Reverse Proteolysis Uncovers a Hidden Dimension of the Peptidome
Bibliographic record
Abstract
Abstract Proteases are conventionally regarded as degradative enzymes, yet their catalytic machinery also permits peptide bond formation through reverse proteolysis, a process that remains poorly characterized. Here, we show that lysosomal cysteine cathepsins catalyze iterative cycles of hydrolysis and ligation to generate multi-generational fusion peptides, including hybrids derived from host-viral protein substrates. Quantitative analysis demonstrates that peptide ligation can account for up to 4.5% of proteolytic turnover. This activity is strongly influenced by pH, substrate sequence, and post-translational modification, with citrullination and neutral pH favoring fusion peptide formation and the accumulation of more stable higher-order products. Using full-length protein substrates, we provide direct evidence that cathepsins can generate a hybrid insulin peptide previously identified as a Type 1 Diabetes (TID) autoantigen in patients. Moreover, several identified fusion peptides show effective binding to TID-associated HLA class II molecules. To examine whether ligation products can be captured under cellular conditions, we developed a click-based targeted transpeptide retrieval and purification strategy (CT-TRAP), which enabled detection of probe-derived cis/transpeptides in cell-based systems under controlled conditions. These findings establish reverse proteolysis by cysteine cathepsins as a quantifiable enzymatic pathway for generating non-genomically templated peptides, revealing an unrecognized dimension of lysosomal protease activity and peptide diversification.
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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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".