Enzyme engineering: Reshaping the biocatalytic functions

New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review

Today's environmental balance has been compromised by the unreasonable and sometimes dangerous actions committed by humans to maintain their dominance over the Earth's natural resources. As a result, oceans are contaminated by the different types of plastic trash, crude oil coming from mismanagement of transporting ships spilling it in the water, and air pollution due to increasing production of greenhouse gases, such as CO₂ and CH₄ etc., into the atmosphere. The lands, agricultural fields, and groundwater are also contaminated by the infamous chemicals viz., polycyclic aromatic hydrocarbons, pyrethroids pesticides, bisphenol-A, and dioxanes. Therefore, bioremediation might function as a convenient alternative to restore a clean environment. However, at present, the majority of bioremediation reports are limited to the natural capabilities of microbial enzymes. Synthetic biology with uncompromised supervision of ethical standards could help to outsmart nature's engineering, such as the CETCH cycle for improved CO₂ fixation. Additionally, a blend of synthetic biology with machine learning algorithms could expand the possibilities of bioengineering. This review summarized current state-of-the-art knowledge of the data-assisted enzyme redesigning to actively promote new research on important enzymes to ameliorate the environment.

Regulation of β-amylase synthesis: a brief overview

BACKGROUND

The major activity of β-amylase (BMY) is the production of maltose by the hydrolytic degradation of starch. BMY is found to be produced by some plants and few microorganisms only. The industrial importance of the enzyme warrants its application in a larger scale with the help of genetic engineering, for which the regulatory mechanism is to be clearly understood.

RESULTS AND CONCLUSION

In plants, the activities of BMY are regulated by various environmental stimuli including stress of drought, cold and heat. In vascular plant, Arabidopsis sp. the enzyme is coded by nine BAM genes, whereas in most bacteria, BMY enzymes are coded by the spoII gene family. The activities of these genes are in turn controlled by various compounds. Production and inhibition of the microbial BMY is regulated by the activation and inactivation of various BAM genes. Various types of transcriptional regulators associated with the plant- BMYs regulate the production of BMY enzyme. The enhancement in the expression of such genes reflects evolutionary significance. Bacterial genes, on the other hand, as exemplified by Bacillus sp and Clostridium sp, clearly depict the importance of a single regulatory gene, the absence or mutation of which totally abolishes the BMY activity.

Methods for enzyme library creation: Which one will you choose?: A guide for novices and experts to introduce genetic diversity

Enzyme engineering allows to explore sequence diversity in search for new properties. The scientific literature is populated with methods to create enzyme libraries for engineering purposes, however, choosing a suitable method for the creation of mutant libraries can be daunting, in particular for the novices. Here, we address both novices and experts: how can one enter the arena of enzyme library design and what guidelines can advanced users apply to select strategies best suited to their purpose? Section I is dedicated to the novices and presents an overview of established and standard methods for library creation, as well as available commercial solutions. The expert will discover an up-to-date tool to freshen up their repertoire (Section I) and learn of the newest methods that are likely to become a mainstay (Section II). We focus primarily on in vitro methods, presenting the advantages of each method. Our ultimate aim is to offer a selection of methods/strategies that we believe to be most useful to the enzyme engineer, whether a first-timer or a seasoned user.

Advance in glycosyltransferases, the important bioparts for production of diversified ginsenosides

Ginsenosides are a series of glycosylated triterpenoids predominantly originated from Panax species with multiple pharmacological activities such as anti-aging, mediatory effect on the immune system and the nervous system. During the biosynthesis of ginsenosides, glycosyltransferases play essential roles by transferring various sugar moieties to the sapogenins in contributing to form structure and bioactivity diversified ginsenosides, which makes them important bioparts for synthetic biology-based production of these valuable ginsenosides. In this review, we summarized the functional elucidated glycosyltransferases responsible for ginsenoside biosynthesis, the advance in the protein engineering of UDP-glycosyltransferases (UGTs) and their application with the aim to provide in-depth understanding on ginsenoside-related UGTs for the production of rare ginsenosides applying synthetic biology-based microbial cell factories in the future.

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%).

