Selection strategies for improved biocatalysts

Growth-coupled high throughput selection for directed enzyme evolution

Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.

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.

Forty Years of Directed Evolution and its Continuously Evolving Technology Toolbox - A Review of the Patent Landscape

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whilst rational protein design approach has made substantial progress, the idea of using a method based on cycles of mutagenesis and natural selection to develop novel binding proteins, enzymes and structures has attracted great attention. Laboratory evolution of proteins/enzymes requires new tools and analytical approaches to create genetic diversity and identifying variants with desired traits. In this pursuit, construction of sufficiently large libraries of target molecules to search for improved variants and the need for new protocols to alter the properties of target molecules has been a continuing challenge in the directed evolution experiments. This review will discuss the in vivo and in vitro gene diversification tools, library screening or selection approaches, and artificial intelligence/machine-learning-based strategies to mutagenesis developed in the last forty years to accelerate the natural process of evolution in creating new functional protein variants, optimization of microbial strains and transformation of enzymes into industrial machines. Analyzing patent position over these techniques and mechanisms also constitutes an integral and distinctive part of this review. The aim is to provide an up-to-date resource/technology toolbox for research-based and pharmaceutical companies to discover the boundaries of competitor's intellectual property (IP) portfolio, their freedom-to-operate in the relevant IP landscape, and the need for patent due diligence analysis to rule out whether use of a particular patented mutagenesis method, library screening/selection technique falls outside the safe harbor of experimental use exemption. While so doing, we have referred to some recent cases that emphasize the significance of selecting a suitable gene diversification strategy in directed evolution experiments. This article is protected by copyright. All rights reserved.

Current state of and need for enzyme engineering of 2-deoxy-D-ribose 5-phosphate aldolases and its impact

Abstract

Deoxyribose-5-phosphate aldolases (DERAs, EC 4.1.2.4) are acetaldehyde-dependent, Class I aldolases catalyzing in nature a reversible aldol reaction between an acetaldehyde donor (C2 compound) and glyceraldehyde-3-phosphate acceptor (C3 compound, C3P) to generate deoxyribose-5-phosphate (C5 compound, DR5P). DERA enzymes have been found to accept also other types of aldehydes as their donor, and in particular as acceptor molecules. Consequently, DERA enzymes can be applied in C–C bond formation reactions to produce novel compounds, thus offering a versatile biocatalytic alternative for synthesis. DERA enzymes, found in all kingdoms of life, share a common TIM barrel fold despite the low overall sequence identity. The catalytic mechanism is well-studied and involves formation of a covalent enzyme-substrate intermediate. A number of protein engineering studies to optimize substrate specificity, enzyme efficiency, and stability of DERA aldolases have been published. These have employed various engineering strategies including structure-based design, directed evolution, and recently also machine learning–guided protein engineering. For application purposes, enzyme immobilization and usage of whole cell catalysis are preferred methods as they improve the overall performance of the biocatalytic processes, including often also the stability of the enzyme. Besides single-step enzymatic reactions, DERA aldolases have also been applied in multi-enzyme cascade reactions both in vitro and in vivo. The DERA-based applications range from synthesis of commodity chemicals and flavours to more complicated and high-value pharmaceutical compounds.

Key points

• DERA aldolases are versatile biocatalysts able to make new C–C bonds.

• Synthetic utility of DERAs has been improved by protein engineering approaches.

• Computational methods are expected to speed up the future DERA engineering efforts.

Graphical abstract

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Development and application of a high throughput assay system for the detection of Rieske dioxygenase activity

Herein we report the development of a new periodate-based reactive assay system for the fluorescent detection of the cis-diol metabolites produced by Rieske dioxygenases. This sensitive and diastereoselective assay system successfully evaluates the substrate scope of Rieske dioxygenases and determines the relative activity of a rationally designed Rieske dioxygenase variant library. The high throughput capacity of the assay system enables rapid and efficient substrate scope investigations and screening of large dioxygenase variant libraries.

Engineering of L-amino acid deaminases for the production of α-keto acids from L-amino acids

α-keto acids are organic compounds that contain an acid group and a ketone group. L-amino acid deaminases are enzymes that catalyze the oxidative deamination of amino acids for the formation of their corresponding α-keto acids and ammonia. α-keto acids are synthesized industrially via chemical processes that are costly and use harsh chemicals. The use of the directed evolution technique, followed by the screening and selection of desirable variants, to evolve enzymes has proven to be an effective way to engineer enzymes with improved performance. This review presents recent studies in which the directed evolution technique was used to evolve enzymes, with an emphasis on L-amino acid deaminases for the whole-cell biocatalysts production of α-keto acids from their corresponding L-amino acids. We discuss and highlight recent cases where the engineered L-amino acid deaminases resulted in an improved production yield of phenylpyruvic acid, α-ketoisocaproate, α-ketoisovaleric acid, α-ketoglutaric acid, α-keto-γ-methylthiobutyric acid, and pyruvate.

