The Dynamic Landscape of the Full-Length HIV-1 Transactivator of Transcription
Bibliographic record
Abstract
The type 1 human immunodeficiency virus (HIV-1) transactivator of transcription (Tat) is a small RNA-binding protein essential for viral gene expression and replication. It has also been shown to bind to a large number of human proteins and to modulate many different cellular activities. We have used nuclear magnetic resonance (NMR) spectroscopy and hydrogen exchange chemistry to measure backbone dynamics over the millisecond to picosecond time scales. Sequential backbone assignment was facilitated by several isotope labeling schemes, including uniform labeling, site-specific labeling, and unlabeling. (15)N NMR relaxation parameters were measured and analyzed by reduced spectral density mapping and the Lipari-Szabo Model-Free approach to characterize the backbone dynamics on the picosecond to nanosecond time scale. The results indicate that the protein exists in an extended disordered conformational ensemble. NMR relaxation dispersion profiles show that on the millisecond time scale no conformational exchange is detected for any of the residues, supporting the model of a disordered backbone. NMR chemical shift differences from random coil values suggest that some segments of the protein have a modest propensity to fold; comparison to X-ray diffraction structures of Tat complexes indicates that some segments of the protein function through an induced-fit mechanism whereas other segments likely operate by conformational selection. Surprisingly, measured hydrogen exchange rates are higher than predicted for a disordered polymer, but this is explained as being caused by the high net charge on the protein that enhances base-catalyzed hydrogen exchange. The dynamics results provide a deeper understanding of the protein conformational ensemble and form a foundation for future studies of the conformational changes that accompany the formation of the superelongation complex that activates viral transcription.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".