Directed evolution of stereoselective enzymes based on genetic selection as opposed to screening systems

Development of growth selection system and pocket engineering of d-amino acid oxidase to enhance selective deamination activity toward d-phosphinothricin

D-amino acid oxidase (DAAO)-catalyzed selective oxidative deamination is a very promising process for synthesizing l-amino acids including l-phosphinothricin ( l-PPT, a high-efficiency and broad-spectrum herbicide). However, the wild-type DAAO's low activity toward unnatural substrates like d-phosphinothricin ( d-PPT) hampers its application. Herein, a DAAO from Caenorhabditis elegans (CeDAAO) was screened and engineered to improve the catalytic potential on d-PPT. First, we designed a novel growth selection system, taking into account the intricate relationship between the growth of Escherichia coli (E. coli) and the catalytic mechanism of DAAO. The developed system was used for high-throughput screening of gene libraries, resulting in the discovery of a variant (M6) with significantly increased catalytic activity against d-PPT. The variant displays different catalytic properties on substrates with varying hydrophobicity and hydrophilicity. Analysis using Alphafold2 modeling and molecular dynamic simulations showed that the reason for the enhanced activity was the substrate-binding pocket with enlarged size and suitable charge distribution. Further QM/MM calculations revealed that the crucial factor for enhancing activity lies in reducing the initial energy barrier of the reductive half reaction. Finally, a comprehensive binding-model index to predict the enhanced activity of DAAO toward d-PPT, and an enzymatic deracemization approach was developed, enabling the efficient synthesis of l-PPT with remarkable efficiency.

Maximizing the potential of nitrilase: Unveiling their diversity, catalytic proficiency, and versatile applications

Nitrilases represent a distinct class of enzymes that play a pivotal role in catalyzing the hydrolysis of nitrile compounds, leading to the formation of corresponding carboxylic acids. These enzymatic entities have garnered significant attention across a spectrum of industries, encompassing pharmaceuticals, agrochemicals, and fine chemicals. Moreover, their significance has been accentuated by mounting environmental pressures, propelling them into the forefront of biodegradation and bioremediation endeavors. Nevertheless, the natural nitrilases exhibit intrinsic limitations such as low thermal stability, narrow substrate selectivity, and inadaptability to varying environmental conditions. In the past decade, substantial efforts have been made in elucidating the structural underpinnings and catalytic mechanisms of nitrilase, providing basis for engineering of nitrilases. Significant breakthroughs have been made in the regulation of nitrilases with ideal catalytic properties and application of the enzymes for industrial productions. This review endeavors to provide a comprehensive discourse and summary of recent research advancements related to nitrilases, with a particular emphasis on the elucidation of the structural attributes, catalytic mechanisms, catalytic characteristics, and strategies for improving catalytic performance of nitrilases. Moreover, the exploration extends to the domain of process engineering and the multifarious applications of nitrilases. Furthermore, the future development trend of nitrilases is prospected, providing important guidance for research and application in the related fields.

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

Protein engineering using mutability landscapes: Controlling site-selectivity of P450-catalyzed steroid hydroxylation

Directed evolution and rational design have been used widely in engineering enzymes for their application in synthetic organic chemistry and biotechnology. With stereoselectivity playing a crucial role in catalysis for the synthesis of valuable chemical and pharmaceutical compounds, rational design has not achieved such wide success in this specific area compared to directed evolution. Nevertheless, one bottleneck of directed evolution is the laborious screening efforts and the observed trade-offs in catalytic profiles. This has motivated researchers to develop more efficient protein engineering methods. As a prime approach, mutability landscaping avoids such trade-offs by providing more information of sequence-function relationships. Here, we describe an application of this efficient protein engineering method to improve the regio-/stereoselectivity and activity of P450BM3 for steroid hydroxylation, while keeping the mutagenesis libraries small so that they will require only minimal screening.

