Comparative analysis of the gene-inactivating potential of retroviral restriction factors APOBEC3F and APOBEC3G
Bibliographic record
Abstract
APOBEC3 (A3) proteins are host-encoded restriction factors that inhibit retrovirus infection by mutagenic deamination of cytosines in minus-strand DNA replication intermediates. APOBEC3F (A3F) and APOBEC3G (A3G) are two of the most potent A3 enzymes in humans with each having a different target DNA specificity. A3G prefers to deaminate cytosines preceded by a cytosine (5'-CC), whereas A3F preferentially targets cytosines preceded by a thymine (5'-TC). Here we performed a detailed comparative analysis of retrovirus-encoded gene sequences edited by A3F and A3G, with the aim of correlating the context and intensity of the mutations with their effects on gene function. Our results revealed that, when there are few (TGG) tryptophan codons in the sequence, both enzymes alter gene function with a similar efficiency when given equal opportunities to deaminate in their preferred target DNA context. In contrast, tryptophan-rich genes are efficiently inactivated in the presence of a low mutational burden, through termination codon generation by A3G but not A3F. Overall, our results clearly demonstrated that the target DNA specificity of an A3 enzyme along with the intensity of the mutational burden and the tryptophan content of the gene being targeted are the factors that have the most forceful influence on whether A3-induced mutations will favour either terminal inactivation or genetic diversification of a retrovirus.
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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) | 0.000 | 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".