Fully automatized high-throughput enzyme library screening using a robotic platform

BAT2: an Open-Source Tool for Flexible, Automated, and Low Cost Absolute Binding Free Energy Calculations

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD) have the potential to greatly reduce costs in the first stages of drug discovery. Here, we introduce BAT2, the new version of the Binding Affinity Tool (BAT.py), designed to combine full automation of ABFE calculations with high-performance MD simulations, making it a potential tool for virtual screening. We describe and test several changes and new features that were incorporated into the code, such as relative restraints between the protein and the ligand instead of using fixed dummy atoms, support for the OpenMM simulation engine, a merged approach to the application/release of restraints, support for cobinders and proteins with multiple chains, and many others. We also reduced the simulation times for each ABFE calculation, assessing the effect on the expected robustness and accuracy of the calculations.

Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering

Lipases are remarkable biocatalysts, adept at catalyzing the breakdown of diverse compounds into glycerol, fatty acids, and mono- and di-glycerides via hydrolysis. Beyond this, they facilitate esterification, transesterification, alcoholysis, acidolysis, and more, making them versatile in industrial applications. In industrial processes, lipases that exhibit high stability are favored as they can withstand harsh conditions. However, most native lipases are unable to endure adverse conditions, making them unsuitable for industrial use. Protein engineering proves to be a potent technology in the development of lipases that can function effectively under challenging conditions and fulfill criteria for various industrial processes. This review concentrated on new trends in protein engineering to enhance the diversity of lipase genes and employed in silico methods for predicting and comprehensively analyzing target mutations in lipases. Additionally, key molecular factors associated with industrial characteristics of lipases, including thermostability, solvent tolerance, catalytic activity, and substrate preference have been elucidated. The present review delved into how industrial traits can be enhanced through directed evolution (epPCR, gene shuffling), rational design (FRESCO, ASR), combined engineering strategies (i.e. CAST, ISM, and FRISM) as protein engineering methodologies in contexts of biodiesel production, food processing, and applications of detergent, pharmaceutics, and plastic degradation.

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Optical-based microbubble for on-demand droplet release from static droplet array (SDA) for dispensing one droplet into one tube

Static droplet array (SDA) is a pivotal tool for high-capacity screening assays, yet extraction and collection the target droplets that contain unique analytes or cells from the SDA remains one major technical bottleneck that limits its broader application. Here we present an optical-based on-demand droplet release (OODR) system by incorporating a 1064 nm laser-responsive indium tin oxide (ITO) layer into a chamber array-based droplet microfluidic chip. By focusing the 1064 nm laser onto the ITO layer, microbubbles can be created via local heating to selectively push-out the droplets from the chamber. Then the released droplet is readily exported in a one-droplet-one-tube (ODOT) manner by the inherent capillary force into pipette tip. Releasing of the droplets containing fluorescein sodium demonstrated ∼100% successful rate (9 out of 6400 droplets were successfully released) and low residual (only ∼5% of the droplet volume remains in the chamber). White or fluorescence image-based releasing of single-cell-droplets directly after cell loading or multi-cells-droplets derived from on-chip single-cell cultivation for both E. coli and yeast cells further demonstrated the wide applicability of OODR. The present system is user-friendly and has the potential to be applied in various high-throughput screening assays, including single molecule/cell analysis, drug screening, and phenotype-based cell sorting.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >10⁷-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >10⁷ variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

Single-cell microliter-droplet screening system (MISS Cell): An integrated platform for automated high-throughput microbial monoclonal cultivation and picking

