Activity‐based proteomics reveals nine target proteases for the recombinant protein‐stabilizing inhibitor <i>Sl</i><scp>CYS</scp>8 in <i>Nicotiana benthamiana</i>
Bibliographic record
Abstract
Co-expression of protease inhibitors like the tomato cystatin SlCYS8 is useful to increase recombinant protein production in plants, but key proteases involved in protein proteolysis are still unknown. Here, we performed activity-based protein profiling to identify proteases that are inhibited by SlCYS8 in agroinfiltrated Nicotiana benthamiana. We discovered that SlCYS8 selectively suppresses papain-like cysteine protease (PLCP) activity in both apoplastic fluids and total leaf extracts, while not affecting vacuolar-processing enzyme and serine hydrolase activity. A robust concentration-dependent inhibition of PLCPs occurred in vitro when purified SlCYS8 was added to leaf extracts, indicating direct cystatin-PLCP interactions. Activity-based proteomics revealed that nine different Cathepsin-L/-F-like PLCPs are strongly inhibited by SlCYS8 in leaves. By contrast, the activity of five other Cathepsin-B/-H-like PLCPs, as well as 87 Ser hydrolases, was unaffected by SlCYS8. SlCYS8 expression prevented protein degradation by inhibiting intermediate and mature isoforms of granulin-containing proteases from the Resistant-to-Desiccation-21 (RD21) PLCP subfamily. Our data underline the key role of endogenous PLCPs on recombinant protein degradation and reveal candidate proteases for depletion strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".