Impact of the access tunnel engineering on catalysis is strictly ligand‐specific

Engineering an Ancestral Glycosyltransferase for Biosynthesis of 2-Phenylethyl-β-d-Glucopyranoside and Salidroside

Phenylethanoid glycosides (PhGs) are naturally occurring glycosides derived from plants with various biological activities. Glycosyltransferases catalyze the production of PhGs from phenylethanols via a transglycosylation reaction. The low activity and stability of glycosyltransferase limit its industrial application. An ancestral glycosyltransferase, UGTAn85, with heat resistance, alkali resistance, and high stability was resurrected using ancestral sequence reconstruction technology. This enzyme can efficiently convert phenylethanols to PhGs. The optimal reaction temperature and pH for UGTAn85 were found to be 70 °C and pH 10.0, respectively. This study employed a combination of structure-guided rational design and co-evolution analysis to enhance its catalytic activity. Potential mutation sites were identified through computer-aided design, including homology modeling, molecular docking, Rosetta dock design, molecular dynamics simulation, and co-evolution analysis. By targeted mutagenesis, the UGTAn85 mutant Q23E/N65D exhibited a 2.2-fold increase in enzyme activity (11.85 U/mg) and elevated affinity (Km = 0.11 mM) for 2-phenylethanol compared to UGTAn85. Following a fed-batch reaction, 36.16 g/L 2-phenylethyl-β-d-glucopyranoside and 51.49 g/L salidroside could be produced within 24 h, respectively. The findings in this study provide a new perspective on enhancing the stability and activity of glycosyltransferases, as well as a potential biocatalyst for the industrial production of PhGs.

Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics

Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Characterization of the Bottlenecks and Pathways for Inhibitor Dissociation from [NiFe] Hydrogenase

[NiFe] hydrogenases can act as efficient catalysts for hydrogen oxidation and biofuel production. However, some [NiFe] hydrogenases are inhibited by gas molecules present in the environment, such as O2 and CO. One strategy to engineer [NiFe] hydrogenases and achieve O2- and CO-tolerant enzymes is by introducing point mutations to block the access of inhibitors to the catalytic site. In this work, we characterized the unbinding pathways of CO in the complex with the wild-type and 10 different mutants of [NiFe] hydrogenase from Desulfovibrio fructosovorans using τ-random accelerated molecular dynamics (τRAMD) to enhance the sampling of unbinding events. The ranking provided by the relative residence times computed with τRAMD is in agreement with experiments. Extensive data analysis of the simulations revealed that from the two bottlenecks proposed in previous studies for the transit of gas molecules (residues 74 and 122 and residues 74 and 476), only one of them (residues 74 and 122) effectively modulates diffusion and residence times for CO. We also computed pathway probabilities for the unbinding of CO, O2, and H2 from the wild-type [NiFe] hydrogenase, and we observed that while the most probable pathways are the same, the secondary pathways are different. We propose that introducing mutations to block the most probable paths, in combination with mutations to open the main secondary path used by H2, can be a feasible strategy to achieve CO and O2 resistance in the [NiFe] hydrogenase from Desulfovibrio fructosovorans.

Computational investigation of cis-1,4-polyisoprene binding to the latex-clearing protein LcpK30

Latex clearing proteins (Lcps) catalyze the oxidative cleavage of the C = C bonds in cis-1,4-polyisoprene (natural rubber), producing oligomeric compounds that can be repurposed to other materials. The active catalytic site of Lcps is buried inside the protein structure, thus raising the question of how the large hydrophobic rubber chains can access the catalytic center. To improve our understanding of hydrophobic polymeric substrate binding to Lcps and subsequent catalysis, we investigated the interaction of a substrate model containing ten carbon-carbon double bonds with the structurally characterized LcpK30, using multiple computational tools. Prediction of the putative tunnels and cavities in the LcpK30 structure, using CAVER-Pymol plugin 3.0.3, fpocket and Molecular Dynamic (MD) simulations provided valuable insights on how substrate enters from the surface to the buried active site. Two dominant tunnels were discovered that provided feasible routes for substrate binding, and the presence of two hydrophobic pockets was predicted near the heme cofactor. The larger of these pockets is likely to accommodate the substrate and to determine the size distribution of the oligomers. Protein-ligand docking was carried out using GOLD software to predict the conformations and interactions of the substrate within the protein active site. Deeper insight into the protein-substrate interactions, including close-contacts, binding energies and potential cleavage sites in the cis-1,4-polyisoprene, were obtained from MD simulations. Our findings provide further justification that the protein-substrate complexation in LcpK30 is mainly driven by the hydrophobic interactions accompanied by mutual conformational changes of both molecules. Two potential binding modes were identified, with the substrate in either extended or folded conformations. Whilst binding in the extended conformation was most favorable, the folded conformation suggested a preference for cleavage of a central double bond, leading to a preference for oligomers with 5 to 6 C = C bonds. The results provide insight into further enzyme engineering studies to improve catalytic activity and diversify the substrate and product scope of Lcps.

