Discovery of the widespread site-specific single-stranded nuclease family Ssn
Bibliographic record
Abstract
Site-specific endonucleases that exclusively cut single-stranded DNA have hitherto never been described and constitute a barrier to the development of ssDNA-based technologies. We identify and characterize one such family, from the GIY-YIG superfamily, of widely distributed site-specific single-stranded nucleases (Ssn) exhibiting unique ssDNA cleavage properties. By first comprehensively studying the Ssn homolog from Neisseria meningitidis, we demonstrate that it interacts specifically with a sequence (called NTS) present in hundreds of copies and surrounding important genes in pathogenic Neisseria. In this species, NTS/Ssn interactions modulate natural transformation and thus constitute an additional mechanism shaping genome dynamics. We further identify thousands of Ssn homologs and demonstrate, in vitro, a range of Ssn nuclease specificities for their corresponding sequence. We demonstrate proofs of concept for applications including ssDNA detection and digestion of ssDNA from RCA. This discovery and its applications set the stage for the development of innovative ssDNA-based molecular tools and technologies. Site-specific endonucleases that exclusively cut single-stranded DNA have hitherto never been described and constitute a barrier to the development of ssDNA-based technologies. Here the authors identify and characterize a family of such enzymes that could be the basis of innovative molecular tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".