Deep structure–function analysis of the endonuclease Mus81 with dominant mutational scanning
Bibliographic record
Abstract
Protein structure–function relationships are critical for understanding molecular mechanisms and the impacts of genetic variation. Mutational scanning approaches can deliver scalable analysis, usually through the study of loss-of-function variants. Rarer dominant negative and gain-of-function variants can be more information rich, as they retain a stable proteoform and can be used to dissect molecular function while retaining biological context. Dominant variant proteoforms can still engage substrates and interact with binding partners. Here, we probe the structure–function relationships of the Mus81 endonuclease by ectopic expression of deep mutational scanning libraries to find amino acid variants that confer dominant sensitivity to genotoxic stress and dominant synthetic lethality. Screening more than 2,200 MUS81 variants at 100 positions identified 13 amino acids that can be altered to elicit a dominant phenotype. The dominant phenotype of these variants required the presence of the obligate Mus81 binding protein, Mms4. The dominant variants affect amino acids in a contiguous surface on Mus81 and fall into two distinct classes: residues that bind the catalytic magnesium atoms and residues that form the hydrophobic wedge. Most of the variant amino acids were conserved across species and cognate variants expressed in human cell lines resulted in dominant sensitivity to replication stress and synthetic growth defects in cells lacking BLM helicase. The dominant variants in both yeast and human MUS81 resulted in phenotypes distinct from a MUS81 knockout. These data demonstrate the utility of dominant genetics using ectopic expression of amino acid site saturation variant libraries to link function to protein structure providing insight into molecular mechanisms.
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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".