Deamination hotspots among APOBEC3 family members are defined by both target site sequence context and ssDNA secondary structure
Bibliographic record
Abstract
The human apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3 (APOBEC3, A3) family member proteins can deaminate cytosines in single-strand (ss) DNA, which restricts human immunodeficiency virus type 1 (HIV-1), retrotransposons, and other viruses such as hepatitis B virus, but can cause a mutator phenotype in many cancers. While structural information exists for several A3 proteins, the precise details regarding deamination target selection are not fully understood. Here, we report the first parallel, comparative analysis of site selection of A3 deamination using six of the seven purified A3 member enzymes, oligonucleotides having 5'TC3' or 5'CT3' dinucleotide target sites, and different flanking bases within diverse DNA secondary structures. A3A, A3F and A3H were observed to have strong preferences toward the TC target flanked by A or T, while all examined A3 proteins did not show a preference for a TC target flanked by a G. We observed that the TC target was strongly preferred in ssDNA regions rather than dsDNA, loop or bulge regions, with flanking bases influencing the degree of preference. CT was also shown to be a potential deamination target. Taken together, our observations provide new insights into A3 enzyme target site selection and how A3 mutagenesis impacts mutation rates.
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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".