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Structure and evolution-guided design of minimal RNA-guided nucleases.

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    Scientists developed an AI strategy to design novel RNA-guided nucleases beyond evolutionary limits. This approach created highly active genome editors for diverse applications, expanding protein design possibilities.

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    Area of Science:

    • Protein engineering
    • Synthetic biology
    • Genomics

    Background:

    • Designing novel RNA-guided nucleases with enhanced properties is crucial for advancing programmable genome editing.
    • Generating diverse, enzymatically robust multi-domain proteins presents a significant challenge in protein engineering.

    Purpose of the Study:

    • To develop an AI-driven strategy for designing active, divergent variants of TnpB, a minimal CRISPR-Cas12-like nuclease.
    • To expand the capabilities of programmable genome editing by creating nucleases with properties not constrained by natural evolution.

    Main Methods:

    • Employed an artificial intelligence strategy combining structure-guided inverse protein folding and evolution-informed residue constraints.
    • Generated and functionally screened a library of AI-designed TnpB variants using high-throughput methods.
    • Determined the structure of a highly active variant using cryo-electron microscopy (Cryo-EM).

    Main Results:

    • AI-generated variants of TnpB demonstrated retained or enhanced nuclease activity compared to wild-type in bacterial, plant, and human cells.
    • The most divergent active variant exhibited new stabilizing contacts at RNA/DNA interfaces across different conformational states.
    • Cryo-EM analysis revealed insights into the structural basis of enhanced activity and stability.

    Conclusions:

    • The study establishes a powerful strategy for designing non-natural RNA-guided nucleases with tailored properties.
    • This AI-driven approach significantly enlarges the designable protein space for nucleic acid binders and editing tools.
    • The findings demonstrate the potential for creating novel biological tools beyond natural evolutionary constraints.