Cas12Fold Accurately Predicts Cas12 Nuclease Structures to Enable Structure‐based Genome‐editing Engineering
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".