AID/APOBEC3 Dynamic Catalytic Pockets and AID-α7 Regulation Mechanisms
Bibliographic record
Abstract
AID/APOBEC3s mutate dC to dU in ssDNA in the restriction of viruses, creation of antibodies, and when off-target affecting cancers. Little was known of the catalytic pocket dynamics of these enzymes in real time. As such, we utilised molecular dynamics analyses to determine catalytic pocket volumes and states. We found that most AID/APOBECs remain predominantly in either a closed or indeterminate state to varying degrees while A3G-CD2 is predominantly open. This occurs chiefly from the catalytic residues themselves and the loop 1 serine/threonine in the "floor" of the catalytic pocket while other residues of the secondary catalytic loops 1, 3, 5, and 7 aid in these states. Surprisingly, we found that the presupposed stable position of AID's unique α7 (β-state) in contact with the β-sheet and α6 was unstable while the α-state in contact with α6 and loop7 was stable and caused pocket closure. Mutations could cause the destabilisation of the α-state (R171Y and R178D) or stabilisation of the β-state (R190D and R194A). Thus, pocket states are relatively similar in overall cause; however, multiple residues from sec-ondary catalytic loops as well as AID-α7 determine the rates at which these states occur, potentially affecting the chances any AID/APOBEC is able to have catalysis.
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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 | 0.001 | 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".