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Substrate Generation for Endonucleases of CRISPR/Cas Systems
Published on: September 8, 2012
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Customizing CRISPR-Cas PAM specificity with protein language models
Stephen Nayfach1, Aadyot Bhatnagar2, Andrey Novichkov2
1Profluent Bio, Emeryville, CA, USA. snayfach@profluent.bio.
Nature Biotechnology
|February 2, 2026
Summary
Scientists developed Protein2PAM, a deep learning model that efficiently designs CRISPR-Cas variants for specific DNA targets. This advances genome editing by overcoming protospacer-adjacent motif limitations.
Area of Science:
- CRISPR-Cas gene editing technology
- Bioinformatics and computational biology
- Protein engineering and directed evolution
Background:
- CRISPR-Cas systems require protospacer-adjacent motifs (PAMs) for DNA targeting, restricting the range of editable genomic sites.
- Current methods for engineering PAM specificity are often laborious and time-consuming, involving iterative experimental steps.
Purpose of the Study:
- To introduce an evolution-informed deep learning model, Protein2PAM, for efficient design of Cas protein variants with altered PAM specificity.
- To enable precise targeting of previously inaccessible genomic sequences by overcoming PAM constraints in CRISPR-Cas systems.
Main Methods:
- Developed and trained Protein2PAM on a large dataset (>45,000) of CRISPR-Cas PAM sequences.
- Utilized in silico mutagenesis to identify key residues for PAM recognition in Cas9, without relying on structural data.
- Employed Protein2PAM for computational evolution of Nme1Cas9 variants.
Main Results:
- Protein2PAM accurately predicts PAM specificity across diverse CRISPR-Cas types (I, II, V).
- Identified critical residues for PAM recognition in Cas9 using computational methods.
- Generated Nme1Cas9 variants with significantly broadened PAM recognition and up to 50-fold increased in vitro cleavage rates.
Conclusions:
- Protein2PAM offers an efficient, machine learning-driven approach to engineer CRISPR-Cas enzymes for specific PAM recognition.
- This technology expands the targeting capabilities of CRISPR-Cas systems, enhancing flexibility for personalized genome editing applications.
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