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Advancing Luciferase Activity and Stability beyond Directed Evolution and Rational Design through Expert Guided Deep

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    Engineered NanoLuc luciferase (NLuc) variants show improved thermostability and activity at high temperatures. This breakthrough uses a hybrid deep learning and rational design approach for better bioluminescence applications.

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    Area of Science:

    • Biochemistry
    • Protein Engineering
    • Molecular Biology

    Background:

    • Engineered luciferases are crucial for biological imaging and sensing.
    • Optimizing NanoLuc luciferase (NLuc) is difficult due to stability-activity trade-offs and low sequence homology.
    • Existing methods for NLuc enhancement are limited.

    Purpose of the Study:

    • To develop enhanced NLuc variants with improved thermostability and activity at elevated temperatures.
    • To integrate computational deep learning with structure-guided rational design for enzyme engineering.
    • To establish a broadly applicable framework for engineering enzymes with stability-activity constraints.

    Main Methods:

    • Systematic analysis of engineered NLuc variant libraries.
    • Integration of computational deep learning with structure-guided rational design.
    • Molecular dynamics simulations and protein folding studies.

    Main Results:

    • Developed NLuc variants (B.07, B.09) with enhanced thermostability (4.2-5.2 °C increase at 50% solubility).
    • Achieved significantly increased activity at elevated temperatures (320-370% of wild-type at 55 °C).
    • Maintained NLuc's pH tolerance and improved activity with coelenterazine.

    Conclusions:

    • Modified termini and distal loops, while preserving allosteric networks, enhance thermal resilience.
    • The developed NLuc variants offer superior tools for bioluminescence applications.
    • The hybrid methodology provides a versatile framework for engineering enzymes beyond traditional approaches.