Selection of peptides interfering with a ribosomal frameshift in the human immunodeficiency virus type 1
Bibliographic record
Abstract
The human immunodeficiency virus of type 1 (HIV-1) uses a programmed -1 ribosomal frameshift to produce the precursor of its enzymes, and changes in frameshift efficiency reduce replicative fitness of the virus. We used a fluorescent two-reporter system to screen for peptides that reduce HIV-1 frameshift in bacteria, knowing that the frameshift can be reproduced in Escherichia coli. Expression of one reporter, the green fluorescent protein (GFP), requires the HIV-1 frameshift, whereas the second reporter, the red fluorescent protein (RFP), is used to assess normal translation. A peptide library biased for RNA binding was inserted into the sequence of the protein thioredoxin and expressed in reporter-containing bacteria, which were then screened by fluorescence-activated cell sorting (FACS). We identified peptide sequences that reduce frameshift efficiency by over 50% without altering normal translation. The identified sequences are also active against different frameshift stimulatory signals, suggesting that they bind a target important for frameshifting in general, probably the ribosome. Successful transfer of active sequences to a different scaffold in a eukaryotic test system demonstrates that the anti-frameshift activity of the peptides is neither due to scaffold-dependent conformation nor effects of the scaffold protein itself on frameshifting. The method we describe identifies peptides that will provide useful tools to further study the mechanism of frameshift and may permit the development of lead compounds of therapeutic interest.
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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) | 0.000 | 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".