Active site residue identity regulates cleavage preference of LAGLIDADG homing endonucleases
Bibliographic record
Abstract
LAGLIDADG homing endonucleases (meganucleases) are site-specific mobile endonucleases that can be adapted for genome-editing applications. However, one problem when reprogramming meganucleases on non-native substrates is indirect readout of DNA shape and flexibility at the central 4 bases where cleavage occurs. To understand how the meganuclease active site regulates DNA cleavage, we used functional selections and deep sequencing to profile the fitness landscape of 1600 I-LtrI and I-OnuI active site variants individually challenged with 67 substrates with central 4 base substitutions. The wild-type active site was not optimal for cleavage on many substrates, including the native I-LtrI and I-OnuI targets. Novel combinations of active site residues not observed in known meganucleases supported activity on substrates poorly cleaved by the wild-type enzymes. Strikingly, combinations of E or D substitutions in the two metal-binding residues greatly influenced cleavage activity, and E184D variants had a broadened cleavage profile. Analyses of I-LtrI E184D and the wild-type proteins co-crystallized with the non-cognate AACC central 4 sequence revealed structural differences that correlated with kinetic constants for cleavage of individual DNA strands. Optimizing meganuclease active sites to enhance cleavage of non-native central 4 target sites is a straightforward addition to engineering workflows that will expand genome-editing applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".