Computational tools for designing and engineering enzymes

Fusion of enzymatic proteins: Enhancing biological activities and facilitating biological modifications

The fusion of enzymatic proteins represents a dynamic frontier in biotechnology and enzymatic engineering. This in-depth review looks at the many different ways that fusion proteins can be used, showing their importance in biosensing, gene therapy, targeted drug delivery, and biocatalysis. Fusion proteins have shown an astounding ability to improve and fine-tune biological functions by combining the most beneficial parts of different enzymes. Our first step is to explain how protein fusion increases biological functions. This will provide a broad picture of how this phenomenon has changed many fields. We dissect the intricate mechanisms through which fusion proteins orchestrate cellular processes, underscoring their potential to revolutionize the landscape of molecular biology. We also explore the complicated world of structural analysis and design strategies, stressing the importance of molecular insights for making the fusion protein approach work better. These insights broaden understanding of the underlying principles and illuminate the path toward unlocking untapped potential. The review also introduces cutting-edge techniques for constructing fusion protein libraries, such as DNA shuffling and phage display. These new methods allow scientists to build a molecular orchestra with an unprecedented level of accuracy, and thus use fusion proteins to their full potential in various situations. In conclusion, we provide a glimpse into the current challenges and prospects in fusion protein research, shedding light on recent advancements that promise to reshape the future of biotechnology. As we make this interesting journey through the field of enzymatic protein combination, it becomes clear that the fusion paradigm is about to start a new era of innovation that will push the limits of what is possible in biology and molecular engineering.

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.

Prospective identification of extracellular triacylglycerol hydrolase with conserved amino acids in Amycolatopsis tolypomycina's high G+C genomic dataset

Extracellular triacylglycerol hydrolases (ETH) play a critical role for microorganisms, acting as essential tools for lipid breakdown and survival in challenging environments. The pursuit of more effective ETH genes and enzymes through evolution holds significant potential for enhancing living conditions. This study employs a proteogenomic approach to identify high G+C ETH in a notable Gram-positive bacterium, Amycolatopsis tolypomycina. Utilizing knowledge from genome and machine learning algorithms, prospective ETH genes/enzymes were identified. Notably, the ETH structural conserved accessibility to solvent clearly indicated the specific sixteen residues (GLY50, PRO93, GLY141, ASP148, GLY151, ASP172, ALA176, GLY195, TYR196, SER197, GLN198, GLY199, GLY200, GLY225, PRO327, ASP336) with no frequency. By pinpointing key residues and understanding their role, this study sets the stage for enhancing ETH performance through computational proteogenomic and contributes to the broader field of enzyme engineering, facilitating the development of more efficient and versatile ETH enzymes tailored to specific industrial or environmental contexts.

Rational Design for Enhancing Cellobiose Dehydrogenase Activity and Its Synergistic Role in Straw Degradation

Addressing the demand for efficient biological degradation of straw, this study employed rational design coupled with structural biology and enzyme engineering techniques to enhance the catalytic activity of cellobiose dehydrogenase (PsCDH, CDH form Pycnoporus sanguineus). By predicting and modifying the active site and key amino acids of PsCDH, several CDH immobilized enzyme preparations with higher catalytic activities were successfully obtained. The excellent mutant T1 (C286Y/A461H/F464R) exhibited a 2.7-fold increase in enzyme activity compared to the wild type. Simulated calculations indicated that the enhancement of catalytic activity was primarily due to the formation of additional intermolecular interactions between CDH and the substrate, as well as the enlargement of the substrate pocket to reduce steric hindrance effects. Additionally, molecular dynamics simulation analysis revealed a potential correlation between structural stability and enzyme activity. When PsCDH was added to a multienzyme synergistic straw degradation system, the cellulose degradation rate increased by 1.84-fold. Moreover, mutant T1 further increased the degradation of lignocellulose in the mixed system. This study provides efficient enzyme sources and modification strategies for the high-efficiency biological conversion of straw and unconventional feedstock degradation, thereby possessing significant academic value and application prospects.

