Selection of TAR RNA-Binding Chameleon Peptides by Using a Retroviral Replication System
Bibliographic record
Abstract
The interaction between the arginine-rich motif (ARM) of the human immunodeficiency virus (HIV) Tat protein and TAR RNA is essential for Tat activation and viral replication. Two related lentiviruses, bovine immunodeficiency virus (BIV) and Jembrana disease virus (JDV), also require Tat ARM-TAR interactions to mediate activation, but the viruses have evolved different RNA-binding strategies. Interestingly, the JDV ARM can act as a "chameleon," adopting both the HIV and BIV TAR binding modes. To examine how RNA-protein interactions may evolve in a viral context and possibly to identify peptides that recognize HIV TAR in novel ways, we devised a retroviral system based on HIV replication to amplify and select for RNA binders. We constructed a combinatorial peptide library based on the BIV Tat ARM and identified peptides that, like the JDV Tat ARM, also function through HIV TAR, revealing unexpected sequence characteristics of an RNA-binding chameleon. The results suggest that a retroviral screening approach may help identify high-affinity TAR binders and may provide new insights into the evolution of RNA-protein interactions.
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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 | 0.001 | 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".