The Cellular Antiviral Protein APOBEC3G Interacts with HIV-1 Reverse Transcriptase and Inhibits Its Function during Viral Replication
Bibliographic record
Abstract
The cytidine deaminase APOBEC3G (A3G) exerts a multifaceted antiviral effect against HIV-1 infection. First, A3G was shown to be able to terminate HIV infection by deaminating the cytosine residues to uracil in the minus strand of the viral DNA during reverse transcription. Also, a number of studies have indicated that A3G inhibits HIV-1 reverse transcription by a non-editing-mediated mechanism. However, the mechanism by which A3G directly disrupts HIV-1 reverse transcription is not fully understood. In the present study, by using a cell-based coimmunoprecipitation (Co-IP) assay, we detected the direct interaction between A3G and HIV-1 reverse transcriptase (RT) in produced viruses and in the cotransfected cells. The data also suggested that their interaction did not require viral genomic RNA bridging or other viral proteins. Additionally, a deletion analysis showed that the RT-binding region in A3G was located between amino acids 65 and 132. Overexpression of the RT-binding polypeptide A3G(65-132) was able to disrupt the interaction between wild-type A3G and RT, which consequently attenuated the anti-HIV effect of A3G on reverse transcription. Overall, this paper provides evidence for the physical and functional interaction between A3G and HIV-1 RT and demonstrates that this interaction plays an important role in the action of A3G against HIV-1 reverse transcription.
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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".