Mapping the native interaction surfaces of PREP1 with PBX1 by cross-linking mass-spectrometry and mutagenesis
Bibliographic record
Abstract
Both onco-suppressor PREP1 and the oncogene MEIS1 bind to PBX1. This interaction stabilizes the two proteins and allows their translocation into the nucleus and thus their transcriptional activity. Here, we have combined cross-linking mass-spectrometry and systematic mutagenesis to detail the binding geometry of the PBX1-PREP1 (and PBX1-MEIS1) complexes, under native in vivo conditions. The data confirm the existence of two distinct interaction sites within the PBC domain of PBX1 and unravel differences among the highly similar binding sites of MEIS1 and PREP1. The HR2 domain has a fundamental role in binding the PBC-B domain of PBX1 in both PREP1 and MEIS1. The HR1 domain of MEIS1, however, seem to play a less stringent role in PBX1 interaction with respect to that of PREP1. This difference is also reflected by the different binding affinity of the two proteins to PBX1. Although partial, this analysis provides for the first time some ideas on the tertiary structure of the complexes not available before. Moreover, the extensive mutagenic analysis of PREP1 identifies the role of individual hydrophobic HR1 and HR2 residues, both in vitro and in vivo.
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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) | 0.000 | 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".