A growth selection system for the directed evolution of amine-forming or converting enzymes

Fast screening of enzyme variants is crucial for tailoring biocatalysts for the asymmetric synthesis of non-natural chiral chemicals, such as amines. However, most existing screening methods either are limited by the throughput or require specialized equipment. Herein, we report a simple, high-throughput, low-equipment dependent, and generally applicable growth selection system for engineering amine-forming or converting enzymes and apply it to improve biocatalysts belonging to three different enzyme classes. This results in (i) an amine transaminase variant with 110-fold increased specific activity for the asymmetric synthesis of the chiral amine intermediate of Linagliptin; (ii) a 270-fold improved monoamine oxidase to prepare the chiral amine intermediate of Cinacalcet by deracemization; and (iii) an ammonia lyase variant with a 26-fold increased activity in the asymmetric synthesis of a non-natural amino acid. Our growth selection system is adaptable to different enzyme classes, varying levels of enzyme activities, and thus a flexible tool for various stages of an engineering campaign.

Strain Development in Microalgal Biotechnology-Random Mutagenesis Techniques

Microalgal biomass and metabolites can be used as a renewable source of nutrition, pharmaceuticals and energy to maintain or improve the quality of human life. Microalgae’s high volumetric productivity and low impact on the environment make them a promising raw material in terms of both ecology and economics. To optimize biotechnological processes with microalgae, improving the productivity and robustness of the cell factories is a major step towards economically viable bioprocesses. This review provides an overview of random mutagenesis techniques that are applied to microalgal cell factories, with a particular focus on physical and chemical mutagens, mutagenesis conditions and mutant characteristics.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

SpeedyGenesXL: an Automated, High-Throughput Platform for the Preparation of Bespoke Ultralarge Variant Libraries for Directed Evolution

Directed evolution of proteins is a highly effective strategy for tailoring biocatalysts to a particular application, and is capable of engineering improvements such as k_cat, thermostability and organic solvent tolerance. It is recognized that large and systematic libraries are required to navigate a protein's vast and rugged sequence landscape effectively, yet their preparation is nontrivial and commercial libraries are extremely costly. To address this, we have developed SpeedyGenesXL, an automated, high-throughput platform for the production of wild-type genes, Boolean OR, combinatorial, or combinatorial-OR-type libraries based on the SpeedyGenes methodology. Together this offers a flexible platform for library synthesis, capable of generating many different bespoke, diverse libraries simultaneously.

DiRect: Site-directed mutagenesis method for protein engineering by rational design

Site-directed mutagenesis (SDM), an indispensable method in molecular biology and protein engineering, is rather time-consuming and laborious. Protein engineering, especially that of enzymes, nowadays increasingly relies on rational design approaches in which both SDM and protein expression are the bottlenecks because they are generally based on the recombinant DNA technology. Here, we developed a new PCR-based mutagenesis method, DiRect, that achieves high performance in product quality (≥99% substitution) without recombinant DNA technology. We applied DiRect in combination with a cell-free protein expression system to an industrially relevant enzyme, nicotinamide adenine dinucleotide phosphate-dependent 3-quinuclidinone reductase from Rhodotorula rubra. In a single round of screening, 90 newly designed mutant proteins were produced within two days, and an unreported mutant (Q135I) exhibiting much higher thermostability than the wild-type enzyme was successfully identified within one extra day. Thus, DiRect is a simple, efficient, and potentially scalable SDM method.

In Vivo Selection for Formate Dehydrogenases with High Efficiency and Specificity toward NADP. ACS Catal 2020;10:7512-7525. [PMID: 32733773 PMCID: PMC7384739 DOI: 10.1021/acscatal.0c01487]

