HIV-1 Reverse Transcriptase (RT) Polymorphism 172K Suppresses the Effect of Clinically Relevant Drug Resistance Mutations to Both Nucleoside and Non-nucleoside RT Inhibitors
Bibliographic record
Abstract
Polymorphisms have poorly understood effects on drug susceptibility and may affect the outcome of HIV treatment. We have discovered that an HIV-1 reverse transcriptase (RT) polymorphism (RT(172K)) is present in clinical samples and in widely used laboratory strains (BH10), and it profoundly affects HIV-1 susceptibility to both nucleoside (NRTIs) and non-nucleoside RT inhibitors (NNRTIs) when combined with certain mutations. Polymorphism 172K significantly suppressed zidovudine resistance caused by excision (e.g. thymidine-associated mutations) and not by discrimination mechanism mutations (e.g. Q151M complex). Moreover, it attenuated resistance to nevirapine or efavirenz imparted by NNRTI mutations. Although 172K favored RT-DNA binding at an excisable pre-translocation conformation, it decreased excision by thymidine-associated mutation-containing RT. 172K affected DNA handling and decreased RT processivity without significantly affecting the k(cat)/K(m) values for dNTP. Surface plasmon resonance experiments revealed that RT(172K) decreased DNA binding by increasing the dissociation rate. Hence, the increased zidovudine susceptibility of RT(172K) results from its increased dissociation from the chain-terminated DNA and reduced primer unblocking. We solved a high resolution (2.15 Å) crystal structure of RT mutated at 172 and compared crystal structures of RT(172R) and RT(172K) bound to NNRTIs or DNA/dNTP. Our structural analyses highlight differences in the interactions between α-helix E (where 172 resides) and the active site β9-strand that involve the YMDD loop and the NNRTI binding pocket. Such changes may increase dissociation of DNA, thus suppressing excision-based NRTI resistance and also offset the effect of NNRTI resistance mutations thereby restoring NNRTI binding.
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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) | 0.001 | 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".