Solid plates have been used for microbial monoclonal isolation, cultivation, and colony picking since 1881. However, the process is labor- and resource-intensive for high-throughput requirements. Currently, several instruments have been integrated for automated and high-throughput picking, but complicated and expensive. To address these issues, we report a novel integrated platform, the single-cell microliter-droplet screening system (MISS Cell), for automated, high-throughput microbial monoclonal colony cultivation and picking. We verified the monoclonality of droplet cultures in the MISS Cell and characterized culture performance. Compared with solid plates, the MISS Cell generated a larger number of monoclonal colonies with higher initial growth rates using fewer resources. Finally, we established a workflow for automated high-throughput screening of Corynebacterium glutamicum using the MISS Cell and identified high glutamate-producing strains. The MISS Cell can serve as a universal platform to efficiently produce monoclonal colonies in high-throughput applications, overcoming the limitations of solid plates to promote rapid development in biotechnology.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD⁺/NADH and NADP⁺/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD⁺/NADH and NADP⁺/NADPH pairs, and of non-canonical redox cofactor pairs NMN⁺/NMNH and NCD⁺/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

Engineering of xylanases for the development of biotechnologically important characteristics

Xylanases are the main biocatalysts used for the reduction of the xylan backbone from hemicellulose, randomly splitting off β-1,4-glycosidic linkages between xylopyranosyl residues. Xylanase market has been annually estimated at 500 million US Dollars and they are potentially used in broad industrial process ranges such as paper pulp biobleaching, xylo-oligosaccharide production, and biofuel manufacture from lignocellulose. The highly stable xylanases are preferred in the downstream procedure of industrial processes because they can tolerate severe conditions. Almost all native xylanases can not endure adverse conditions thus they are industrially not proper to be utilized. Protein engineering is a powerful technology for developing xylanases, which can effectively work in adverse conditions and can meet requirements for industrial processes. This study considered state-of-the-art strategies of protein engineering for creating the xylanase gene diversity, high-throughput screening systems toward upgraded traits of the xylanases, and the prediction and comprehensive analysis of the target mutations in xylanases by in silico methods. Also, key molecular factors have been elucidated for industrial characteristics (alkaliphilic enhancement, thermal stability, and catalytic performance) of GH11 family xylanases. The present review explores industrial characteristics improved by directed evolution, rational design, and semi-rational design as protein engineering approaches for pulp bleaching process, xylooligosaccharides production, and biorefinery & bioenergy production.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

Emerging platforms for high-throughput enzymatic bioassays

Enzymes have essential roles in catalyzing biological reactions and maintaining metabolic systems. Many in vitro enzymatic bioassays have been developed for use in industrial and research fields, such as cell biology, enzyme engineering, drug screening, and biofuel production. Of note, many of these require the use of high-throughput platforms. Although the microtiter plate remains the standard for high-throughput enzymatic bioassays, microfluidic arrays and droplet microfluidics represent emerging methods. Each has seen significant advances and offers distinct advantages; however, drawbacks in key performance metrics, including reagent consumption, reaction manipulation, reaction recovery, real-time measurement, concentration gradient range, and multiplexity, remain. Herein, we compare recent high-throughput platforms using the aforementioned metrics as criteria and provide insights into remaining challenges and future research trends.

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.

Biofoundry Palette: Planning-Assistant Software for Liquid Handler-Based Experimentation and Operation in the Biofoundry Workflow

Lab automation has facilitated synthetic biology applications in an automated workflow, and biofoundry facilities have enabled automated high-throughput experiments of gene cloning and genome engineering to be conducted following a precise experimental design and protocol. However, before-experiment procedures in biofoundry applications have been underdetermined. We aimed to develop a Python-based planning-assistant software, namely Biofoundry Palette, for liquid handler-based experimentation and operation in the biofoundry workflow. Depending on the synthetic biology project, variable information and content information may vary; the Biofoundry Palette provides precise information for the before-experiment units for each process module in the biofoundry workflow. As a demonstration, more than 200 unique information sets, generated by Biofoundry Palette, were used in automated gene cloning or pathway construction. The information on planning and management can potentially help the operator faithfully execute the biofoundry workflow after securing the before-experiment unit, thereby lowering the risk of human errors and performing successful biofoundry operations for synthetic biology applications.