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Improving the Cβ Stereoselectivity of l-Threonine Aldolase for the Synthesis of l-threo-4-Methylsulfonylphenylserine by Modulating the Substrate-Binding Pocket To Control the Orientation of the Substrate Entrance

l-Threonine aldolase from Actinocorallia herbida (AhLTA) is an ideal catalyst for producing l-threo-4-methylsulfonylphenylserine [(2S,3R)-1 b], a key chiral precursor for florfenicol and thiamphenicol. The moderate C_β stereoselectivity is the main obstacle to the industrial application of AhLTA. To address this issue, a combinatorial active-site saturation test (CAST) together with sequence conservatism analysis was applied to engineer the AhLTA toward improved C_β stereoselectivity. The optical mutant Y314R could asymmetrically synthesize l-threo-4-methylsulfonylphenylserine with 81 % diastereomeric excess (de), which is 23 % higher than wild-type AhLTA. Molecular dynamic (MD) simulations revealed that the mechanism for the improvement in C_β stereoselectivity of Y314R is due to the acylamino group of residues Arg314 controlling the orientation of substrate 4-methylsulfonyl benzaldehyde (1 a) in the active pocket by directed interaction with the methylsulfonyl group; this leads to asymmetric synthesis of l-threo-4-methylsulfonylphenylserine. The success in this study demonstrates that direct control of substrates in an active pocket is an attract strategy to address the C_β stereoselectivity problem of LTA and contribute to the industrial application of LTA.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Structure-Guided Rational Design of a Mono- and Diacylglycerol Lipase from Aspergillus oryzae: A Single Residue Mutant Increases the Hydrolysis Ability

Engineering of enzymes on the basis of protein structures are rational and efficient approaches to acquire biocatalysts of desired performances. In this study, we focused on a special mono- and diacylglycerol lipase (MDGL) isolated from the lipolytic enzyme-enriched fungus Aspergillus oryzae and discovered improved variants based on its crystal structure. We first solved the crystal structure of Aspergillus oryzae lipase (AOL) at 1.7 Å resolution. Structure analysis and sequence alignment of AOL and other MDGLs revealed that the residue V269 is of vital importance for catalysis. Replacement of the V269 in AOL with the corresponding residues in other MDGLs has led to noticeable changes in hydrolysis without sacrificing the thermostability and substrate specificity. Among the investigated variants, V269D exhibited about a six-fold higher hydrolysis activity compared to the wild type. Molecular dynamics simulations and protein-ligand interaction frequency analyses revealed that the Asp substitution enhanced the substrate affinity of AOL. Our work sheds light on understanding the catalytic process of AOL and helps tailoring MDGLs with desired catalytic performance to fulfill the demand for biotechnological applications.

Addressing the Selectivity of Enzyme Biosensors: Solutions and Perspectives

Enzymatic biosensors enjoy commercial success and are the subject of continued research efforts to widen their range of practical application. For these biosensors to reach their full potential, their selectivity challenges need to be addressed by comprehensive, solid approaches. This review discusses the status of enzymatic biosensors in achieving accurate and selective measurements via direct biocatalytic and inhibition-based detection, with a focus on electrochemical enzyme biosensors. Examples of practical solutions for tackling the activity and selectivity problems and preventing interferences from co-existing electroactive compounds in the samples are provided such as the use of permselective membranes, sentinel sensors and coupled multi-enzyme systems. The effect of activators, inhibitors or enzymatic substrates are also addressed by coupled enzymatic reactions and multi-sensor arrays combined with data interpretation via chemometrics. In addition to these more traditional approaches, the review discusses some ingenious recent approaches, detailing also on possible solutions involving the use of nanomaterials to ensuring the biosensors' selectivity. Overall, the examples presented illustrate the various tools available when developing enzyme biosensors for new applications and stress the necessity to more comprehensively investigate their selectivity and validate the biosensors versus standard analytical methods.

Single-Point Mutant Inverts the Stereoselectivity of a Carbonyl Reductase toward β-Ketoesters with Enhanced Activity

Enzyme stereoselectivity control is still a major challenge. To gain insight into the molecular basis of enzyme stereo-recognition and expand the source of antiPrelog carbonyl reductase toward β-ketoesters, rational enzyme design aiming at stereoselectivity inversion was performed. The designed variant Q139G switched the enzyme stereoselectivity toward β-ketoesters from Prelog to antiPrelog, providing corresponding alcohols in high enantiomeric purity (89.1-99.1 % ee). More importantly, the well-known trade-off between stereoselectivity and activity was not found. Q139G exhibited higher catalytic activity than the wildtype enzyme, the enhancement of the catalytic efficiency (k_cat /K_m ) varied from 1.1- to 27.1-fold. Interestingly, the mutant Q139G did not lead to reversed stereoselectivity toward aromatic ketones. Analysis of enzyme-substrate complexes showed that the structural flexibility of β-ketoesters and a newly formed cave together facilitated the formation of the antiPrelog-preferred conformation. In contrast, the relatively large and rigid structure of the aromatic ketones prevents them from forming the antiPrelog-preferred conformation.