Molecular parts and genetic circuits for metabolic engineering of microorganisms

Microbial conversion of biomass into value-added biochemicals is a highly sustainable process compared to petroleum-based production. In this regard, microorganisms have been engineered via simple overexpression or deletion of metabolic genes to facilitate the production. However, the producer microorganisms require complex regulatory circuits to maximize productivity and performance. To address this issue, diverse genetic circuits have been developed that allow cells to minimize their metabolic burden, overcome metabolic imbalances and respond to a dynamically changing environment. In this review, we briefly explain the basic strategy for constructing genetic circuits by assembling molecular parts such as input, operation and output modules. Next, we describe recent applications of the circuits in the metabolic engineering of microorganisms to improve biochemical production. Beyond those achievements, genetic circuits will facilitate more innovative approaches to future strain development through mining and engineering new genetic elements and improving the complexity of genetic circuit design.

High-throughput droplet-based microfluidics for directed evolution of enzymes

Natural enzymes have evolved over millions of years to allow for their effective operation within specific environments. However, it is significant to note that despite their wide structural and chemical diversity, relatively few natural enzymes have been successfully applied to industrial processes. To address this limitation, directed evolution (DE) (a method that mimics the process of natural selection to evolve proteins toward a user‐defined goal) coupled with droplet‐based microfluidics allows the detailed analysis of millions of enzyme variants on ultra‐short timescales, and thus the design of novel enzymes with bespoke properties. In this review, we aim at presenting the development of DE over the last years and highlighting the most important advancements in droplet‐based microfluidics, made in this context towards the high‐throughput demands of enzyme optimization. Specifically, an overview of the range of microfluidic unit operations available for the construction of DE platforms is provided, focusing on their suitability and benefits for cell‐based assays, as in the case of directed evolution experimentations.

A growth- and bioluminescence-based bioreporter for the in vivo detection of novel biocatalysts

The use of bioreporters in high-throughput screening for small molecules is generally laborious and/or expensive. The technology can be simplified by coupling the generation of a desired compound to cell survival, causing only positive cells to stay in the pool of generated variants. Here, a dual selection/screening system was developed for the in vivo detection of novel biocatalysts. The sensor part of the system is based on the transcriptional regulator AraC, which controls expression of both a selection reporter (LeuB or KmR; enabling growth) for rapid reduction of the initially large library size and a screening reporter (LuxCDABE; causing bioluminescence) for further quantification of the positive variants. Of four developed systems, the best system was the medium copy system with KmR as selection reporter. As a proof of principle, the system was tested for the selection of cells expressing an l-arabinose isomerase derived from mesophilic Escherichia coli or thermophilic Geobacillus thermodenitrificans. A more than a millionfold enrichment of cells with l-arabinose isomerase activity was demonstrated by selection and exclusion of false positives by screening. This dual selection/screening system is an important step towards an improved detection method for small molecules, and thereby for finding novel biocatalysts.

High-Throughput Screening in Protein Engineering: Recent Advances and Future Perspectives

Over the last three decades, protein engineering has established itself as an important tool for the development of enzymes and (therapeutic) proteins with improved characteristics. New mutagenesis techniques and computational design tools have greatly aided in the advancement of protein engineering. Yet, one of the pivotal components to further advance protein engineering strategies is the high-throughput screening of variants. Compartmentalization is one of the key features allowing miniaturization and acceleration of screening. This review focuses on novel screening technologies applied in protein engineering, highlighting flow cytometry- and microfluidics-based platforms.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Virus scaffolds as enzyme nano-carriers

The cooperative organization of enzymes by cells is a key feature for the efficiency of living systems. In the field of nanotechnologies, effort currently aims at mimicking this natural organization. Nanoscale resolution and high-registration alignment are necessary to control enzyme distribution in nano-containers or on the surface of solid supports. Virus capsid self-assembly is driven by precise supramolecular combinations of protein monomers, which have made them attractive building blocks to engineer enzyme nano-carriers (ENCs). We discuss some examples of what in our opinion constitute the latest advances in the use of plant viruses, bacteriophages and virus-like particles (VLPs) as nano-scaffolds for enzyme selection, enzyme confinement and patterning, phage therapy, raw material processing, and single molecule enzyme kinetics studies.

