Crystallographic fragment screening reveals ligand hotspots in TRIM21 PRY-SPRY domain
Bibliographic record
Abstract
Tripartite motif-containing protein 21 (TRIM21), and particularly its PRY-SPRY protein interaction domain, plays a critical role in the immune response by recognizing intracellular antibodies targeting them for degradation. In this study, we performed a crystallographic fragment screening (CFS) campaign to identify potential small molecule binders targeting the PRY-SPRY domain of TRIM21. Our screen identified a total of 109 fragments binding to TRIM21 that were distributed across five distinct binding sites. These fragments have been designed to facilitate straightforward follow-up chemistry, making them ideal starting points for further chemical optimization. A subsequent fragment merging approach demonstrated improved activity. To enable functional validation of compounds with full length human TRIM21, we established a NanoBRET assay suitable for measuring target engagement to the main Fc binding site in life cells. The high-resolution structural data and observed binding modes across the different sites highlight the versatility of the PRY-SPRY domain as a target for small-molecule intervention. The presented data provide a solid foundation for structure-guided ligand design, enabling the rational design of more potent and selective compounds, with the goal to develop bivalent molecules such as Proteolysis Targeting Chimeras (PROTACs).
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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) | 0.002 | 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".