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Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

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Directed Enzyme Evolution: Advances and Applications

Abstract

Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.

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Authors and Affiliations

  1. Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Jordi Girona 29, 08034, Barcelona, Spain

    Emanuele Monza, Sandra Acebes, M. Fátima Lucas & Victor Guallar PhD

  2. Anaxomics Biotech, Balmes 89, 08008, Barcelona, Spain

    M. Fátima Lucas

  3. ICREA, Passeig Lluís Companys 23, 08010, Barcelona, Spain

    Victor Guallar PhD

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  1. Emanuele Monza
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  2. Sandra Acebes
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  3. M. Fátima Lucas
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  4. Victor Guallar PhD
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Correspondence to Victor Guallar PhD .

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  1. Department of Biocatalysis, Institute of Catalysis and Petrochemistry, CSIC, Madrid, Spain

    Miguel Alcalde

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Monza, E., Acebes, S., Lucas, M.F., Guallar, V. (2017). Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution. In: Alcalde, M. (eds) Directed Enzyme Evolution: Advances and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-50413-1_10

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