# CasMiner: a deep-learning tool for high-throughput mining and rational design of efficient Cas9

## TL;DR

CasMiner is a deep-learning tool that helps discover and improve Cas9 proteins for more effective genome editing.

## Contribution

CasMiner introduces a deep-learning model for mining and engineering Cas9 proteins with high accuracy and efficiency.

## Key findings

- CasMiner identified VpCas9 with 99.63% accuracy and confirmed its strong double-strand cleavage activity.
- Engineered mutants of VpCas9 showed increased structural rigidity and higher editing efficiency in rice and maize.
- The mutant VPM2-3 achieved editing efficiency comparable to SpCas9 in plant cells.

## Abstract

Since its inception, the CRISPR-Cas system, particularly Cas9, has demonstrated immense potential for life science applications, but expansion of the Cas9 toolkit is constrained by sequence-alignment-based strategies for mining and optimization. Here, we developed CasMiner—a deep-learning model for discovering and engineering novel Cas9 proteins. CasMiner achieved 99.63% accuracy in predicting Cas9s and identified VpCas9 from public databases. Experimental validation showed that VpCas9 exhibits robust double-strand cleavage activity. Combining CasMiner and evolutionary analysis, we engineered three mutants with markedly increased structural rigidity and positive charge. In vivo cleavage assays revealed that the mutant VPM2-3 achieved a higher average editing efficiency in rice callus and maize protoplasts than the wild-type VpCas9, the editing efficiency of which rivals that of SpCas9. This study thus establishes a comprehensive platform for mining and engineering Cas9 proteins, and provides VpCas9 and derivative nucleases as powerful tools that greatly broaden the horizon for genome-editing applications.