A targeted <scp>DNA</scp> substrate mechanism for the inhibition of <scp>HIV</scp>‐1 integrase by inhibitors with antiretroviral activity
Bibliographic record
Abstract
We recently reported that viral DNA could be the primary target of raltegravir (RAL), an efficient anti-HIV-1 drug, which acts by inhibiting integrase. To elucidate this mechanism, we conducted a comparative analysis of RAL and TB11, a diketoacid abandoned as an anti-HIV-1 drug for its weak efficiency and marked toxicity, and tested the effects of the catalytic cofactor Mg(2+) (5 mm) on drug-binding properties. We used circular dichroism and fluorescence to determine drug affinities for viral DNA long terminal repeats (LTRs) and peptides derived from the integrase active site and DNA retardation assays to assess drug intercalation into DNA base pairs. We found that RAL bound more tightly to LTR ends than did TB11 (a diketo acid bearing an azido group) and that Mg(2+) significantly increased the affinity of both RAL and TB11. We also observed a good relationship between drug binding with processed LTR and strand transfer inhibition. This unusual type of inhibition was caused by Mg(2+)-assisted binding of drugs to DNA substrate, rather than to enzyme. Notably, while RAL bound exclusively to the cleavable/cleaved site, TB11 further intercalated into DNA base pairs and interacted with the integrase-derived peptides. These unwanted binding sites explain the weaker bioavailability and higher toxicity of TB11 compared with the more effective RAL.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".