N348I in HIV-1 Reverse Transcriptase Can Counteract the Nevirapine-mediated Bias toward RNase H Cleavage during Plus-strand Initiation
Bibliographic record
Abstract
Drug resistance-associated mutations in HIV-1 reverse transcriptase (RT) can affect the balance between polymerase and ribonuclease H (RNase H) activities of the enzyme. We have recently demonstrated that the N348I mutation in the connection domain causes selective dissociation from RNase H-competent complexes, whereas the functional integrity of the polymerase-competent complex remains largely unaffected. N348I has been associated with resistance to the non-nucleoside RT inhibitor (NNRTI), nevirapine; however, a possible mechanism that links changes in RNase H activity to changes in NNRTI susceptibility remains to be established. To address this problem, we consider recent findings suggesting that NNRTIs may affect the orientation of RT on its nucleic acid substrate and increase RNase H activity. Here we demonstrate that RNase H-mediated primer removal is indeed more efficient in the presence of NNRTIs; however, the N348I mutant enzyme is able to counteract this effect. Efavirenz, a tight binding inhibitor, restricts the influence of the mutation. These findings provide strong evidence to suggest that N348I can thwart the inhibitory effects of nevirapine during initiation of (+)-strand DNA synthesis, which provides a novel mechanism for resistance. The data are in agreement with clinical data, which demonstrate a stronger effect of N348I on susceptibility to nevirapine as compared with efavirenz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".