A Highly Sensitive BRET-Based Reporter for Live-Cell Detection of HIV-1 Protease Activity and Inhibitor Screening
Bibliographic record
Abstract
Given their role in viral polyprotein processing, viral proteases (PRs) are excellent targets for antiviral therapy. Most assays developed for screening PR inhibitors are in vitro assays, and therefore have several limitations, including the inability to account for cell permeability, toxicity and the need for compounds activation within cells. The development of cellular reporters overcoming these limitations is therefore highly desirable. In this study, we developed two different Bioluminescence Resonance Energy Transfer (BRET)-based reporters for Human Immunodeficiency virus-1 (HIV-1) PR, allowing the simultaneous monitoring of cell viability and HIV-1 PR activity. The reporters employ two different BRET pairs as donor and acceptor moieties: Renilla luciferase (RLuc) with Yellow Fluorescent Protein (YFP), and Nano luciferase (NLuc) with mNeonGreen (mNG), both linked by the HIV-1 p2/p7 cleavage site. While both reporters specifically detected HIV-1 protease activity, mNG-p2/p7-NLuc exhibited higher sensitivity, increased energy transfer and better spectral separation between donor and acceptor emissions, resulting in a significantly higher BRET ratio. mNG-p2/p7-NLuc was used to quantify the effect of a panel of protease inhibitors in living cells, assessing simultaneously cell viability and HIV-1 PR activity. Additionally, it was employed to measure the potency of well-known HIV-1 PR inhibitors. Together, these findings demonstrate the utility of the mNG-p2/p7-NLuc reporter as a cell-based tool for the evaluation of HIV-1 PR activity and inhibitor efficacy. Its dual-readout capability provides a valuable platform for antiviral drug screening in physiologically relevant conditions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".