Cas12Fold Accurately Predicts Cas12 Nuclease Structures to Enable Structure‐based Genome‐editing Engineering
Why this work is in the frame
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Bibliographic record
Abstract
Predicting the structurally diverse Cas12 nucleases remains challenging for general protein modeling algorithms, hindering rational engineering to enhance their genome‐editing capabilities. Here we present Cas12Fold, a deep learning framework tailored to Cas12 proteins. Cas12Fold leverages the deep evolutionary information from Cas12‐focused sequences and structures, and employs an iterative structure‐based alignment strategy to resolve conformational complexity. This approach achieves superior accuracy compared to existing methods in modeling key functional domains and capturing alternative conformations. Cas12Fold improves the structure predictions for previously refractory Cas12 proteins, including the phage‐encoded Casλ, a type V enzyme with extensive sequence and structural diversity. Accurate models generated by Cas12Fold enable robust inference of mechanistically critical residues. Guided by these predictions, structure‐based mutagenesis of DNA‐binding sites enhanced the genome‐editing efficiency of Cas12j.4. Cas12Fold thus provides a robust and generalizable platform for both mechanistic studies and the rational engineering of CRISPR–Cas12 systems.
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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 | 0.000 | 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) | 0.000 | 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 it