Accelerated strain construction and characterization of C. glutamicum protein secretion by laboratory automation

Secretion of bacterial proteins into the culture medium simplifies downstream processing by avoiding cell disruption for target protein purification. However, a suitable signal peptide for efficient secretion needs to be identified, and currently, there are no tools available to predict optimal combinations of signal peptides and target proteins. The selection of such a combination is influenced by several factors, including protein biosynthesis efficiency and cultivation conditions, which both can have a significant impact on secretion performance. As a result, a large number of combinations must be tested. Therefore, we have developed automated workflows allowing for targeted strain construction and secretion screening using two platforms. Key advantages of this experimental setup include lowered hands-on time and increased throughput. In this study, the automated workflows were established for the heterologous production of Fusarium solani f. sp. pisi cutinase in Corynebacterium glutamicum. The target protein was monitored in culture supernatants via enzymatic activity and split GFP assay. Varying spacer lengths between the Shine-Dalgarno sequence and the start codon of Bacillus subtilis signal peptides were tested. Consistent with previous work on the secretory cutinase production in B. subtilis, a ribosome binding site with extended spacer length to up to 12 nt, which likely slows down translation initiation, does not necessarily lead to poorer cutinase secretion by C. glutamicum. The best performing signal peptides for cutinase secretion with a standard spacer length were identified in a signal peptide screening. Additional insights into the secretion process were gained by monitoring secretion stress using the C. glutamicum K9 biosensor strain. KEY POINTS: • Automated workflows for strain construction and screening of protein secretion • Comparison of spacer, signal peptide, and host combinations for cutinase secretion • Signal peptide screening for secretion by C. glutamicum using the split GFP assay.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

Biocatalysis as Key to Sustainable Industrial Chemistry

The role and power of biocatalysis in sustainable chemistry has been continuously brought forward step by step to its present outstanding position. The problem-solving capabilities of biocatalysis have been realized by numerous substantial achievements in biology, chemistry and engineering. Advances and breakthroughs in the life sciences and interdisciplinary cooperation with chemistry have clearly accelerated the implementation of biocatalytic synthesis in modern chemistry. Resource-efficient biocatalytic manufacturing processes have already provided numerous benefits to sustainable chemistry as well as customer-centric value creation in the pharmaceutical, food, flavor, fragrance, vitamin, agrochemical, polymer, specialty, and fine chemical industries. Biocatalysis can make significant contributions not only to manufacturing processes, but also to the design of completely new value-creation chains. Biocatalysis can now be considered as a key enabling technology to implement sustainable chemistry.

Research progress of pathway and genome evolution in microbes

Microbes can produce valuable natural products widely applied in medicine, food and other important fields. Nevertheless, it is usually challenging to achieve ideal industrial yields due to low production rate and poor toxicity tolerance. Evolution is a constant mutation and adaptation process used to improve strain performance. Generally speaking, the synthesis of natural products in microbes is often intricate, involving multiple enzymes or multiple pathways. Individual evolution of a certain enzyme often fails to achieve the desired results, and may lead to new rate-limiting nodes that affect the growth of microbes. Therefore, it is inevitable to evolve the biosynthetic pathways or the whole genome. Here, we reviewed the pathway-level evolution including multi-enzyme evolution, regulatory elements engineering, and computer-aided engineering, as well as the genome-level evolution based on several tools, such as genome shuffling and CRISPR/Cas systems. Finally, we also discussed the major challenges faced by in vivo evolution strategies and proposed some potential solutions.

Recent advances in the development of Aspergillus for protein production

Aspergillus had been widely used in the industrial production of recombinant proteins. In addition to the safety and broad substrate utilization spectrum, its efficient post-translational modification and strong protein secretion capacity have significant advantages for developing an excellent protein-producing cell factory in industrial production. However, the difficulties in genetic manipulation of Aspergillus and varying expression levels of different heterologous proteins hampered its further development and application. Recently, the development of CRISPR genome editing and high-throughput screening platforms has facilitated the Aspergillus development of a wide range of modifications and applications. Meanwhile, multi-omics analysis and multiplexed genetic engineering have promoted effective knowledge mining. This paper provides a comprehensive and updated review of these advances, including high-throughput screening, genome editing, protein expression modules, and fermentation optimization. It also highlights and discusses the latest significant progress, aiming to provide a practical guide for implementing Aspergillus as an efficient protein-producing cell factory.

