Scabin, a Novel DNA-acting ADP-ribosyltransferase from Streptomyces scabies
Bibliographic record
Abstract
A bioinformatics strategy was used to identify Scabin, a novel DNA-targeting enzyme from the plant pathogen 87.22 strain of Streptomyces scabies Scabin shares nearly 40% sequence identity with the Pierisin family of mono-ADP-ribosyltransferase toxins. Scabin was purified to homogeneity as a 22-kDa single-domain enzyme and was shown to possess high NAD(+)-glycohydrolase (Km (NAD) = 68 ± 3 μm; kcat = 94 ± 2 min(-1)) activity with an RSQXE motif; it was also shown to target deoxyguanosine and showed sigmoidal enzyme kinetics (K0.5(deoxyguanosine) = 302 ± 12 μm; kcat = 14 min(-1)). Mass spectrometry analysis revealed that Scabin labels the exocyclic amino group on guanine bases in either single-stranded or double-stranded DNA. Several small molecule inhibitors were identified, and the most potent compounds were found to inhibit the enzyme activity with Ki values ranging from 3 to 24 μm PJ34, a well known inhibitor of poly-ADP-ribosyltransferases, was shown to be the most potent inhibitor of Scabin. Scabin was crystallized, representing the first structure of a DNA-targeting mono-ADP-ribosyltransferase enzyme; the structures of the apo-form (1.45 Å) and with two inhibitors (P6-E, 1.4 Å; PJ34, 1.6 Å) were solved. These x-ray structures are also the first high resolution structures of the Pierisin subgroup of the mono-ADP-ribosyltransferase toxin family. A model of Scabin with its DNA substrate is also proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".