Directed evolution of stereo-, regio-, and chemoselective enzymes constitutes a unique way to generate biocatalysts for synthetically interesting transformations in organic chemistry and biotechnology. In order for this protein engineering technique to be efficient, fast, and reliable, and also of relevance to synthetic organic chemistry, methodology development was and still is necessary. Following a description of early key contributions, this review focuses on recent developments. It includes optimization of molecular biological methods for gene mutagenesis and the design of efficient strategies for their application, resulting in notable reduction of the screening effort (bottleneck of directed evolution). When aiming for laboratory evolution of selectivity and activity, second-generation versions of Combinatorial Active-Site Saturation Test (CAST) and Iterative Saturation Mutagenesis (ISM), both involving saturation mutagenesis (SM) at sites lining the binding pocket, have emerged as preferred approaches, aided by in silico methods such as machine learning. The recently proposed Focused Rational Iterative Site-specific Mutagenesis (FRISM) constitutes a fusion of rational design and directed evolution. On-chip solid-phase chemical gene synthesis for rapid library construction enhances library quality notably by eliminating undesired amino acid bias, the future of directed evolution?

The Crucial Role of Methodology Development in Directed Evolution of Selective Enzymes.

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

/pdf/recent-trends-in-biocatalysis-e1icub3xwz.pdf

Recent trends in biocatalysis

Abstract Enzyme turnover numbers ( k cat ) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k cat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k cat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k cat changes for mutated enzymes and identify amino acid residues with a strong impact on k cat values. We applied this approach to predict genome-scale k cat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k cat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale. 

/pdf/deep-learning-based-kcat-prediction-enables-improved-enzyme-pkqkgv3j.pdf

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Activity-based protein profiling (ABPP) is a technique that uses highly selective active-site targeted chemical probes to label and monitor the state of proteins. ABPP integrates the strengths of both chemical and biological disciplines. By utilizing chemically synthesized or modified bioactive molecules, ABPP is able to reveal complex physiological and pathological enzyme-substrate interactions at molecular and cellular levels. It is also able to provide critical information of the catalytic activity changes of enzymes, annotate new functions of enzymes, discover new substrates of enzymes, and allow real-time monitoring of the cellular location of enzymes. Based on the mechanism of probe-enzyme interaction, two types of probes that have been used in ABPP are activity-based probes (ABPs) and affinity-based probes (AfBPs). This review highlights the recent advances in the use of ABPs and AfBPs, and summarizes their design strategies (based on inhibitors and substrates) and detection approaches.

/pdf/recent-advances-in-activity-based-probes-abps-and-affinity-3ym0m71lo6.pdf

Recent advances in activity-based probes (ABPs) and affinity-based probes (AfBPs) for profiling of enzymes.

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO2, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO2-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO2-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO2 fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO2 derivates, such as formate or methanol, represents a suitable alternative to direct use of CO2 and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO2 fixation and its potential to reduce CO2 emissions.

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.2c00581

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymes are versatile catalysts and their synthetic potential has been recognized for a long time. In order to exploit their full potential, enzymes often need to be re-engineered or optimized for a given application. (Semi-) rational design has emerged as a powerful means to engineer proteins, but requires detailed knowledge about structure function relationships. In turn, directed evolution methodologies, which consist of iterative rounds of diversity generation and screening, can improve an enzyme's properties with virtually no structural knowledge. Current diversity generation methods grant us access to a vast sequence space (libraries of >1012 enzyme variants) that may hide yet unexplored catalytic activities and selectivity. However, the time investment for conventional agar plate or microtiter plate-based screening assays represents a major bottleneck in directed evolution and limits the improvements that are obtainable in reasonable time. Ultrahigh-throughput screening (uHTS) methods dramatically increase the number of screening events per time, which is crucial to speed up biocatalyst design, and to widen our knowledge about sequence function relationships. In this review, we summarize recent advances in uHTS for directed enzyme evolution. We shed light on the importance of compartmentalization to preserve the essential link between genotype and phenotype and discuss how cells and biomimetic compartments can be applied to serve this function. Finally, we discuss how uHTS can inspire novel functional metagenomics approaches to identify natural biocatalysts for novel chemical transformations.

Advances in ultrahigh-throughput screening for directed enzyme evolution

Oxidases are of interest to chemical and pharmaceutical industries because they catalyze highly selective oxidations. However, oxidases found in nature often need to be re-engineered for synthetic applications. Herein, we developed the versatile and robust flow cytometry-based screening platform "FlOxi" for directed oxidase evolution. FlOxi utilizes hydrogen peroxide produced by oxidases expressed in E. coli to oxidize Fe2+ to Fe3+ (Fenton reaction). Fe3+ mediates the immobilization of a His6-tagged eGFP (eGFPHis) on the E. coli cell surface, ensuring the identification of beneficial oxidase variants by flow cytometry. FlOxi was validated with two oxidases-a galactose oxidase (GalOx) and a D-amino acid oxidase (D-AAO)--yielding a GalOx variant (T521A) with a 4.4-fold lower Km value and a D-AAO variant (L86M/G14/A48/T205) with a 4.2-fold higher kcat than their wildtypes. Thus, FlOxi can be used for the evolution of hydrogen peroxide-producing oxidases and applied for non-fluorescence substrates.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/anie.202214999

A Flow Cytometry-Based Ultrahigh-Throughput Screening Method for Directed Evolution of Oxidases.

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Advances in ultrahigh-throughput screening for directed enzyme evolution

A Flow Cytometry-Based Ultrahigh-Throughput Screening Method for Directed Evolution of Oxidases.