Single-Stranded DNA Structure and Positional Context of the Target Cytidine Determine the Enzymatic Efficiency of AID
Bibliographic record
Abstract
Activation-induced cytidine deaminase (AID) initiates antibody diversification processes by deaminating immunoglobulin sequences. Since transcription of target genes is required for deamination in vivo and AID exclusively mutates single-stranded DNA (ssDNA) in vitro, AID has been postulated to mutate transcription bubbles. However, since ssDNA generated by transcription can assume multiple structures, it is unknown which of these are targeted in vivo. Here we examine the enzymatic and binding properties of AID for different DNA structures. We report that AID has minimal activity on stem-loop structures and preferentially deaminates five-nucleotide bubbles. We compared AID activity on cytidines placed at various distances from the single-stranded/double-stranded DNA junction of bubble substrates and found that the optimal target consists of a single-stranded NWRCN motif. We also show that high-affinity binding is required for but does not necessarily lead to efficient deamination. Using nucleotide analogues, we show that AID's WRC preference (W = A or T; R = A or G) involves the recognition of a purine in the R position and that the carbonyl or amino side chains of guanosine negatively influence specificity at the W position. Our results indicate that AID is likely to target short-tract regions of ssDNA produced by transcription elongation and that it requires a fully single-stranded WRC motif.
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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 | 0.001 |
| 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".