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Substrate Generation for Endonucleases of CRISPR/Cas Systems
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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.

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|February 2, 2026
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Summary
This summary is machine-generated.

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.

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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.