Advancements in the Heterologous Expression of Sucrose Phosphorylase and Its Molecular Modification for the Synthesis of Glycosylated Products

Sucrose phosphorylase (SPase), a member of the glycoside hydrolase GH13 family, possesses the ability to catalyze the hydrolysis of sucrose to generate α-glucose-1-phosphate and can also glycosylate diverse substrates, showcasing a wide substrate specificity. This enzyme has found extensive utility in the fields of food, medicine, and cosmetics, and has garnered significant attention as a focal point of research in transglycosylation enzymes. Nevertheless, SPase encounters numerous obstacles in industrial settings, including low enzyme yield, inadequate thermal stability, mixed regioselectivity, and limited transglycosylation activity. In-depth exploration of efficient expression strategies and molecular modifications based on the crystal structure and functional information of SPase is now a critical research priority. This paper systematically reviews the source microorganisms, crystal structure, and catalytic mechanism of SPase, summarizes diverse heterologous expression systems based on expression hosts and vectors, and examines the application and molecular modification progress of SPase in synthesizing typical glycosylated products. Additionally, it anticipates the broad application prospects of SPase in industrial production and related research fields, laying the groundwork for its engineering modification and industrial application.

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.

In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

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.

The shortest path method (SPM) webserver for computational enzyme design

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.

Computer-Aided Reconstruction and Application of Bacillus halodurans S7 Xylanase with Heat and Alkali Resistance

β-1,4-Endoxylanase is the most critical hydrolase for xylan degradation during lignocellulosic biomass utilization. However, its poor stability and activity in hot and alkaline environments hinder its widespread application. In this study, BhS7Xyl from Bacillus halodurans S7 was improved using a computer-aided design through isothermal compressibility (βT) perturbation engineering and by combining three thermostability prediction algorithms (ICPE-TPA). The best variant with remarkable improvement in specific activity, heat resistance (70 °C), and alkaline resistance (both pH 9.0 and 70 °C), R69F/E137M/E145L, exhibited a 4.9-fold increase by wild-type in specific activity (1368.6 U/mg), a 39.4-fold increase in temperature half-life (458.1 min), and a 57.6-fold increase in pH half-life (383.1 min). Furthermore, R69F/E137M/E145L was applied to the hydrolysis of agricultural waste (corncob and hardwood pulp) to efficiently obtain a higher yield of high-value xylooligosaccharides. Overall, the ICPE-TPA strategy has the potential to improve the functional performance of enzymes under extreme conditions for the high-value utilization of lignocellulosic biomass.

Sequence- and Structure-Based Mining of Thermostable D-Allulose 3-Epimerase and Computer-Guided Protein Engineering To Improve Enzyme Activity

D-Allulose, a functional sweetener, can be synthesized from fructose using D-allulose 3-epimerase (DAEase). Nevertheless, a majority of the reported DAEases have inadequate stability under harsh industrial reaction conditions, which greatly limits their practical applications. In this study, big data mining combined with a computer-guided free energy calculation strategy was employed to discover a novel DAEase with excellent thermostability. Consensus sequence analysis of flexible regions and comparison of binding energies after substrate docking were performed using phylogeny-guided big data analyses. TtDAE from Thermogutta terrifontis was the most thermostable among 358 candidate enzymes, with a half-life of 32 h at 70 °C. Subsequently, structure-guided virtual screening and a customized strategy based on a combinatorial active-site saturation test/iterative saturation mutagenesis were utilized to engineer TtDAE. Finally, the catalytic activity of the M4 variant (P105A/L14C/T63G/I65A) was increased by 5.12-fold. Steered molecular dynamics simulations indicated that M4 had an enlarged substrate-binding pocket, which enhanced the fit between the enzyme and the substrate. The approach presented here, combining DAEases mining with further rational modification, provides guidance for obtaining promising catalysts for industrial-scale production.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Structural Analysis of Non-native Peptide-Based Catalysts Using 2D NMR-Guided MD Simulations

Proteins and enzymes generally achieve their functions by creating well-defined 3D architectures that pre-organize reactive functionalities. Mimicking this approach to supramolecular pre-organization is leading to the development of highly versatile artificial chemical environments, including new biomaterials, medicines, artificial enzymes, and enzyme-like catalysts. The use of β-turn and α-helical motifs is one approach that enables the precise placement of reactive functional groups to enable selective substrate activation and reactivity/selectivity that approaches natural enzymes. Our recent work has demonstrated that helical peptides can serve as scaffolds for pre-organizing two reactive groups to achieve enzyme-like catalysis. In this study, we used CYANA and AmberTools to develop a computational approach for determining how the structure of our peptide catalysts can lead to enhancements in reactivity. These results support our hypothesis that the bifunctional nature of the peptide enables catalysis by pre-organizing the two catalysts in reactive conformations that accelerate catalysis by proximity. We also present evidence that the low reactivity of monofunctional peptides can be attributed to interactions between the peptide-bound catalyst and the helical backbone, which are not observed in the bifunctional peptide.

