Impact of Primer-induced Conformational Dynamics of HIV-1 Reverse Transcriptase on Polymerase Translocation and Inhibition
Bibliographic record
Abstract
The rapid emergence and the prevalence of resistance mutations in HIV-1 reverse transcriptase (RT) underscore the need to identify RT inhibitors with novel binding modes and mechanisms of inhibition. Recently, two structurally distinct inhibitors, phosphonoformic acid (foscarnet) and INDOPY-1 were shown to disrupt the translocational equilibrium of RT during polymerization through trapping of the enzyme in the pre- and the post-translocation states, respectively. Here, we show that foscarnet and INDOPY-1 additionally display a shared novel inhibitory preference with respect to substrate primer identity. In RT-catalyzed reactions using RNA-primed substrates, translocation inhibitors were markedly less potent at blocking DNA polymerization than in equivalent DNA-primed assays; i.e. the inverse pattern observed with marketed non-nucleoside inhibitors that bind the allosteric pocket of RT. This potency profile was shown to correspond with reduced binding on RNA·DNA primer/template substrates versus DNA·DNA substrates. Furthermore, using site-specific footprinting with chimeric RNA·DNA primers, we demonstrate that the negative impact of the RNA primer on translocation inhibitor potency is overcome after 18 deoxyribonucleotide incorporations, where RT transitions primarily into polymerization-competent binding mode. In addition to providing a simple means to identify similarly acting translocation inhibitors, these findings suggest a broader role for the primer-influenced binding mode on RT translocation equilibrium and inhibitor sensitivity.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".