Making Enzymes Suitable for Organic Chemistry by Rational Protein Design

Abstract

This review outlines recent developments in protein engineering of stereo- and regioselective enzymes, which are of prime interest in organic and pharmaceutical chemistry as well as biotechnology. The widespread application of enzymes was hampered for decades due to limited enantio-, diastereo- and regioselectivity, which was the reason why most organic chemists were not interested in biocatalysis. This attitude began to change with the advent of semi-rational directed evolution methods based on focused saturation mutagenesis at sites lining the binding pocket. Screening constitutes the labor-intensive step (bottleneck), which is the reason why various research groups are continuing to develop techniques for the generation of small and smart mutant libraries. Rational enzyme design, traditionally an alternative to directed evolution, provides small collections of mutants which require minimal screening. This approach first focused on thermostabilization, and did not enter the field of stereoselectivity until later. Computational guides such as the Rosetta algorithms, HotSpot Wizard metric, and machine learning (ML) contribute significantly to decision making. The newest advancements show that semi-rational directed evolution such as CAST/ISM and rational enzyme design no longer develop on separate tracks, instead, they have started to merge. Indeed, researchers utilizing the two approaches have learned from each other. Today, the toolbox of organic chemists includes enzymes, primarily because the possibility of controlling stereoselectivity by protein engineering has ensured reliability when facing synthetic challenges. This review was also written with the hope that undergraduate and graduate education will include enzymes more so than in the past.

Keywords: enzyme stereoselectivity; process engineering; rational enzyme design; saturation mutagenesis; semi-rational directed evolution.

Figure 1

CAST/ISM schemes. ^[2d] a) Schematic representation of CAST sites. b) Illustration indicating that only a limited number of CAST sites (red‐marked) generally need to be considered for obtaining excellent results. c) Schematic representation of ISM showing the number of upward pathways when choosing 2, 3 or 4 CAST sites, each composed of a single or of several residues. SM: Saturation mutagenesis: SSM: Site‐saturation mutagenesis; CSM: Combinatorial saturation mutagenesis.

Figure 2

The innov'SAR workflow used in the ML‐based prediction of enantioselective ANEH variants as catalysts in the hydrolytic kinetic resolution of glycidyl phenyl ether (fitness defined by the enantioselectivity factor E).

Figure 3

Biocatalytic stereoselective Diels–Alder cycloaddition achieved by rational design using Rosetta algorithm. Reproduced with permission from Ref. [35]. Copyright 2010, The American Association for the Advancement of Science.

Figure 4

The utility of RosettaRemodel for a variety of protein backbone manipulations. ^[38a] The crystal structure of the model protein G (PDB ID: 1PGA) served as the starting point of the different cases. The colored regions indicate places of predictions made by RosettaRemodel.

Figure 5

Illustration of Focused Rational Iterative Site‐specific Mutagenesis (FRISM).^[2d] a) FRISM with 2 mutational sites; b) ISM with 3 mutational sites, 2 of the 6 possible pathways being shown.