Characterization of tight-binding nonnucleoside inhibitors of HIV-1 reverse transcriptase
Bibliographic record
Abstract
5-chloro-3-(phenylsulfonyl) indole-2-carboxamide (CSIC) and clinically used efavirenz (EFV, SustivaRTM) are nonnucleoside inhibitors of HIV-1 reverse transcriptase (RT) polymerization. They have unusually low IC50 values, which are in the range of the enzyme concentration used in the assay. Their binding constants (KiCSIC = 0.1 nM, KiEFV = 8.5 nM) indicate that like UC781 (Ki = 2.4 nM) (Barnard et al., 1997), CSIC and EFV (Maga et al., 2000) are also tight-binding nonnucleoside inhibitors (TBNNI) of HIV-1 RT. As expected, TBNNI dissociate from HIV-1 RT slowly (6.4 x 10 -4--2.2 x 10-3 s-1 ). On the other hand, while we have confirmed that UC781 binds to RT rapidly (kon = 2.1 x 105 s-1M-1) (Barnard et al., 1997), CSIC and EFV undergo a slow conformational change upon initial binding to HIV-1 RT (kisomerisation forward = 3.4 x 10-3 s-1 and 5.4 x 10 -3 s-1, respectively), which leads to a tighter enzyme-inhibitor (E-I) complex. Once bound, all TBNNI are able to completely block polymerization ('dead-end' inhibition), a property that is not observed with other non-tight-binding NNRTI. 'Dead-end' inhibition may be due to the ability of TBNNI to block the conformational step preceding dNTP incorporation, unlike non-tight-binding NNRTI that slow down the ensuing chemistry step (Spence et al., 1995).
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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) | 0.003 | 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".