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype–phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma.

Microorganisms harbor keys to a circular bioeconomy making them useful tools in fighting plastic pollution and rising CO2 levels

The major global and man-made challenges of our time are the fossil fuel-driven climate change a global plastic pollution and rapidly emerging plant, human and animal infections. To meet the necessary global changes, a dramatic transformation must take place in science and society. This transformation will involve very intense and forward oriented industrial and basic research strongly focusing on (bio)technology and industrial bioprocesses developments towards engineering a zero-carbon sustainable bioeconomy. Within this transition microorganisms-and especially extremophiles-will play a significant and global role as technology drivers. They harbor the keys and blueprints to a sustainable biotechnology in their genomes. Within this article, we outline urgent and important areas of microbial research and technology advancements and that will ultimately make major contributions during the transition from a linear towards a circular bioeconomy.

EnzymeML-a data exchange format for biocatalysis and enzymology

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

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%).

Robotics for enzyme technology: innovations and technological perspectives

The use of robotics in the life science sector has created a considerable and significant impact on a wide range of research areas, including enzyme technology due to their immense applications in enzyme and microbial engineering as an indispensable tool in high-throughput screening applications. Scientists are experiencing the advanced applications of various biological robots (nanobots), fabricated based on bottom-up or top-down approaches for making nanotechnology scaffolds. Nanobots and enzyme-powered nanomotors are particularly attractive because they are self-propelled vehicles, which consume biocompatible fuels. These smart nanostructures are widely used as drug delivery systems for the efficient treatment of various diseases. This review gives insights into the escalating necessity of robotics and nanobots and their ever-widening applications in enzyme technology, including biofuel production and biomedical applications. It also offers brief insights into high-throughput robotic platforms that are currently being used in enzyme screening applications for monitoring and control of microbial growth conditions. KEY POINTS: • Robotics and their applications in biotechnology are highlighted. • Robotics for high-throughput enzyme screening and microbial engineering are described. • Nanobots and enzyme-powered nanomotors as controllable drug delivery systems are reviewed.

Ionic liquids for regulating biocatalytic process: Achievements and perspectives

Biocatalysis has found enormous applications in sorts of fields as an alternative to chemical catalysis. In the pursue of green and sustainable chemistry, ionic liquids (ILs) have been considered as promising reaction media for biocatalysis, owing to their unique characteristics, such as nonvolatility, inflammability and tunable properties as regards polarity and water miscibility behavior, compared to organic solvents. In recent years, great developments have been achieved in respects to biocatalysis in ILs, especially for preparing various chemicals. This review tends to give illustrative examples with a focus on representative chemicals production by biocatalyst in ILs and elucidate the possible mechanism in such systems. It also discusses how to regulate the catalytic efficiency from several aspects and finally provides an outlook on the opportunities to broaden biocatalysis in ILs.

Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses

Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7 , 2019) was extended and implemented into a software ("mDoE-toolbox") to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Functional expression of eukaryotic cytochrome P450s in yeast

Cytochrome P450 enzymes (P450s) are a superfamily of heme-thiolate proteins widely existing in various organisms. Due to their key roles in secondary metabolism, degradation of xenobiotics, and carcinogenesis, there is a great demand to heterologously express and obtain a sufficient amount of active eukaryotic P450s. However, most eukaryotic P450s are endoplasmic reticulum-localized membrane proteins, which is the biggest challenge for functional expression to high levels. Furthermore, the functions of P450s require the cooperation of cytochrome P450 reductases for electron transfer. Great efforts have been devoted to the heterologous expression of eukaryotic P450s, and yeasts, particularly Saccharomyces cerevisiae are frequently considered as the first expression systems to be tested for this challenging purpose. This review discusses the strategies for improving the expression and activity of eukaryotic P450s in yeasts, followed by examples of P450s involved in biosynthetic pathway engineering.

