Activity-Based Proteome Profiling of Hepatoma Cells during Hepatitis C Virus Replication Using Protease Substrate Probes
Bibliographic record
Abstract
Activity-based protein profiling (ABPP) offers direct insight into changes in catalytic activity of enzyme classes in complex proteomes, rather than protein or transcript abundance. Here, ABPP was performed in Huh7 hepatoma cell lines with a group of ABPP probes composed of an N-acetylated amino acid, that mimic the P(1) position in protease peptide substrates. Five different probes bearing distinct amino acids (Ser, Thr, Phe, Glu and His) labeled 54 differentially active proteins, including proteases, other hydrolases, oxidoreductases and isomerases. Four of the six protease families were targeted based on their P(1) substrate preferences. The broader specificity of the labeling observed could be explained by the substrate-based targeting nature and the electrophilic properties of the ABPP probes. When applied to Huh7 cells stably replicating hepatitis C virus (HCV) subgenomic replicon RNA, four proteins showed reduced activity, while three proteins had increased activity during HCV replication. These differentially active hits included carboxylesterase 1, cathepsin D, HSP105, protein disulfide isomerase 1 and A6, chaperonin containing TCP1 and isochorismatase domain containing 1, which demonstrated substrate preferences by being labeled by specific substrate probes. This illustrates the broader activity-based profiling capabilities of these substrate-based probes to reveal novel enzyme candidates and their potential roles during HCV replication.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".