The dual ubiquitin binding mode of SPRTN secures rapid spatiotemporal proteolysis of DNA–protein crosslinks
Bibliographic record
Abstract
DNA-protein crosslinks (DPCs) are endogenous and chemotherapy-induced genotoxic DNA lesions and, if not repaired, lead to embryonic lethality, neurodegeneration, premature ageing, and cancer. DPCs are heavily polyubiquitinated, and the SPRTN protease and 26S proteasome emerged as two central enzymes for DPC proteolysis. The proteasome recognizes its substrates by their ubiquitination status. How SPRTN protease, an essential enzyme for DPC proteolysis, achieves specificity for DPCs is still not entirely clear. We found that the N-terminal SPRTN catalytic region (SprT) possesses a ubiquitin-binding domain that we named the Ubiquitin Interface of SprT Domain (USD). Using multiple biochemical, biophysical, and structural approaches, we reveal that USD binds ubiquitin chains in an avidity manner. SPRTN binding to ubiquitin chains via USD leads to ∼67-fold higher activation of SPRTN proteolysis towards polyubiquitinated DPCs than the unmodified DPCs. In contrast, the constitutive components of the replisome during unperturbed or translesional DNA synthesis, namely proliferating cell nuclear antigen (PCNA) or monoUb-PCNA, respectively, were poorly degraded, if at all, by SPRTN. This study reveals that the poly-ubiquitination of DPCs serves as the key signal for SPRTN's rapid proteolysis and determines its substrate specificity towards DPCs, rather than the replisome.
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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) | 0.000 | 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 | 0.000 |
| 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".