Engineering of a metagenome derived lipase toward thermal tolerance: effect of asparagine to lysine mutation on the protein surface

A highly thermostable mutant lipase was generated and characterized. Mutant enzyme demonstrated 144 fold enhanced thermostability over the wild type enzyme at 60°C. Interestingly, the overall catalytic efficiency (k(cat/)K(m)) of mutant was also enhanced (~20 folds). Circular dichroism spectroscopy, studied as function of temperature, demonstrated that the mutant lipase retained its secondary structure up to 70-80°C, whereas wild type protein structure was completely distorted above 35°C. Additionally, the intrinsic tryptophan fluorescence (a probe for the tertiary structure) also displayed difference in the conformation of two enzymes during temperature dependent unfolding. Furthermore, mutation N355K resulted in extensive H-bonding (Lys355 HZ1OE2 Glu284) with a distance 2.44 Å. In contrast to this, Wt enzyme has not shown such H-bonding interaction.

Assessing directed evolution methods for the generation of biosynthetic enzymes with potential in drug biosynthesis

To address the synthesis of increasingly structurally diverse small-molecule drugs, methods for the generation of efficient and selective biological catalysts are becoming increasingly important. 'Directed evolution' is an umbrella term referring to a variety of methods for improving or altering the function of enzymes using a nature-inspired twofold strategy of mutagenesis followed by selection. This article provides an objective assessment of the effectiveness of directed evolution campaigns in generating enzymes with improved catalytic parameters for new substrates from the last decade, excluding studies that aimed to select for only improved physical properties and those that lack kinetic characterization. An analysis of the trends of methodologies and their success rates from 81 qualifying examples in the literature reveals the average fold improvement for k (cat) (or V (max)), K (m) and k (cat)/K (m) to be 366-, 12- and 2548-fold, respectively, whereas the median fold improvements are 5.4, 3 and 15.6. Further analysis by enzyme class, library-generation methodology and screening methodology explores relationships between successful campaigns and the methodologies employed.

Iterative saturation mutagenesis accelerates laboratory evolution of enzyme stereoselectivity: rigorous comparison with traditional methods

Efficacy in laboratory evolution of enzymes is currently a pressing issue, making comparative studies of different methods and strategies mandatory. Recent reports indicate that iterative saturation mutagenesis (ISM) provides a means to accelerate directed evolution of stereoselectivity and thermostability, but statistically meaningful comparisons with other methods have not been documented to date. In the present study, the efficacy of ISM has been rigorously tested by applying it to the previously most systematically studied enzyme in directed evolution, the lipase from Pseudomonas aeruginosa as a catalyst in the stereoselective hydrolytic kinetic resolution of a chiral ester. Upon screening only 10,000 transformants, unprecedented enantioselectivity was achieved (E = 594). ISM proves to be considerably more efficient than all previous systematic efforts utilizing error-prone polymerase chain reaction at different mutation rates, saturation mutagenesis at hot spots, and/or DNA shuffling, pronounced positive epistatic effects being the underlying reason.

Highly L and D enantioselective variants of horseradish peroxidase discovered by an ultrahigh-throughput selection method

A highly efficient selection method for enhanced enzyme enantioselectivity based on yeast surface display and fluorescence-activated cell sorting (FACS) is developed and validated. Its application to horseradish peroxidase has resulted in enzyme variants up to 2 orders of magnitude selective toward either substrate enantiomer at will. These marked improvements in enantioselectivity are demonstrated for the surface-bound and soluble enzymes and rationalized by computational docking studies.

Advances in laboratory evolution of enzymes

We address recent developments in the area of laboratory, or directed evolution, with a focus on enzymes and on new methodologies of generic potential. We survey three main areas: (i) library making techniques, including the application of computational and rational methods for library design; (ii) screening and selection techniques, including recent applications of enzyme screening by FACS (fluorescence activated cell sorter); (iii) new approaches for performing directed evolution, and in particular, the application of 'neutral drifts' (libraries generated by rounds of mutation and selection for the enzyme's original function) and of consensus mutations to generate highly evolvable starting points for directed evolution.

Directed evolution for drug and nucleic acid delivery

Directed evolution is a term used to describe a variety of related techniques to rapidly evolve peptides and proteins into new forms that exhibit improved properties for specific applications. In this process, molecular biology techniques allow the creation of up to billions of mutants in a single experiment, which are then subjected to high-throughput screening to identify those with enhanced activity. Applications of directed evolution to drug and gene delivery have been recently described, including those that improve the effectiveness of therapeutic enzymes, targeting peptides and antibodies, and the effectiveness or tropism of viral vectors for use in gene therapy. This review first introduces fundamental concepts of directed evolution, and then discusses emerging applications in the field of drug and gene delivery.