Study of binding stoichiometries of the human immunodeficiency virus type 1 reverse transcriptase by capillary electrophoresis and laser‐induced fluorescence polarization using aptamers as probes
Bibliographic record
Abstract
Binding stoichiometries between four DNA aptamers (RT12, RT26, RTlt49, and ODN93) and the reverse transcriptase (RT) of the type 1 human immunodeficiency virus (HIV-1) were studied using affinity CE (ACE) coupled with LIF polarization and fluorescence polarization (FP). The ACE/LIF study showed evidence of two binding stoichiometries between the HIV-1 RT protein and aptamers RT12, RT26, and ODN93, suggesting that these aptamers can bind to both the p66 and p51 subunits of the HIV-1 RT. Only one binding stoichiometry for aptamer RTlt49 was found. The affinity complexes were easily separated from the unbound aptamers; however, the different stoichiometries were not well resolved. A complementary technique, FP, was able to provide additional information about the binding and supporting evidence for the ACE/LIF results. The ACE/LIFP study also revealed that the FP values of the 1:1 complexes of the HIV-1 RT protein with aptamers RT12, RT26, and ODN93 were always much greater than those of the 1:2 complexes. This was initially surprising because the larger molecular size of the 1:2 complexes was expected to result in higher FP values than the corresponding 1:1 complexes. This phenomenon was probably a result of fluorescence resonance energy transfer between the two fluorescent molecules bound to the HIV-1 RT protein.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".