Production of human cathepsins using <scp>Expi293™</scp> mammalian cell expression system for off‐target activity of cysteine protease inhibitor screening
Bibliographic record
Abstract
Abstract Following the SARS‐CoV‐2 pandemic, many direct‐acting antivirals targeting viral cysteine protease were developed. SARS‐CoV‐2, as well as other viruses, rely on cysteine proteases for their replication, suggesting future generations of antivirals targeting cysteine proteases will emerge. A major concern for these first‐generation drugs is the off‐target effects on host cysteine proteases. Therefore, screening for inhibitor specificity is a crucial step in antiviral drug development. Cathepsins are one of the most abundant human proteases, which have roles in maintaining cell health and are key to many physiological processes. Here we describe a general expression and purification protocol for cathepsins B, S, and L using the Expi293™ mammalian expression system. We characterized the glycosylation pattern and kinetic parameters of purified enzymes with commercially available cathepsin‐specific fluorogenic substrates and also established cathepsin inhibition screen assays. We tested specificity indices of peptidic inhibitors of SARS‐CoV‐2 M pro synthesized by our team and nirmatrelvir as a benchmark for all three cathepsins. Establishing a reliable cathepsin inhibition assay would assist with screening of newly developed cysteine protease inhibitors for off‐target activity within the scope of pandemic preparedness.
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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 | 0.001 | 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 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".