Activity-based protein profiling of the hepatitis C virus replication in Huh-7 hepatoma cells using a non-directed active site probe
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) poses a growing threat to global health as it often leads to serious liver diseases and is one of the primary causes for liver transplantation. Currently, no vaccines are available to prevent HCV infection and clinical treatments have limited success. Since HCV has a small proteome, it relies on many host cell proteins to complete its life cycle. In this study, we used a non-directed phenyl sulfonate ester probe (PS4 identical with) to selectively target a broad range of enzyme families that show differential activity during HCV replication in Huh-7 cells. RESULTS: The PS4 identical with probe successfully targeted 19 active proteins in nine distinct protein families, some that were predominantly labeled in situ compared to the in vitro labeled cell homogenate. Nine proteins revealed altered activity levels during HCV replication. Some candidates identified, such as heat shock 70 kDa protein 8 (or HSP70 cognate), have been shown to influence viral release and abundance of cellular lipid droplets. Other differentially active PS4 identical with targets, such as electron transfer flavoprotein alpha, protein disulfide isomerase A5, and nuclear distribution gene C homolog, constitute novel proteins that potentially mediate HCV propagation. CONCLUSIONS: These findings demonstrate the practicality and versatility of non-directed activity-based protein profiling (ABPP) to complement directed methods and accelerate the discovery of altered protein activities associated with pathological states such as HCV replication. Collectively, these results highlight the ability of in situ ABPP approaches to facilitate the identification of enzymes that are either predominantly or exclusively labeled in living cells. Several of these differentially active enzymes represent possible HCV-host interactions that could be targeted for diagnostic or therapeutic purposes.
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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 | 0.000 | 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) | 0.001 | 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".