An effective computational-screening strategy for simultaneously improving both catalytic activity and thermostability of α-l-rhamnosidase

Catalytic efficiency and thermostability are the two most important characteristics of enzymes. However, it is always tough to improve both catalytic efficiency and thermostability of enzymes simultaneously. In the present study, a computational strategy with double-screening steps was proposed to simultaneously improve both catalysis efficiency and thermostability of enzymes; and a fungal α-l-rhamnosidase was used to validate the strategy. As the result, by molecular docking and sequence alignment analysis within the binding pocket, seven mutant candidates were predicted with better catalytic efficiency. By energy variety analysis, A355N, S356Y, and D525N among the seven mutant candidates were predicted with better thermostability. The expression and characterization results showed the mutant D525N had significant improvements in both enzyme activity and thermostability. Molecular dynamics simulations indicated that the mutations located within the 5 Å range of the catalytic domain, which could improve root mean squared deviation, electrostatic, Van der Waal interaction, and polar salvation values, and formed water bridge between the substrate and the enzyme. The study indicated that the computational strategy based on the binding energy, conservation degree and mutation energy analyses was effective to develop enzymes with better catalysis and thermostability, providing practical approach for developing industrial enzymes.

Enzyme engineering and its industrial applications

Recently, there has been an increase in the demand for enzymes with modified activity, specificity, and stability. Enzyme engineering is an important tool to meet the demand for enzymes adjusted to different industrial processes. Knowledge of the structure and function of enzymes guides the choice of the best strategy for engineering enzymes. Each enzyme engineering strategy, such as rational design, directed evolution, and semi-rational design, has specific applications, as well as limitations, which must be considered when choosing a suitable strategy. Engineered enzymes can be optimized for different industrial applications by choosing the appropriate strategy. This review features engineered enzymes that have been applied in food, animal feed, pharmaceuticals, medical applications, bioremediation, biofuels, and detergents.

In-silico discovery of bifunctional enzymes with enhanced lignocellulose hydrolysis from microbiota big data

Due to the importance of using lignocellulosic biomass, it is always important to find an effective novel enzyme or enzyme cocktail or fusion enzymes. Identification of bifunctional enzymes through a metagenomic approach is an efficient method for converting agricultural residues and a beneficial way to reduce the cost of enzyme cocktail and fusion enzyme production. In this study, a novel stable bifunctional cellulase/xylanase, PersiCelXyn1 was identified from the rumen microbiota by the multi-stage in-silico screening pipeline and computationally assisted methodology. The enzyme exhibited the optimal activity at pH 5 and 50°C. Analyzing the enzyme activity at extreme temperature, pH, long-term storage, and presence of inhibitors and metal ions, confirmed the stability of the bifunctional enzyme under harsh conditions. Hydrolysis of the rice straw by PersiCelXyn1 showed its capability to degrade both cellulose and hemicellulose polymers. Also, the enzyme improved the degradation of various biomass substrates after 168 h of hydrolysis. Our results demonstrated the power of the multi-stage in-silico screening to identify bifunctional enzymes from metagenomic big data for effective bioconversion of lignocellulosic biomass.

Engineering Escherichia coli for the utilization of ethylene glycol

Background

A considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and fuels.

Results

In this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG assimilation.

Conclusion

Overall, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic parity with petrochemical feedstocks while sequestering carbon dioxide.

Flow biocatalysis 101: design, development and applications

Flow biocatalysis: where to start? This tutorial review aims to guide and inspire new-comers to the field to boost the potential of flow biocatalysis.

Harnessing the sponge microbiome for industrial biocatalysts

Within the marine sphere, host-associated microbiomes are receiving growing attention as prolific sources of novel biocatalysts. Given the known biocatalytic potential of poriferan microbial inhabitants, this review focuses on enzymes from the sponge microbiome, with special attention on their relevant properties and the wide range of their potential biotechnological applications within various industries. Cultivable bacterial and filamentous fungal isolates account for the majority of the enzymatic sources. Hydrolases, mainly glycoside hydrolases and carboxylesterases, are the predominant reported group of enzymes, with varying degrees of tolerance to alkaline pH and growing salt concentrations being common. Prospective areas for the application of these microbial enzymes include biorefinery, detergent, food and effluent treatment industries. Finally, alternative strategies to identify novel biocatalysts from the sponge microbiome are addressed, with an emphasis on modern -omics-based approaches that are currently available in the enzyme research arena. By providing this current overview of the field, we hope to not only increase the appetite of researchers to instigate forthcoming studies but also to stress how basic and applied research can pave the way for new biocatalysts from these symbiotic microbial communities in a productive fashion. KEY POINTS: • The sponge microbiome is a burgeoning source of industrial biocatalysts. • Sponge microbial enzymes have useful habitat-related traits for several industries. • Strategies are provided for the future discovery of microbial enzymes from sponges.

The various levels of integration of chemo- and bio-catalysis towards hybrid catalysis

Hybrid catalysis is an emerging concept that combines chemo- and biocatalysts in a wide variety of approaches. Combining the specifications and advantages of multiple disciplines, it is a very promising way to diversify tomorrow's catalysis.