LigD: A Structural Guide to the Multi-Tool of Bacterial Non-Homologous End Joining
Bibliographic record
Abstract
DNA double-strand breaks are the most lethal form of damage for living organisms. The non-homologous end joining (NHEJ) pathway can repair these breaks without the use of a DNA template, making it a critical repair mechanism when DNA is not replicating, but also a threat to genome integrity. NHEJ requires proteins to anchor the DNA double-strand break, recruit additional repair proteins, and then depending on the damage at the DNA ends, fill in nucleotide gaps or add or remove phosphate groups before final ligation. In eukaryotes, NHEJ uses a multitude of proteins to carry out processing and ligation of the DNA double-strand break. Bacterial NHEJ, though, accomplishes repair primarily with only two proteins-Ku and LigD. While Ku binds the initial break and recruits LigD, it is LigD that is the primary DNA end processing machinery. Up to three enzymatic domains reside within LigD, dependent on the bacterial species. These domains are a polymerase domain, to fill in nucleotide gaps with a preference for ribonucleotide addition; a phosphoesterase domain, to generate a 3'-hydroxyl DNA end; and the ligase domain, to seal the phosphodiester backbone. To date, there are no experimental structures of wild-type LigD, but there are x-ray and nuclear magnetic resonance structures of the individual enzymatic domains from different bacteria and archaea, along with structural predictions of wild-type LigD via AlphaFold. In this review, we will examine the structures of the independent domains of LigD from different bacterial species and the contributions these structures have made to understanding the NHEJ repair mechanism. We will then examine how the experimental structures of the individual LigD enzymatic domains combine with structural predictions of LigD from different bacterial species and postulate how LigD coordinates multiple enzymatic activities to carry out DNA double-strand break repair in bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.074 |
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".