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Published on: September 25, 2021
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Uncovering Cas9 PAM diversity through metagenomic mining and machine learning
Tao Fang1, Lea Bogensperger2, Lilith Feer2
1Institute of Pharmacology and Toxicology, University of Zurich, Zürich, Switzerland.
Nature Communications
|February 8, 2026
Summary
We developed CRISPR-PAMdb and CICERO to discover new CRISPR-Cas9 PAM sequences. This expands genome editing capabilities by identifying novel Cas9 orthologs with diverse PAM compatibilities.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Protospacer adjacent motif (PAM) recognition is essential for CRISPR-Cas systems.
- Limited PAM compatibility of current Cas9 variants restricts genome editing applications.
- Discovering novel Cas9 orthologs with expanded PAM recognition is critical.
Purpose of the Study:
- To create a comprehensive database of Cas9 proteins and their PAM profiles.
- To develop a machine learning model for predicting PAM preferences from Cas9 sequences.
- To facilitate the discovery of new CRISPR-Cas9 tools for precise genome engineering.
Main Methods:
- Compiled Cas9 sequences from millions of genomes and PAM profiles from phage/plasmid sequences into CRISPR-PAMdb.
- Inferred consensus PAM preferences using spacer-protospacer alignment for 8003 Cas9 clusters.
- Developed CICERO, a machine learning model based on ESM2, to predict PAMs from Cas9 sequences.
Main Results:
- CRISPR-PAMdb contains extensive Cas9 and PAM data.
- Alignment-based methods identified PAMs for 8003 Cas9 clusters.
- CICERO predicted PAMs for 50,308 additional Cas9 proteins, achieving high accuracy on validated orthologs.
Conclusions:
- CRISPR-PAMdb and CICERO enable large-scale exploration of Cas9 PAM diversity.
- These resources accelerate the design of next-generation CRISPR-Cas9 tools.
- The findings support enhanced precision in genome engineering applications.
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