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Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
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Pareto Optimization of Combinatorial Mutagenesis Libraries.

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    We developed POCoM, a computational method to optimize protein variant libraries for multiple desired properties. This approach accelerates the discovery of beneficial protein variants through efficient, multi-objective library design.

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    Area of Science:

    • Biochemistry
    • Computational Biology
    • Protein Engineering

    Background:

    • High-throughput screening aims to discover beneficial protein variants.
    • Optimizing combinatorial mutagenesis libraries is crucial for efficient discovery.
    • Balancing multiple properties in protein variants presents a significant challenge.

    Purpose of the Study:

    • To develop a computational method for optimizing combinatorial mutagenesis libraries for multiple properties.
    • To enable the design of libraries that balance diverse objectives, such as structural and sequence information.
    • To accelerate the discovery of beneficial protein variants through improved library design.

    Main Methods:

    • Developed Pareto Optimal Combinatorial Mutagenesis (POCoM), a computational method for library design.
    • POCoM scores entire libraries based on average variant properties and designs optimal combinations.
    • Instantiated POCoM using scoring functions based on protein structure and homolog sequences.

    Main Results:

    • POCoM efficiently designs large, multi-objective combinatorial libraries in hours.
    • Demonstrated POCoM's effectiveness on green fluorescent protein, cytochrome P450, and β-lactamase.
    • Analyzed trade-offs between structure- and sequence-based scores and experimental constraints.

    Conclusions:

    • POCoM is the first general-purpose method for designing combinatorial libraries for multiple objectives.
    • POCoM facilitates the discovery of diverse, beneficial protein variants by balancing multiple properties.
    • POCoM libraries incorporate known beneficial mutations in novel contexts while maintaining variant quality.