Protein Engineering by Efficient Sequence Space Exploration Through Combination of Directed Evolution and Computational Design Methodologies

Protein Engineering, pp. 153–176

Abstract

Directed evolution has matured in academia and industry as a versatile algorithm to redesign enzymes to match demands in biotechnological applications (as documented by the Nobel Prize in chemistry in 2018). Based on the obtained knowledge, computational methods (e.g. FRESCO, FoldX, CNA, PROSS, ProSAR) emerged to be predictive methods to especially improve properties that could be localized within a protein (e.g. thermostability, selectivity, catalytic efficiency, and activity). The main limitation to efficiently explore and benefit from nature's potential in generating better enzymes is the size of the protein sequence space; experimentalists have to admit that they will never be able to experimentally sample through the whole sequence space. A combination of experimental and computational methods proved to be time efficient in redesigning enzymes to meet the application demands (e.g. in chemical and pharmaceuticals synthesis). In this chapter, we highlighted protein engineering strategies that combine directed evolution and computational analysis to efficiently reengineer enzymes and that partly contribute to a molecular understanding of structure function relationship, which can be transferred from enzymes to another. In this respect, an emphasis will be given to the “KnowVolution” (knowledge gaining directed evolution) strategy, which is generally applicable, minimizes experimental efforts, generates a molecular understanding on each positions/amino acid substitution, and was successfully applied to a broad range of and enzymes and properties.

Authors 4

1. Subrata Pramanik Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute of Biotechnology, Worringerweg 3, 52074 Aachen, Germany

2. Francisca Contreras Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute of Biotechnology, Worringerweg 3, 52074 Aachen, Germany

3. Mehdi D. Davari Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Institute of Biotechnology, Worringerweg 3, 52074 Aachen, Germany

4. Ulrich Schwaneberg Aachen DWI - Leibniz Institute for Interactive Materials Institute of Biotechnology

   DWI – Leibniz Institute for Interactive Materials · RWTH Aachen University

   Affiliation as printed

   DWI – Leibniz Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany

   RWTH Aachen University, Institute of Biotechnology, Worringerweg 3, 52074 Aachen, Germany