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

Mining and rational design of psychrophilic catalases using metagenomics and deep learning models

A complete catalase-encoding gene, designated soiCat1, was obtained from soil samples via metagenomic sequencing, assembly, and gene prediction. soiCat1 showed 73% identity to a catalase-encoding gene of Mucilaginibacter rubeus strain P1, and the amino acid sequence of soiCAT1 showed 99% similarity to the catalase of a psychrophilic bacterium, Pedobacter cryoconitis. soiCAT1 was identified as a psychrophilic enzyme due to the low optimum temperature predicted by the deep learning model Preoptem, which was subsequently validated through analysis of enzymatic properties. Experimental results showed that soiCAT1 has a very narrow range of optimum temperature, with maximal specific activity occurring at the lowest test temperature (4 °C) and decreasing with increasing reaction temperature from 4 to 50 °C. To rationally design soiCAT1 with an improved temperature range, soiCAT1 was engineered through site-directed mutagenesis based on molecular evolution data analyzed through position-specific amino acid possibility calculation. Compared with the wild type, one mutant, soiCAT1S205K, exhibited an extended range of optimum temperature ranging from 4 to 20 °C. The strategies used in this study may shed light on the mining of genes of interest and rational design of desirable proteins. KEY POINTS: • Numerous putative catalases were mined from soil samples via metagenomics. • A complete sequence encoding a psychrophilic catalase was obtained. • A mutant psychrophilic catalase with an extended range of optimum temperature was engineered through site-directed mutagenesis.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Decoupling Fluorous Protein Coatings Yield Heat-Stable and Intrinsically Sterile Bioformulations

Thermal inactivation is a major bottleneck to the scalable production, storage, and transportation of protein-based reagents and therapies. Failures in temperature control both compromise protein bioactivity and increase the risk of microorganismal contamination. Herein, we report the rational design of fluorochemical additives that promiscuously bind to and coat the surfaces of proteins to enable their stable dispersion within fluorous solvents. By replacing traditional aqueous liquids with fluorinated media, this strategy conformationally rigidifies proteins to preserve their structure and function at extreme temperatures (≥90 °C). We show that fluorous protein formulations resist contamination by bacterial, fungal, and viral pathogens, which require aqueous environments for survival, and display equivalent serum bioavailability to standard saline samples in animal models. Importantly, by designing dispersants that decouple from the protein surface in physiologic solutions, we deliver a fluorochemical formulation that does not alter the pharmacologic function or safety profile of the functionalized protein in vivo. As a result, this nonaqueous protein storage paradigm is poised to open technological opportunities in the design of shelf-stable protein reagents and biopharmaceuticals.

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.

Impact of single-residue mutations on protein thermal stability: The case of threonine 83 of BC2L-CN lectin

The thermal stability of trimeric lectin BC2L-CN was investigated and found to be considerably altered when mutating residue 83, originally a threonine, located at the fucose-binding loop. Mutants were analyzed using differential scanning calorimetry and isothermal microcalorimetry. Although most mutations decreased the affinity of the protein for oligosaccharide H type 1, six mutations increased the melting temperature (Tm) by >5 °C; one mutation, T83P, increased the Tm value by 18.2 °C(T83P, Tm = 96.3 °C). In molecular dynamic simulations, the investigated thermostable mutants, T83P, T83A, and T83S, had decreased fluctuations in the loop containing residue 83. In the T83S mutation, the side-chain hydroxyl group of serine formed a hydrogen bond with a nearby residue, suggesting that the restricted movement of the side-chain resulted in fewer fluctuations and enhanced thermal stability. Residue 83 is located at the interface and near the upstream end of the equivalent loop in a different protomer; therefore, fluctuations by this residue likely propagate throughout the loop. Our study of the dramatic change in thermal stability by a single amino acid mutation provides useful insights into the rational design of protein structures, especially the structures of oligomeric proteins.

Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development

Carbohydrate-based surfactants are amphiphilic compounds containing hydrophilic moieties linked to hydrophobic aglycones. More specifically, carbohydrate esters are biosourced and biocompatible surfactants derived from inexpensive renewable raw materials (sugars and fatty acids). Their unique properties allow them to be used in various areas, such as the cosmetic, food, and medicine industries. These multi-applications have created a worldwide market for biobased surfactants and consequently expectations for their production. Biobased surfactants can be obtained from various processes, such as chemical synthesis or microorganism culture and surfactant purification. In accordance with the need for more sustainable and greener processes, the synthesis of these molecules by enzymatic pathways is an opportunity. This work presents a state-of-the-art lipase action mode, with a focus on the active sites of these proteins, and then on four essential parameters for optimizing the reaction: type of lipase, reaction medium, temperature, and ratio of substrates. Finally, this review discusses the latest trends and recent developments, showing the unlimited potential for optimization of such enzymatic syntheses.