Developing a cell-bound detection system for the screening of oxidase activity using the fluorescent peroxide sensor roGFP2-Orp1

Accurate yet efficient high-throughput screenings have emerged as essential technology for enzyme engineering via directed evolution. Modern high-throughput screening platforms for oxidoreductases are commonly assisted by technologies such as surface display and rely on emulsification techniques to facilitate single-cell analysis via fluorescence-activated cell sorting. Empowered by the dramatically increased throughput, the screening of significantly larger sequence spaces in acceptable time frames is achieved but usually comes at the cost of restricted applicability. In this work, we tackle this problem by utilizing roGFP2-Orp1 as a fluorescent one-component detection system for enzymatic H2O2 formation. We determined the kinetic parameters of the roGFP2-Orp1 reaction with H2O2 and established an efficient immobilization technique for the sensor on Saccharomyces cerevisiae cells employing the lectin Concanavalin A. This allowed to realize a peroxide-sensing shell on enzyme-displaying cells, a system that was successfully employed to screen for H2O2 formation of enzyme variants in a whole-cell setting.

Parallelized disruption of prokaryotic and eukaryotic cells via miniaturized and automated bead mill

The application of integrated microbioreactor systems is rapidly becoming of more interest to accelerate strain characterization and bioprocess development. However, available high-throughput screening capabilities are often limited to target extracellular compounds only. Consequently, there is a great demand for automated technologies allowing for miniaturized and parallel cell disruption providing access to intracellular measurements. In this study, a fully automated bead mill workflow was developed and validated for four different industrial platform organisms: Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Aspergillus niger. The workflow enables up to 48 parallel cell disruptions in microtiter plates and is applicable at-line to running lab-scale cultivations. The resulting cell extracts form the basis for quantitative omics studies where no rapid metabolic quenching is required (e.g., genomics and proteomics).

Exploration of enzyme diversity: High-throughput techniques for protein production and microscale biochemical characterization

Enzymes are being increasingly utilized for acceleration of industrially and pharmaceutically critical chemical reactions. The strong demand for finding robust and efficient biocatalysts for these applications can be satisfied via the exploration of enzyme diversity. The first strategy is to mine the natural diversity, represented by millions of sequences available in the public genomic databases, by using computational approaches. Alternatively, metagenomic libraries can be targeted experimentally or computationally to explore the natural diversity of a specific environment. The second strategy, known as directed evolution, is to generate man-made diversity in the laboratory using gene mutagenesis and screen the constructed library of mutants. The selected hits must be experimentally characterized in both strategies, which currently represent the rate-limiting step in the process of diversity exploration. The traditional techniques used for biochemical characterization are time-demanding, cost, and sample volume ineffective, and low-throughput. Therefore, the development and implementation of high-throughput experimental methods are essential for discovering novel enzymes. This chapter describes the experimental protocols employing the combination of robust production and high-throughput microscale biochemical characterization of enzyme variants. We validated its applicability against the model enzyme family of haloalkane dehalogenases. These protocols can be adapted to other enzyme families, paving the way towards the functional characterization and quick identification of novel biocatalysts.

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.

Determining transaminase activity in bacterial libraries by time-lapse imaging

Transaminase activity was determined by time-lapse imaging using a colourimetric reaction and image analysis. A correlation between the benzaldehyde conversion and relative luminance was determined, allowing the identification of the most promising biocatalysts, the determination of kinetic parameters, and the assessment of the effect of the substrate concentration on activity.