The efficient regeneration of cofactors is vital for the establishment of biocatalytic processes. Formate is an ideal electron donor for cofactor regeneration due to its general availability, low reduction potential, and benign byproduct (CO₂). However, formate dehydrogenases (FDHs) are usually specific to NAD⁺, such that NADPH regeneration with formate is challenging. Previous studies reported naturally occurring FDHs or engineered FDHs that accept NADP⁺, but these enzymes show low kinetic efficiencies and specificities. Here, we harness the power of natural selection to engineer FDH variants to simultaneously optimize three properties: kinetic efficiency with NADP⁺, specificity toward NADP⁺, and affinity toward formate. By simultaneously mutating multiple residues of FDH from Pseudomonas sp. 101, which exhibits practically no activity toward NADP⁺, we generate a library of >10⁶ variants. We introduce this library into an E. coli strain that cannot produce NADPH. By selecting for growth with formate as the sole NADPH source, we isolate several enzyme variants that support efficient NADPH regeneration. We find that the kinetically superior enzyme variant, harboring five mutations, has 5-fold higher efficiency and 14-fold higher specificity in comparison to the best enzyme previously engineered, while retaining high affinity toward formate. By using molecular dynamics simulations, we reveal the contribution of each mutation to the superior kinetics of this variant. We further determine how nonadditive epistatic effects improve multiple parameters simultaneously. Our work demonstrates the capacity of in vivo selection to identify highly proficient enzyme variants carrying multiple mutations which would be almost impossible to find using conventional screening methods.

Recent progress in directed evolution of stereoselective monoamine oxidases

Monoamine oxidases (MAOs) use molecular dioxygen as oxidant to catalyze the oxidation of amines to imines. This type of enzyme can be employed for the synthesis of primary, secondary, and tertiary amines by an appropriate deracemization protocol. Consequently, MAOs are an attractive class of enzymes in biocatalysis. However, they also have limitations in enzyme-catalyzed processes due to the often-observed narrow substrate scope, low activity, or poor/wrong stereoselectivity. Therefore, directed evolution was introduced to eliminate these obstacles, which is the subject of this review. The main focus is on recent efforts concerning the directed evolution of four MAOs: monoamine oxidase (MAO-N), cyclohexylamine oxidase (CHAO),D-amino acid oxidase (pkDAO), and 6-hydroxy-D-nicotine oxidase (6-HDNO).

Application of the uridine auxotrophic host and synthetic nucleosides for a rapid selection of hydrolases from metagenomic libraries

A high-throughput method (≥ 106 of clones can be analysed on a single agar plate) for the selection of ester-hydrolysing enzymes was developed based on the uridine auxotrophy of Escherichia coli strain DH10B ΔpyrFEC and the acylated derivatives 2',3',5'-O-tri-acetyluridine and 2',3',5'-O-tri-hexanoyluridine as the sole source of uridine. The proposed approach permits the selection of hydrolases belonging to different families and active towards different substrates. Moreover, the ester group of the substrate used for the selection, at least partly, determined the specificity of the selected enzymes.

'Head-to-Head' mRNA display for the translation of multi-copied proteins with a free C-terminus

With the development of various methods for affinity-based selection of proteins such as phage display, ribosomal display, and mRNA display, the progress in this field has been gradually shifting to function-based selection, such as through single-molecule observation, genetic selection, and compartmentalization technologies. In this vein, we present an opposite link mode of mRNA display termed as a 'Head-to-Head' (H2H) link. The key technique in H2H, formation of a covalent bond between O⁶-benzylguanine (BG) and O⁶-alkylguanine-DNA alkyltransferase (AGT), was demonstrated to be workable in H2H ligation, where mRNA is linked to a nascent AGT via a BG-DNA linker, resulting in a "(C-terminus) protein-BG-DNA linker-mRNA (5'-terminus)" conjugate. Thus, a head (N-terminus) to head (5'-terminus) linkage is formed. Among the advantages of H2H, the generation of multi-copied proteins is the most promising and was proven to be possible owing to the restored stop codon, which had been intentionally removed in the conventional mRNA display. Another advantage is obviously having a free C-terminus of the protein, which can be used for modifications such as C-terminal methylation, α-amidation, and others, which occur in nature. A superior merit of H2H is that it makes it possible to use a single construct commonly in mRNA display (affinity-based) and compartmentalization technologies (function-based) without requiring complicated construct changes.

