Substrate N2 atom recognition mechanism in pierisin family DNA-targeting, guanine-specific ADP-ribosyltransferase ScARP
Bibliographic record
Abstract
ScARP from the bacterium Streptomyces coelicolor belongs to the pierisin family of DNA-targeting ADP-ribosyltransferases (ARTs). These enzymes ADP-ribosylate the N 2 amino groups of guanine residues in DNA to yield N 2 -(ADP-ribos-1-yl)-2′-deoxyguanosine. Although the structures of pierisin-1 and Scabin were revealed recently, the substrate recognition mechanisms remain poorly understood because of the lack of a substrate-binding structure. Here, we report the apo structure of ScARP and of ScARP bound to NADH and its GDP substrate at 1.50 and 1.57 Å resolutions, respectively. The bound structure revealed that the guanine of GDP is trapped between N -ribose of NADH and Trp-159. Interestingly, N 2 and N 3 of guanine formed hydrogen bonds with the OE1 and NE2 atoms of Gln-162, respectively. We directly observed that the ADP-ribosylating toxin turn-turn (ARTT)-loop, including Trp-159 and Gln-162, plays a key role in the specificity of DNA-targeting, guanine-specific ARTs as well as protein-targeting ARTs such as the C3 exoenzyme. We propose that the ARTT-loop recognition is a common substrate-recognition mechanism in the pierisin family. Furthermore, this complex structure sheds light on similarities and differences among two subclasses that are distinguished by conserved structural motifs: H-Y-E in the ARTD subfamily and R-S-E in the ARTC subfamily. The spatial arrangements of the electrophile and nucleophile were the same, providing the first evidence for a common reaction mechanism in these ARTs. ARTC (including ScARP) uses the ARTT-loop for substrate recognition, whereas ARTD (represented by Arr) uses the C-terminal helix instead of the ARTT-loop. These observations could help inform efforts to improve ART inhibitors.
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 | 0.000 | 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".