Directed Evolution of the UDP-Glycosyltransferase UGTBL1 for Highly Regioselective and Efficient Biosynthesis of Natural Phenolic Glycosides

The O-glycosylation of polyphenols for the synthesis of glycosides has garnered substantial attention in food research applications. However, the practical utility of UDP-glycosyltransferases (UGTs) is significantly hindered by their low catalytic efficiency and suboptimal regioselectivity. The concurrent optimization of the regioselectivity and activity during the glycosylation of polyphenols presents a formidable challenge. Here, we addressed the long-standing activity-regioselectivity tradeoff in glycosyltransferase UGTBL1 through systematic enzyme engineering. The optimal combination of mutants, N61S/I62M/D63W/A208R/P218W/R282W (SMWRW1W2), yielded a 6.1-fold improvement in relative activity and a 17.3-fold increase in the ratio of gastrodin to para-hydroxybenzyl alcohol-4'-O-β-glucoside (with 89.5% regioselectivity for gastrodin) compared to those of the wild-type enzyme and ultimately allowed gram-scale production of gastrodin (1,066.2 mg/L) using whole-cell biocatalysis. In addition, variant SMWRW1W2 exhibited a preference for producing phenolic glycosides from several substrates. This study lays the foundation for the engineering of additional UGTs and the practical applications of UGTs in regioselective retrofitting.

Rational Redesign of Chitosanase to Enhance Thermostability and Catalytic Activity to Produce Chitooligosaccharides with a Relatively High Degree of Polymerization

Chitooligosaccharides (hdpCOS) with a high degree of polymerization (hdp, DP 4-10) generally have greater biological activities than those of low-DP (ldp, DP 2-3) COS. Chitosanase from Bacillus amyloliquefaciens KCP2 (Csn46) can degrade chitosan to more hdpCOS at high temperature (70 °C), but low thermal stability at this temperature makes it unsuitable for industrial application; the wild-type enzyme can only produce COS (DP 2-4) at lower temperatures. Several thermostable mutants were obtained by modifying chitosanase using a comprehensive strategy based on a computer-aided mutant design. A combination of four beneficial single-point mutations (A129L/T175 V/K70T/D34G) to Csn46 was selected to obtain a markedly improved mutant, Mut4, with a half-life at 60 °C extended from 34.31 to 690.80 min, and the specific activity increased from 1671.73 to 3528.77 U/mg. Mut4 produced COS with DPs of 2-4 and 2-7 at 60 and 70 °C, respectively. Therefore, Mut4 has the potential to be applied to the industrial-scale preparation of hdpCOS with high biological activity.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Recent advances in simultaneous thermostability-activity improvement of industrial enzymes through structure modification

Engineered thermostable microbial enzymes are widely employed to catalyze chemical reactions in numerous industrial sectors. Although high thermostability is a prerequisite of industrial applications, enzyme activity is usually sacrificed during thermostability improvement. Therefore, it is vital to select the common and compatible strategies between thermostability and activity improvement to reduce mutants̕ libraries and screening time. Three functional protein engineering approaches, including directed evolution, rational design, and semi-rational design, are employed to manipulate protein structure on a genetic basis. From a structural standpoint, integrative strategies such as increasing substrate affinity; introducing electrostatic interaction; removing steric hindrance; increasing flexibility of the active site; N- and C-terminal engineering; and increasing intramolecular and intermolecular hydrophobic interactions are well-known to improve simultaneous activity and thermostability. The current review aims to analyze relevant strategies to improve thermostability and activity simultaneously to circumvent the thermostability and activity trade-off of industrial enzymes.

Improving the activity and expression level of a phthalate-degrading enzyme by a combination of mutagenesis strategies and strong promoter replacement

Phthalic acid esters (PAEs) are widely used plasticizers found in consumer products, which enter the environment and pose severe threats to human health. Here, a new PAE-degrading enzyme EstJ6 was modified by combining mutagenesis strategies and a strong promoter replacement to improve its catalytic activity and expression level. Four mutants with enhanced activity were obtained by random mutation, among which EstJ6M1.1 exhibited the highest catalytic activity with an increase in catalytic activity by 2.9-fold toward dibutyl phthalate (DBP) than that of the wild-type (WT) enzyme. With these mutants as a template, a variant EstJ6M2 with 3.1-fold higher catalytic activity and 4.61 times higher catalytic efficiency (Kcat/Km) was identified by staggered extension PCR. Targeting four mutation sites of EstJ6M2, a variant EstJ6M3.1 was gained by site-directed saturation mutagenesis and displayed 4.3-fold higher activity and 5.97 times higher Kcat/Km than WT. The expression level of three mutants EstJ6M1.1, EstJ6M2, and EstJ6M3.1, as well as the WT, increased nearly threefold after a strong promoter replacement. These results provide a proof-theoretical basis and practicable pipeline for applying PAE-degrading enzymes.

Thermostability engineering of industrial enzymes through structure modification

Thermostability is an essential requirement of enzymes in the industrial processes to catalyze the reactions at high temperatures; thus, enzyme engineering through directed evolution, semi-rational design and rational design are commonly employed to construct desired thermostable mutants. Several strategies are implemented to fulfill enzymes' thermostability demand including decreasing the entropy of the unfolded state through substitutions Gly → Xxx or Xxx → Pro, hydrogen bond, salt bridge, introducing two different simultaneous interactions through single mutant, hydrophobic interaction, filling the hydrophobic cavity core, decreasing surface hydrophobicity, truncating loop, aromatic-aromatic interaction and introducing positively charged residues to enzyme surface. In the current review, horizons about compatibility between secondary structures and substitutions at preferable structural positions to generate the most desirable thermostability in industrial enzymes are broadened. KEY POINTS: • Protein engineering is a powerful tool for generating thermostable industrial enzymes. • Directed evolution and rational design are practical approaches in enzyme engineering. • Substitutions in preferable structural positions can increase thermostability.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

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.