Proteolytic Cleavage—Mechanisms, Function, and “Omic” Approaches for a Near-Ubiquitous Posttranslational Modification
Bibliographic record
Abstract
Proteases enzymatically hydrolyze peptide bonds in substrate proteins, resulting in a widespread, irreversible posttranslational modification of the protein's structure and biological function. Often regarded as a mere degradative mechanism in destruction of proteins or turnover in maintaining physiological homeostasis, recent research in the field of degradomics has led to the recognition of two main yet unexpected concepts. First, that targeted, limited proteolytic cleavage events by a wide repertoire of proteases are pivotal regulators of most, if not all, physiological and pathological processes. Second, an unexpected in vivo abundance of stable cleaved proteins revealed pervasive, functionally relevant protein processing in normal and diseased tissue-from 40 to 70% of proteins also occur in vivo as distinct stable proteoforms with undocumented N- or C-termini, meaning these proteoforms are stable functional cleavage products, most with unknown functional implications. In this Review, we discuss the structural biology aspects and mechanisms of catalysis by different protease classes. We also provide an overview of biological pathways that utilize specific proteolytic cleavage as a precision control mechanism in protein quality control, stability, localization, and maturation, as well as proteolytic cleavage as a mediator in signaling pathways. Lastly, we provide a comprehensive overview of analytical methods and approaches to study activity and substrates of proteolytic enzymes in relevant biological models, both historical and focusing on state of the art proteomics techniques in the field of degradomics research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".