Binding Kinetics and Affinities of Heterodimeric versus Homodimeric HIV-1 Reverse Transcriptase on DNA–DNA Substrates at the Single-Molecule Level
Bibliographic record
Abstract
During viral replication, HIV-1 reverse transcriptase (RT) plays a pivotal role in converting genomic RNA into proviral DNA. While the biologically relevant form of RT is the p66-p51 heterodimer, two recombinant homodimer forms of RT, p66-p66 and p51-p51, are also catalytically active. Here we investigate the binding of the three RT isoforms to a fluorescently labeled 19/50-nucleotide primer/template DNA duplex by exploiting single-molecule protein-induced fluorescence enhancement (SM-PIFE). PIFE, which does not require labeling of the protein, allows us to directly visualize the binding/unbinding of RT to a double-stranded DNA substrate. We provide values for the association and dissociation rate constants of the RT homodimers p66-p66 and p51-p51 with a double-stranded DNA substrate and compare those to the values recorded for the RT heterodimer p66-p51. We also report values for the equilibrium dissociation constant for the three isoforms. Our data reveal great similarities in the intrinsic binding affinities of p66-p51 and p66-p66, with characteristic Kd values in the nanomolar range, much smaller (50-100-fold) than that of p51-p51. Our data also show discrepancies in the association/dissociation dynamics among the three dimeric RT isoforms. Our results further show that the apparent binding affinity of p51-p51 for its DNA substrate is to a great extent time-dependent when compared to that of p66-p66 and p66-p51, and is more likely determined by the dimer dissociation into its constituent monomers rather than the intrinsic binding affinity of dimeric RT.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".