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

In Silico Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations

The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase–PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase–PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future.

Evolution of tunnels in α/β-hydrolase fold proteins—What can we learn from studying epoxide hydrolases?

The evolutionary variability of a protein’s residues is highly dependent on protein region and function. Solvent-exposed residues, excluding those at interaction interfaces, are more variable than buried residues whereas active site residues are considered to be conserved. The abovementioned rules apply also to α/β-hydrolase fold proteins—one of the oldest and the biggest superfamily of enzymes with buried active sites equipped with tunnels linking the reaction site with the exterior. We selected soluble epoxide hydrolases as representative of this family to conduct the first systematic study on the evolution of tunnels. We hypothesised that tunnels are lined by mostly conserved residues, and are equipped with a number of specific variable residues that are able to respond to evolutionary pressure. The hypothesis was confirmed, and we suggested a general and detailed way of the tunnels’ evolution analysis based on entropy values calculated for tunnels’ residues. We also found three different cases of entropy distribution among tunnel-lining residues. These observations can be applied for protein reengineering mimicking the natural evolution process. We propose a ‘perforation’ mechanism for new tunnels design via the merging of internal cavities or protein surface perforation. Based on the literature data, such a strategy of new tunnel design could significantly improve the enzyme’s performance and can be applied widely for enzymes with buried active sites.

So far very little is known about proteins tunnels evolution. The goal of this study is to evaluate the evolution of tunnels in the family of soluble epoxide hydrolases—representatives of numerous α/β-hydrolase fold enzymes. As a result two types of tunnels evolution analysis were proposed (a general and a detailed approach), as well as a ‘perforation’ mechanism which can mimic native evolution in proteins and can be used as an additional strategy for enzymes redesign.

Recent Advances Regarding the Physiological Functions and Biosynthesis of D-Allulose

D-Allulose, a generally regarded as safe (GRAS) sugar, is rare in nature. It is among the most promising sweeteners for future use due to its low caloric content, sucrose-like taste, and unique functions. D-Allulose has many physiological effects, such as antiobesity, antihyperglycemia, antidiabetes, anti-inflammatory, antioxidant, and neuroprotective effects. Therefore, D-allulose has important application value in the food, pharmaceutical, and healthcare industries. However, the high cost of D-allulose production limits its large-scale application. Currently, biotransformation is very attractive for D-allulose synthesis, with the two main methods of biosynthesis being the Izumoring strategy and the DHAP-dependent aldolase strategy. This article reviews recent advances regarding the physiological functions and biosynthesis of D-allulose. In addition, future perspectives on the production of D-allulose are presented.

Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases

Simple Summary

Solvent-Accessible Surface Area (SASA) as the one dimensional structure property of the protein considers as the measuring the exposure of an amino acid residue to the solvent in one protein. It is an important structural property as the active sites of proteins are mostly located on the protein surfaces. The aim of this paper is to provide the clear information on different Amycolatopsis eburnea lipases based on the SASA patterns. This information could help in recognizing the structural stability and conformation as well as precise clustering them for revealing lipase evolution.

Abstract

The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglomerative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.

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.

In Silico Prediction Methods for Site-Saturation Mutagenesis

Directed enzyme evolution has proven to be a powerful means to endow biocatalysts with novel catalytic repertoires. Apart from completely random gene mutagenesis, site-directed or site-saturation mutagenesis requires a semi-rational selection of the amino acid positions or the substituted residues, which can dramatically reduce the screening efforts in protein engineering. To this end, in silico prediction methods play a pivotal role in targeting site-saturation mutagenesis. In this chapter, we provide two distinct computational methods, (a) conformational dynamics-guided design and (b) protein-ligand interaction fingerprinting analysis, to identify specific positions for site-saturation mutagenesis toward manipulating substrate specificity/stereoselectivity of an alcohol dehydrogenase, and improving activity of a carboxylic acid reductase, respectively.

