Efficient Production of HIV-1 Virus-Like Particles from a Mammalian Expression Vector Requires the N-Terminal Capsid Domain
Bibliographic record
Abstract
It is now well accepted that the structural protein Pr55(Gag) is sufficient by itself to produce HIV-1 virus-like particles (VLPs). This polyprotein precursor contains different domains including matrix, capsid, SP1, nucleocapsid, SP2 and p6. In the present study, we wanted to determine by mutagenesis which region(s) is essential to the production of VLPs when Pr55(Gag) is inserted in a mammalian expression vector, which allows studying the protein of interest in the absence of other viral proteins. To do so, we first studied a minimal Pr55(Gag) sequence called Gag min that was used previously. We found that Gag min fails to produce VLPs when expressed in an expression vector instead of within a molecular clone. This failure occurs early in the cell at the assembly of viral proteins. We then generated a series of deletion and substitution mutants, and examined their ability to produce VLPs by combining biochemical and microscopic approaches. We demonstrate that the matrix region is not necessary, but that the efficiency of VLP production depends strongly on the presence of its basic region. Moreover, the presence of the N-terminal domain of capsid is required for VLP production when Gag is expressed alone. These findings, combined with previous observations indicating that HIV-1 Pr55(Gag)-derived VLPs act as potent stimulators of innate and acquired immunity, make the use of this strategy worth considering for vaccine development.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".