrate repertoires, also known as substrate degradomes. Knowledge of the substrate degradome is key to understand the role of proteases in vivo. Quantitative shotgun proteomic studies have been successful in identifying protease substrates, but while simple to perform they are biased toward abundant proteins and do not reveal precise cleavage sites. Current degradomics techniques overcome these limitations by focusing on the information-rich amino- and carboxy-terminal peptides of the original mature proteins and the protease-generated neo-termini. Targeted quantitative analysis of protein termini identifies precise cleavage sites in protease substrates with exquisite sensitivity and dynamic range in in vitro and in vivo systems. This review provides an overview of state-of-the-art methods for enrichment of protein terminal peptides, and their application to protease research. These emerging degradomics techniques promise to clarify the elusive biological roles of proteases and proteolysis in plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".