Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

Abstract

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Keywords: biofuel; biomanufacturing; computational; design; engineering; enzyme; metabolism; microbes.

FIGURE 1

Experimental protein engineering strategies and an idealized scheme for a design–test–build–learn cycle of optimizing enzymes using computation. (A) Exemplified workflow for low- and medium-throughput enzyme engineering strategies. (B) Exemplified workflow for high- and ultrahigh-throughput enzyme engineering strategies. (C) Design–test–build–learn cycle for industrial chemical production including computational methodology and production–consumption–recycling cycle of chemical usage. Promising enzyme variants are identified computationally, which leads to targeted experimental testing. The metabolic systems are then applied in microorganisms for industrial-scale production. The experimental implementation provides additional information for computational optimization. Consumption of chemicals as biofuels results in the release of CO₂, which can be recycled by microorganisms in bioreactors to close the cycle.

FIGURE 2

Computational enzyme engineering pipelines. Module 1: structure–function analysis to identify active site and substrate-binding pocket. Module 2: building enzyme–substrate complexes with molecular docking approaches. Module 3: identification of design positions for the subsequent sequence design. Module 4: engineering stability of enzymes with PROSS and FireProt. Module 5: engineering activity and specificity of enzymes with FuncLib, IPRO, CADEE, and HotSpotWizard. Module 6: screening for stability, affinity, and activity changes with DUET, STRUM, KDEEP, and mCSM-lig.

FIGURE 3

Optimization of enzymes with different engineering strategies with increasing amount of computational modeling and predictions. (A) Rational engineering of CvFAP enzyme for the increased production of propane. (B) Rational engineering based on MD simulation data of SrCAR increased production of benzaldehyde. (C) Semirational sequence design with IPRO of EcTesA for altered substrate specificity. (D) Semirational sequence design with FuncLib and subsequent screening with the EVB approach to increase the catalytic efficiency of a Kemp eliminase GNCA4.