Computational Enzyme Design at Zymvol

Directed evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.

Computational-Designed Enzyme for β-Tyrosine Production in Lignin Valorization

Lignin is an underutilized sustainable source of aromatic compounds. To valorize the low-value lignin monomers, we proposed an efficient strategy, involving enzymatic conversion from trans-p-hydroxycinnamic acids to generate valued-added canonical and non-canonical aromatic amino acids. Among them, β-amino acids are recognized as building blocks for bioactive natural products and pharmaceutical ingredients due to their attractive antitumor properties. Using computational enzyme design, the (R)-β-selective phenylalanine aminomutase from Taxus chinensis (TchPAM) was successfully mutated to accept β-tyrosine as the substrate, as well as to generate the (R)-β-tyrosine with excellent enantiopurity (ee > 99%) as the unique product from trans-p-hydroxycinnamic acid. Moreover, the kinetic parameters were determined for the reaction of four Y424 enzyme variants with the synthesis of different phenylalanine and tyrosine enantiomers. In the ammonia elimination reaction of (R)-β-tyrosine, the variants Y424N and Y424C displayed a two-fold increased catalytic efficiency of the wild type. In this work, a binding pocket in the active site, including Y424, K427, I431, and E455, was examined for its influence on the β-enantioselectivity of this enzyme family. Combining the upstream lignin depolymerization and downstream production, a sustainable value chain based on lignin is enabled. In summary, we report a β-tyrosine synthesis process from a monolignol component, offering a new way for lignin valorization by biocatalyst modification.

Rational Prediction of Distal Activity-Enhancing Mutations in Tryptophan Synthase

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood, but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. In particular, we predict positions crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the new conformationally-driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design can be ultimately addressed.

Rational Prediction of Distal Activity-Enhancing Mutations in Tryptophan Synthase

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood, but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. In particular, we predict positions crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the new conformationally-driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design can be ultimately addressed.

Current computational methods for enzyme design

Computational enzyme design has made great strides over the last five years. Traditional methods of enzyme design require synthesis and evaluation of many mutations. Computational enzyme design has emerged as a powerful tool to predict how specific mutations modify a protein’s activity, stability, and/or selectivity. Such computational approaches can evaluate many mutations and reduce the load of in vitro work by identifying mutations likely to accomplish design objectives. Computational approaches can explore mutational spaces inaccessible in traditional mutagenesis. Computational methods reduce cost and time compared with experimental approaches. We review the efficacy and key differences of computational enzyme design methods as published in recent studies. The included articles used computational methods to design enzymes, were published no earlier than 2015, met design objectives, and verified results in vitro.

Web-based Tools for Computational Enzyme Design

Enzymes are on high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.