Replacement of Murine Leukemia Virus Readthrough Mechanism by Human Immunodeficiency Virus Frameshift Allows Synthesis of Viral Proteins and Virus Replication
Bibliographic record
Abstract
Retroviruses use unusual recoding strategies to synthesize the Gag-Pol polyprotein precursor of viral enzymes. In human immunodeficiency virus, ribosomes translating full-length viral RNA can shift back by 1 nucleotide at a specific site defined by the presence of both a slippery sequence and a downstream stimulatory element made of an extensive secondary structure. This so-called frameshift mechanism could become a target for the development of novel antiviral strategies. A different recoding strategy is used by other retroviruses, such as murine leukemia viruses, to synthesize the Gag-Pol precursor; in this case, a stop codon is suppressed in a readthrough process, again due to the presence of a specific structure adopted by the mRNA. Development of antiframeshift agents will greatly benefit from the availability of a simple animal and virus model. For this purpose, the murine leukemia virus readthrough region was rendered inactive by mutagenesis and the frameshift region of human immunodeficiency virus was inserted to generate a chimeric provirus. This substitution of readthrough by frameshift allows the synthesis of viral proteins, and the chimeric provirus sequence was found to generate infectious viruses. This system could be a most interesting alternative to study ribosomal frameshift in the context of a virus amenable to the use of a simple animal model.
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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 | 0.001 |
| 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".