Evolutionary Comparative Analyses of DNA-Editing Enzymes of the Immune System: From 5-Dimensional Description of Protein Structures to Immunological Insights and Applications to Protein Engineering
Bibliographic record
Abstract
The immune system is unique among all biological sub-systems in its usage of DNA-editing enzymes to introduce targeted gene mutations and double-strand DNA breaks to diversify antigen receptor genes and combat viral infections. These processes, initiated by specific DNA-editing enzymes, often result in mistargeted induction of genome lesions that initiate and drive cancers. Like other molecules involved in human health and disease, the DNA-editing enzymes of the immune system have been intensively studied in humans and mice, with little attention paid (< 1% of published studies) to the same enzymes in evolutionarily distant species. Here, we present a systematic review of the literature on the characterization of one such DNA-editing enzyme, activation-induced cytidine deaminase (AID), from an evolutionary comparative perspective. The central thesis of this review is that although the evolutionary comparative approach represents a minuscule fraction of published works on this and other DNA-editing enzymes, this approach has made significant impacts across the fields of structural biology, immunology, and cancer research. Using AID as an example, we highlight the value of the evolutionary comparative approach in discoveries already made, and in the context of emerging directions in immunology and protein engineering. We introduce the concept of 5-dimensional (5D) description of protein structures, a more nuanced view of a structure that is made possible by evolutionary comparative studies. In this higher dimensional view of a protein’s structure, the classical 3-dimensional (3D) structure is integrated in the context of real-time conformations and evolutionary time shifts (4thdimension) and the relevance of these dynamics to its biological function (5thdimension).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".