Modern strategies for the diversification of the supply of natural compounds - the case of alkaloid painkillers

Plant-derived natural compounds are used for treating diseases since the beginning of humankind. The supply of many plant-derived natural compounds for medicinal purposes, such as thebaine, morphine, and codeine, is, nowadays, majorly dependent on opium poppy crop harvesting. This dependency puts an extra risk factor in ensuring the supply chain because crops are highly susceptible to environmental factors. Emerging technologies, such as biocatalysis, might help to solve this problem, by diversifying the sources of supply of these compounds. Here we review the first committed step in the production of alkaloid painkillers, the production of S-norcoclaurine, and the enzymes involved. The improvement of these enzymes can be carried out by experimental directed evolution and rational design strategies, supported by computational methods, to create variants that produce the S-norcoclaurine precursor for alkaloid painkillers in heterologous organisms, meeting the pharmaceutical industry standards and needs without depending on opium poppy crops.

Current and emerging tools of computational biology to improve the detoxification of mycotoxins

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we will focus on mycotoxins-fungi-produced toxins that contaminate food and feed-and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

In Silico Identification and Experimental Validation of Distal Activity-Enhancing Mutations in Tryptophan Synthase

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. Those residues are predicted to be crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the conformationally driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design has become within reach.

Development of aldolase-based catalysts for the synthesis of organic chemicals

Aldol chemicals are synthesized by condensation reactions between the carbon units of ketones and aldehydes using aldolases. The efficient synthesis of diverse organic chemicals requires intrinsic modification of aldolases via engineering and design, as well as extrinsic modification through immobilization or combination with other catalysts. This review describes the development of aldolases, including their engineering and design, and the selection of desired aldolases using high-throughput screening, to enhance their catalytic properties and perform novel reactions. Aldolase-containing catalysts, which catalyze the aldol reaction combined with other enzymatic and/or chemical reactions, can efficiently synthesize diverse complex organic chemicals using inexpensive and simple materials as substrates. We also discuss the current challenges and emerging solutions for aldolase-based catalysts.

Partial Consensus Design and Enhancement of Protein Function by Secondary-Structure-Guided Consensus Mutations

Consensus design (CD) is a representative sequence-based protein design method that enables the design of highly functional proteins by analyzing vast amounts of protein sequence data. This study proposes a partial consensus design (PCD) of a protein as a derivative approach of CD. The method replaces the target protein sequence with a consensus sequence in a secondary-structure-dependent manner (i.e., regionally dependent and divided into α-helix, β-sheet, and loop regions). In this study, we generated several artificial partial consensus l-threonine 3-dehydrogenases (PcTDHs) by PCD using the TDH from Cupriavidus necator (CnTDH) as a target protein. Structural and functional analysis of PcTDHs suggested that thermostability would be independently improved when consensus mutations are introduced into the loop region of TDHs. On the other hand, enzyme kinetic parameters (k_cat/K_m) and average productivity would be synergistically enhanced by changing the combination of the mutations-replacement of one region of CnTDH with a consensus sequence provided only negative effects, but the negative effects were nullified when the two regions were replaced simultaneously. Taken together, we propose the hypothesis that there are protein regions that encode individual protein properties, such as thermostability and activity, and that the introduction of consensus mutations into these regions could additively or synergistically modify their functions.

Rubisco Adaptation Is More Limited by Phylogenetic Constraint Than by Catalytic Trade-off

