Identification of novel bifunctional HIV-1 reverse transcriptase inhibitors
Bibliographic record
Abstract
OBJECTIVES: The increasing prevalence of mutations in HIV-1 reverse transcriptase (RT) that confer resistance to existing NRTIs and NNRTIs underscores the need to develop RT inhibitors with novel mode-of-inhibition and distinct resistance profiles. METHODS: Biochemical assays were employed to identify inhibitors of RT activity and characterize their mode of inhibition. The antiviral activity of the inhibitors was assessed by cell-based assays using laboratory HIV-1 isolates and MT4 cells. RT variants were purified via avidin affinity columns. RESULTS: Compound A displayed equal or greater potency against many common NNRTI-resistant RTs (K103N and Y181C RTs) relative to WT RT. Despite possessing certain NNRTI-like properties, such as being unable to inhibit an engineered variant of RT lacking an NNRTI-binding pocket, we found that compound A was dependent on Mg2+ for binding to RT. Optimization of compound A led to more potent analogues, which retained similar activities against WT and K103N mutant viruses with submicromolar potency in a cell-based assay. One of the analogues, compound G, was crystallized in complex with RT and the structure was determined at 2.6 Å resolution. The structure indicated that compound G simultaneously interacts with the active site (Asp186), the highly conserved primer grip region (Leu234 and Trp229) and the NNRTI-binding pocket (Tyr188). CONCLUSIONS: These findings reveal a novel class of RT bifunctional inhibitors that are not sensitive to the most common RT mutations, which can be further developed to address the deficiency of current RT inhibitors.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".