Fast and Flexible Synthesis of Combinatorial Libraries for Directed Evolution

Directed evolution (DE) is a powerful tool for optimizing an enzyme's properties toward a particular objective, such as broader substrate scope, greater thermostability, or increased k_cat. A successful DE project requires the generation of genetic diversity and subsequent screening or selection to identify variants with improved fitness. In contrast to random methods (error-prone PCR or DNA shuffling), site-directed mutagenesis enables the rational design of variant libraries and provides control over the nature and frequency of the encoded mutations. Knowledge of protein structure, dynamics, enzyme mechanisms, and natural evolution demonstrates that multiple (combinatorial) mutations are required to discover the most improved variants. To this end, we describe an experimentally straightforward and low-cost method for the preparation of combinatorial variant libraries. Our approach employs a two-step PCR protocol, first producing mutagenic megaprimers, which can then be combined in a "mix-and-match" fashion to generate diverse sets of combinatorial variant libraries both quickly and accurately.

Efficient molecular evolution to generate enantioselective enzymes using a dual-channel microfluidic droplet screening platform

Directed evolution has long been a key strategy to generate enzymes with desired properties like high selectivity, but experimental barriers and analytical costs of screening enormous mutant libraries have limited such efforts. Here, we describe an ultrahigh-throughput dual-channel microfluidic droplet screening system that can be used to screen up to ~10⁷ enzyme variants per day. As an example case, we use the system to engineer the enantioselectivity of an esterase to preferentially produce desired enantiomers of profens, an important class of anti-inflammatory drugs. Using two types of screening working modes over the course of five rounds of directed evolution, we identify (from among 5 million mutants) a variant with 700-fold improved enantioselectivity for the desired (S)-profens. We thus demonstrate that this screening platform can be used to rapidly generate enzymes with desired enzymatic properties like enantiospecificity, chemospecificity, and regiospecificity.

Optimizing an enzyme usually requires testing thousands of variants, thus consuming large amounts of material and time. Here, the authors present a method that allows for measuring two different activities of the same enzyme simultaneously instead of doing two consecutive rounds of screening.

Practical Considerations Regarding the Choice of the Best High-Throughput Assay

All protein engineering studies include the stage of identifying and characterizing variants within a mutant library by employing a suitable assay or selection method. A large variety of different assay approaches for different enzymes have been developed in the last few decades, and the throughput performance of these assays vary considerably. Thus, the concept of a protein engineering study must be adapted to the available assay methods. This introductory review chapter describes different assay concepts on selected examples, including selection and screening approaches, detection of pH and cosubstrate changes, coupled enzyme assays, methods using surrogate substrates and selective derivatization. The given examples should guide and inspire the reader when choosing and developing own high-throughput screening approaches.

Protein Engineering: Past, Present, and Future

The last decade has seen a dramatic increase in the utilization of enzymes as green and sustainable (bio)catalysts in pharmaceutical and industrial applications. This trend has to a significant degree been fueled by advances in scientists' and engineers' ability to customize native enzymes by protein engineering. A review of the literature quickly reveals the tremendous success of this approach; protein engineering has generated enzyme variants with improved catalytic activity, broadened or altered substrate specificity, as well as raised or reversed stereoselectivity. Enzymes have been tailored to retain activity at elevated temperatures and to function in the presence of organic solvents, salts and pH values far from physiological conditions. However, readers unfamiliar with the field will soon encounter the confusingly large number of experimental techniques that have been employed to accomplish these engineering feats. Herein, we use history to guide a brief overview of the major strategies for protein engineering-past, present, and future.

Directed Evolution of Proteins Based on Mutational Scanning

Directed evolution has emerged as one of the most effective protein engineering methods in basic research as well as in applications in synthetic organic chemistry and biotechnology. The successful engineering of protein activity, allostery, binding affinity, expression, folding, fluorescence, solubility, substrate scope, selectivity (enantio-, stereo-, and regioselectivity), and/or stability (temperature, organic solvents, pH) is just limited by the throughput of the genetic selection, display, or screening system that is available for a given protein. Sometimes it is possible to analyze millions of protein variants from combinatorial libraries per day. In other cases, however, only a few hundred variants can be screened in a single day, and thus the creation of smaller yet smarter libraries is needed. Different strategies have been developed to create these libraries. One approach is to perform mutational scanning or to construct "mutability landscapes" in order to understand sequence-function relationships that can guide the actual directed evolution process. Herein we provide a protocol for economically constructing scanning mutagenesis libraries using a cytochrome P450 enzyme in a high-throughput manner. The goal is to engineer activity, regioselectivity, and stereoselectivity in the oxidative hydroxylation of a steroid, a challenging reaction in synthetic organic chemistry. Libraries based on mutability landscapes can be used to engineer any fitness trait of interest. The protocol is also useful for constructing gene libraries for deep mutational scanning experiments.