Rubisco assimilates CO2 to form the sugars that fuel life on earth. Correlations between rubisco kinetic traits across species have led to the proposition that rubisco adaptation is highly constrained by catalytic trade-offs. However, these analyses did not consider the phylogenetic context of the enzymes that were analyzed. Thus, it is possible that the correlations observed were an artefact of the presence of phylogenetic signal in rubisco kinetics and the phylogenetic relationship between the species that were sampled. Here, we conducted a phylogenetically resolved analysis of rubisco kinetics and show that there is a significant phylogenetic signal in rubisco kinetic traits. We re-evaluated the extent of catalytic trade-offs accounting for this phylogenetic signal and found that all were attenuated. Following phylogenetic correction, the largest catalytic trade-offs were observed between the Michaelis constant for CO2 and carboxylase turnover (∼21-37%), and between the Michaelis constants for CO2 and O2 (∼9-19%), respectively. All other catalytic trade-offs were substantially attenuated such that they were marginal (<9%) or non-significant. This phylogenetically resolved analysis of rubisco kinetic evolution also identified kinetic changes that occur concomitant with the evolution of C4 photosynthesis. Finally, we show that phylogenetic constraints have played a larger role than catalytic trade-offs in limiting the evolution of rubisco kinetics. Thus, although there is strong evidence for some catalytic trade-offs, rubisco adaptation has been more limited by phylogenetic constraint than by the combined action of all catalytic trade-offs.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Prospects of Using Biocatalysis for the Synthesis and Modification of Polymers

Trends in the dynamically developing application of biocatalysis for the synthesis and modification of polymers over the past 5 years are considered, with an emphasis on the production of biodegradable, biocompatible and functional polymeric materials oriented to medical applications. The possibilities of using enzymes not only as catalysts for polymerization but also for the preparation of monomers for polymerization or oligomers for block copolymerization are considered. Special attention is paid to the prospects and existing limitations of biocatalytic production of new synthetic biopolymers based on natural compounds and monomers from biomass, which can lead to a huge variety of functional biomaterials. The existing experience and perspectives for the integration of bio- and chemocatalysis in this area are discussed.

Tunnel engineering for modulating the substrate preference in cytochrome P450BsβHI

An active site is normally located inside enzymes, hence substrates should go through a tunnel to access the active site. Tunnel engineering is a powerful strategy for refining the catalytic properties of enzymes. Here, P450BsβHI (Q85H/V170I) derived from hydroxylase P450Bsβ from Bacillus subtilis was chosen as the study model, which is reported as a potential decarboxylase. However, this enzyme showed low decarboxylase activity towards long-chain fatty acids. Here, a tunnel engineering campaign was performed for modulating the substrate preference and improving the decarboxylation activity of P450BsβHI. The finally obtained BsβHI-F79A variant had a 15.2-fold improved conversion for palmitic acid; BsβHI-F173V variant had a 3.9-fold improved conversion for pentadecanoic acid. The study demonstrates how the substrate preference can be modulated by tunnel engineering strategy.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

Improved thermostability of proteinase K and recognizing the synergistic effect of Rosetta and FoldX approaches

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.

Computational studies of polyurethanases from Pseudomonas

Polyurethanes (PU) are multifunctional polymers, used in automotive industry, in coatings, rigid and flexible foams, and also in biomimetic materials. In the same way as all plastic waste, the incorrect disposal of these materials leads to the accumulation of polyurethanes in the environment. To reduce the amount of waste as well as add value to degradation products, bioremediation methods have been studied for waste management of PU. Enzymes of the hydrolases class have been experimentally tested for enzymatic degradation of PU, with very promising results. In this work, two enzymes that can degrade polyurethanes were studied by molecular dynamics simulations: a protease and an esterase, both from Pseudomonas. From molecular dynamics simulations analysis, it was observed the stability of the structures, both in the simulations of the free enzymes and in the simulations of the complexes with a PU monomer. Hydrogen bonds were formed with the monomer and the enzymes throughout the simulation time, and the interaction free energy was found to be strongly negative, pointing to strong interactions in both cases.

Computational Elucidation of Phylogenetic, Functional and Structural Features of Methioninase from Pseudomonas, Escherichia, Clostridium and Citrobacter Strains

BACKGROUND

L-Methioninase (EC 4.4.1.11; MGL) is a pyridoxal phosphate (PLP)-dependent enzyme that is produced by a variety of bacteria, fungi, and plants. L-methioninase, especially from Pseudomonas and Citrobacter sp., is considered as the efficient therapeutic enzyme, particularly in cancers such as glioblastomas, medulloblastoma, and neuroblastoma that are more sensitive to methionine starvation.

