Effects of Nucleotides and Nucleotide Analogue Inhibitors of HIV-1 Reverse Transcriptase in a Ratchet Model of Polymerase Translocation
Bibliographic record
Abstract
A single cycle of nucleotide incorporation by the reverse transcriptase of the human immunodeficiency virus type 1 (HIV-1 RT) involves the initial binding of an incoming nucleotide, a conformational change that traps the substrate, the formation of a new phosphodiester bond, the release of pyrophosphate (PPi), and ultimately polymerase translocation, which clears the nucleotide binding site. This article reviews different mechanistic models for polymerase translocation with emphasis placed on HIV-1 RT. Structure-function analyses of stalled complexes of HIV-1 RT provide strong evidence to suggest that the enzyme can oscillate between pre- and post-translocational states. Nucleotide hydrolysis is not required for the movement of the polymerase in a stalled configuration; thermal energy is sufficient to allow random bidirectional sliding. The next complementary nucleotide, following the incorporated chain-terminator, acts like a pawl of a ratchet that traps the enzyme in the post-translocation state and prevents the reverse movement. Quantitative footprinting experiments have shown that the concentration of the templated nucleotide required to shift the translocational equilibrium forward depends crucially on the structure of the 3'end of the primer. Changes in the relative population of pre- and post-translocation complexes can influence rates of excision of incorporated NRTIs, which, in turn, affects drug susceptibility. The concept of a ratchet model of HIV-1 RT translocation and its implications for drug action and resistance, and the discovery and development of novel antiviral compounds is discussed.
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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) | 0.001 | 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 | 0.001 |
| 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".