<p>Proteins are essential components in synthetic biology, providing multiple functions at the nanoscale. Newly developed protein optimization and design tools allow the generation of proteins with desired properties, offering new opportunities for the engineering of protein-based biological systems. In this Review, we explore how bottom-up synthetic biology, with its aim to construct synthetic cells, can use these tools to devise complex biological functions and functional systems from scratch. We provide an overview of current capabilities in protein optimization, de novo protein design and iterative system optimization, and discuss their potential in synthetic cell science with regard to standardization, the generation of missing functionality and integration. We conclude with the outline of an integrated pipeline that combines protein engineering, automated synthetic cell generation and active learning, which might allow the design of entirely new biological systems that do not rely on naturally evolved protein components.</p>

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Protein design and optimization for synthetic cells

  • Béla P. Frohn,
  • Shunshi Kohyama,
  • Petra Schwille

摘要

Proteins are essential components in synthetic biology, providing multiple functions at the nanoscale. Newly developed protein optimization and design tools allow the generation of proteins with desired properties, offering new opportunities for the engineering of protein-based biological systems. In this Review, we explore how bottom-up synthetic biology, with its aim to construct synthetic cells, can use these tools to devise complex biological functions and functional systems from scratch. We provide an overview of current capabilities in protein optimization, de novo protein design and iterative system optimization, and discuss their potential in synthetic cell science with regard to standardization, the generation of missing functionality and integration. We conclude with the outline of an integrated pipeline that combines protein engineering, automated synthetic cell generation and active learning, which might allow the design of entirely new biological systems that do not rely on naturally evolved protein components.