OBJECTIVE

The low stability is one of the main drawbacks of the enzyme; in this regard, in the current study, different features of the enzyme, including phylogenetic, functional, and structural from Pseudomonas, Escherichia, Clostridium, and Citrobacter strains were evaluated to find the best bacterial L-Methioninase.

METHODS

After the initial screening of L-Methioninase sequences from the above-mentioned bacterial strains, the three-dimensional structures of enzymes from Escherichia fergusonii, Pseudomonas fluorescens, and Clostridium homopropionicum were determined through homology modeling via GalaxyTBM server and refined by GalaxyRefine server.

RESULTS AND CONCLUSION

Afterwards, PROCHECK, verify 3D, and ERRAT servers were used for verification of the obtained models. Moreover, antigenicity, allergenicity, and physico-chemical analysis of enzymes were also carried out. In order to get insight into the interaction of the enzyme with other proteins, the STRING server was used. The secondary structure of the enzyme is mainly composed of random coils and alpha-helices. However, these outcomes should further be validated by wet-lab investigations.

Enhancing the promiscuity of a member of the Caspase protease family by rational design

The N-terminal cleavage of fusion tags to restore the native N-terminus of recombinant proteins is a challenging task and up to today, protocols need to be optimized for different proteins individually. Within this work, we present a novel protease that was designed in-silico to yield enhanced promiscuity toward different N-terminal amino acids. Two mutations in the active-site amino acids of human Caspase-2 were determined to increase the recognition of branched amino-acids, which show only poor binding capabilities in the unmutated protease. These mutations were determined by sequential and structural comparisons of Caspase-2 and Caspase-3 and their effect was additionally predicted using free-energy calculations. The two mutants proposed in the in-silico studies were expressed and in-vitro experiments confirmed the simulation results. Both mutants showed not only enhanced activities toward branched amino acids, but also smaller, unbranched amino acids. We believe that the created mutants constitute an important step toward generalized procedures to restore original N-termini of recombinant fusion proteins.

Zebra2: advanced and easy-to-use web-server for bioinformatic analysis of subfamily-specific and conserved positions in diverse protein superfamilies

Zebra2 is a highly automated web-tool to search for subfamily-specific and conserved positions (i.e. the determinants of functional diversity as well as the key catalytic and structural residues) in protein superfamilies. The bioinformatic analysis is facilitated by Mustguseal—a companion web-server to automatically collect and superimpose a large representative set of functionally diverse homologs with high structure similarity but low sequence identity to the selected query protein. The results are automatically prioritized and provided at four information levels to facilitate the knowledge-driven expert selection of the most promising positions on-line: as a sequence similarity network; interfaces to sequence-based and 3D-structure-based analysis of conservation and variability; and accompanied by the detailed annotation of proteins accumulated from the integrated databases with links to the external resources. The integration of Zebra2 and Mustguseal web-tools provides the first of its kind out-of-the-box open-access solution to conduct a systematic analysis of evolutionarily related proteins implementing different functions within a shared 3D-structure of the superfamily, determine common and specific patterns of function-associated local structural elements, assist to select hot-spots for rational design and to prepare focused libraries for directed evolution. The web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/zebra2, no login required.

Computational and structure-guided design of phosphoinositide substrate specificity into the tyrosine specific LMW-PTP enzyme

We have used a combination of computational and structure-based redesign of the low molecular weight protein tyrosine phosphatase, LMW-PTP, to create new activity towards phosphoinositide substrates for which the wild-type enzyme had little or no activity. The redesigned enzymes retain catalytic activity despite residue alterations in the active site, and kinetic experiments confirmed specificity for up to four phosphoinositide substrates. Changes in the shape and overall volume of the active site where critical to facilitate access of the new substrates for catalysis. The kinetics data suggest that both the position and the combination of amino acid mutations are important for specificity towards the phosphoinositide substrates. The introduction of basic residues proved essential to establish new interactions with the multiple phosphate groups in the inositol head, thus promoting catalytically productive complexes. The crystallographic structures of the top-ranking designs confirmed the computational predictions and showed that residue substitutions do not alter the overall folding of the phosphatase or the conformation of the active site P-loop. The engineered LMW-PTP mutants with new activities can be useful reagents in investigating cell signalling pathways and offer the potential for therapeutic applications.