Biocatalysts for the pharmaceutical industry created by structure-guided directed evolution of stereoselective enzymes

Enzymes have been used for a long time as catalysts in the asymmetric synthesis of chiral intermediates needed in the production of therapeutic drugs. However, this alternative to man-made catalysts has suffered traditionally from distinct limitations, namely the often observed wrong or insufficient enantio- and/or regioselectivity, low activity, narrow substrate range, and insufficient thermostability. With the advent of directed evolution, these problems can be generally solved. The challenge is to develop and apply the most efficient mutagenesis methods which lead to highest-quality mutant libraries requiring minimal screening. Structure-guided saturation mutagenesis and its iterative form have emerged as the method of choice for evolving stereo- and regioselective mutant enzymes needed in the asymmetric synthesis of chiral intermediates. The number of (industrial) applications in the preparation of chiral pharmaceuticals is rapidly increasing. This review features and analyzes typical case studies.

Different Structural Origins of the Enantioselectivity of Haloalkane Dehalogenases toward Linear β-Haloalkanes: Open-Solvated versus Occluded-Desolvated Active Sites

The enzymatic enantiodiscrimination of linear β-haloalkanes is difficult because the simple structures of the substrates prevent directional interactions. Herein we describe two distinct molecular mechanisms for the enantiodiscrimination of the β-haloalkane 2-bromopentane by haloalkane dehalogenases. Highly enantioselective DbjA has an open, solvent-accessible active site, whereas the engineered enzyme DhaA31 has an occluded and less solvated cavity but shows similar enantioselectivity. The enantioselectivity of DhaA31 arises from steric hindrance imposed by two specific substitutions rather than hydration as in DbjA.

Enzymatic site-selectivity enabled by structure-guided directed evolution

This review covers recent advances in the directed evolution of enzymes for controlling site-selectivity of hydroxylation, amination and chlorination.

Economical analysis of saturation mutagenesis experiments

Saturation mutagenesis is a powerful technique for engineering proteins, metabolic pathways and genomes. In spite of its numerous applications, creating high-quality saturation mutagenesis libraries remains a challenge, as various experimental parameters influence in a complex manner the resulting diversity. We explore from the economical perspective various aspects of saturation mutagenesis library preparation: We introduce a cheaper and faster control for assessing library quality based on liquid media; analyze the role of primer purity and supplier in libraries with and without redundancy; compare library quality, yield, randomization efficiency, and annealing bias using traditional and emergent randomization schemes based on mixtures of mutagenic primers; and establish a methodology for choosing the most cost-effective randomization scheme given the screening costs and other experimental parameters. We show that by carefully considering these parameters, laboratory expenses can be significantly reduced.

Engineering Novel and Improved Biocatalysts by Cell Surface Display

Biocatalysts, especially enzymes, have the ability to catalyze reactions with high product selectivity, utilize a broad range of substrates, and maintain activity at low temperature and pressure. Therefore, they represent a renewable, environmentally friendly alternative to conventional catalysts. Most current industrial-scale chemical production processes using biocatalysts employ soluble enzymes or whole cells expressing intracellular enzymes. Cell surface display systems differ by presenting heterologous enzymes extracellularly, overcoming some of the limitations associated with enzyme purification and substrate transport. Additionally, coupled with directed evolution, cell surface display is a powerful platform for engineering enzymes with enhanced properties. In this review, we will introduce the molecular and cellular principles of cell surface display and discuss how it has been applied to engineer enzymes with improved properties as well as to develop surface-engineered microbes as whole-cell biocatalysts.