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liquid-handling stations can perform a large number of automated experiments in parallel. Nevertheless, the experimental data sets that are obtained are not always relevant for development of industrial bioprocesses, leading to a high rate of failure during scale-up. We present an automated mini bioreactor platform that enables parallel cultivations in the milliliter scale with online monitoring and control, well-controlled conditions, and advanced feeding strategies similar to industrial processes. The combination of two liquid handlers allows both automated mini bioreactor operation and at-line analysis in parallel. A central database enables end-to-end data exchange and fully integrated device and process control. A model-based operation algorithm allows for the accurate performance of complex cultivations for scale-down studies and strain characterization via optimal experimental redesign, significantly increasing the reliability and transferability of data throughout process development. The platform meets the tradeoff between experimental throughput and process control and monitoring comparable to laboratory-scale bioreactors.

Alteration of Chain Length Selectivity of Candida antarctica Lipase A by Semi-Rational Design for the Enrichment of Erucic and Gondoic Fatty Acids

Biotechnological strategies using renewable materials as starting substrates are a promising alternative to traditional oleochemical processes for the isolation of different fatty acids. Among them, long chain mono-unsaturated fatty acids are especially interesting in industrial lipid modification, since they are precursors of several economically relevant products, including detergents, plastics and lubricants. Therefore, the aim of this study was to develop an enzymatic method in order to increase the percentage of long chain mono-unsaturated fatty acids from Camelina and Crambe oil ethyl ester derivatives, by using selective lipases. Specifically, the focus was on the enrichment of gondoic (C20:1 cisΔ11) and erucic acid (C22:1 cisΔ13) from Camelina and Crambe oil derivatives, respectively. The pursuit of this goal entailed several steps, including: (i) the choice of a suitable lipase scaffold to serve as a protein engineering template (Candida antarctica lipase A); (ii) the identification of potential amino acid targets to disrupt the binding tunnel at the adequate location; (iii) the design, creation and high-throughput screening of lipase mutant libraries; (iv) the study of the selectivity towards different chain length p-nitrophenyl fatty acid esters of the best hits found, as well as the analysis of the contribution of each amino acid change and the outcome of combining several of the aforementioned residue alterations and, finally, (v) the selection and application of the most promising candidates for the fatty acid enrichment biocatalysis. As a result, enrichment of C22:1 from Crambe ethyl esters was achieved either, in the free fatty acid fraction (wt, 78%) or in the esterified fraction (variants V1, 77%; V9, 78% and V19, 74%). Concerning the enrichment of C20:1 when Camelina oil ethyl esters were used as substrate, the best variant was the single mutant V290W, which doubled its content in the esterified fraction from approximately 15% to 34%. A moderately lower increase was achieved by V9 and its two derived triple mutant variants V19 and V20 (27%).

β-Phenylalanine Ester Synthesis from Stable β-Keto Ester Substrate Using Engineered ω-Transaminases

The successful synthesis of chiral amines from ketones using ω-transaminases has been shown in many cases in the last two decades. In contrast, the amination of β-keto acids is a special and relatively new challenge, as they decompose easily in aqueous solution. To avoid this, transamination of the more stable β-keto esters would be an interesting alternative. For this reason, ω-transaminases were tested in this study, which enabled the transamination of the β-keto ester substrate ethyl benzoylacetate. Therefore, a ω-transaminase library was screened using a coloring o-xylylenediamine assay. The ω-transaminase mutants 3FCR_4M and ATA117 11Rd show great potential for further engineering experiments aiming at the synthesis of chiral (S)- and (R)-β-phenylalanine esters. This alternative approach resulted in the conversion of 32% and 13% for the (S)- and (R)-enantiomer, respectively. Furthermore, the (S)-β-phenylalanine ethyl ester was isolated by performing a semi-preparative synthesis.

Microbioreactor Systems for Accelerated Bioprocess Development

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation

The open source LARA software suite is designed for guiding manual or automated high-throughput screening experiments. Process planning, data reading, analysis, and visualization are herein explained in a step-by-step guide using exemplary dataset.