Tunnel engineering of gas-converting enzymes for inhibitor retardation and substrate acceleration

Carbon monoxide dehydrogenase (CODH), formate dehydrogenase (FDH), hydrogenase (H2ase), and nitrogenase (N2ase) are crucial enzymatic catalysts that facilitate the conversion of industrially significant gases such as CO, CO2, H2, and N2. The tunnels in the gas-converting enzymes serve as conduits for these low molecular weight gases to access deeply buried catalytic sites. The identification of the substrate tunnels is imperative for comprehending the substrate selectivity mechanism underlying these gas-converting enzymes. This knowledge also holds substantial value for industrial applications, particularly in addressing the challenges associated with separation and utilization of byproduct gases. In this comprehensive review, we delve into the emerging field of tunnel engineering, presenting a range of approaches and analyses. Additionally, we propose methodologies for the systematic design of enzymes, with the ultimate goal of advancing protein engineering strategies.

pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support

SUMMARY

Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines.

AVAILABILITY AND IMPLEMENTATION

pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

Behavior of enzymes under high pressure in food processing: mechanisms, applications, and developments

High pressure processing (HPP) offers the benefits of safety, uniformity, energy-efficient, and low waste, which is widely applied for microbial inactivation and shelf-life extension for foods. Over the past forty years, HPP has been extensively researched in the food industry, enabling the inactivation or activation of different enzymes in future food by altering their molecular structure and active site conformation. Such activation or inactivation of enzymes effectively hinders the spoilage of food and the production of beneficial substances, which is crucial for improving food quality. This paper reviews the mechanism in which high pressure affects the stability and activity of enzymes, concludes the roles of key enzymes in the future food processed using high pressure technologies. Moreover, we discuss the application of modified enzymes based on high pressure, providing insights into the future direction of enzyme evolution under complex food processing conditions (e.g. high temperature, high pressure, high shear, and multiple elements). Finally, we conclude with prospects of high pressure technology and research directions in the future. Although HPP has shown positive effects in improving the future food quality, there is still a pressing need to develop new and effective combined processing methods, upgrade processing modes, and promote sustainable lifestyles.

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling

HaloTag labeling technology has introduced unrivaled potential in protein chemistry and molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, a tetramethylrhodamine- and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We showed that highly efficient labeling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from the wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates the need for expensive and laborious protein engineering.

Fast approximative methods for study of ligand transport and rational design of improved enzymes for biotechnologies

Acceleration of chemical reactions by the enzymes optimized using protein engineering represents one of the key pillars of the contribution of biotechnology towards sustainability. Tunnels and channels of enzymes with buried active sites enable the exchange of ligands, ions, and water molecules between the outer environment and active site pockets. The efficient exchange of ligands is a fundamental process of biocatalysis. Therefore, enzymes have evolved a wide range of mechanisms for repetitive conformational changes that enable periodic opening and closing. Protein-ligand interactions are traditionally studied by molecular docking, whereas molecular dynamics is the method of choice for studying conformational changes and ligand transport. However, computational demands make molecular dynamics impractical for screening purposes. Thus, several approximative methods have been recently developed to study interactions between a protein and ligand during the ligand transport process. Apart from identifying the best binding modes, these methods also provide information on the energetics of the transport and identify problematic regions limiting the ligand passage. These methods use approximations to simulate binding or unbinding events rapidly (calculation times from minutes to hours) and provide energy profiles that can be used to rank ligands or pathways. Here we provide a critical comparison of available methods, showcase their results on sample systems, discuss their practical applications in molecular biotechnologies and outline possible future developments.

In silico comparative structural and compositional analysis of glycoproteins of RSV to study the nature of stability and transmissibility of RSV A

The current scenario of COVID-19 makes us to think about the devastating diseases that kill so many people every year. Analysis of viral proteins contributes many things that are utterly useful in the evolution of therapeutic drugs and vaccines. In this study, sequence and structure of fusion glycoproteins and major surface glycoproteins of respiratory syncytial virus (RSV) were analysed to reveal the stability and transmission rate. RSV A has the highest abundance of aromatic residues. The Kyte-Doolittle scale indicates the hydrophilic nature of RSV A protein which leads to the higher transmission rate of this virus. Intra-protein interactions such as carbonyl interactions, cation-pi, and salt bridges were shown to be greater in RSV A compared to RSV B, which might lead to improved stability. This study discovered the presence of a network aromatic-sulphur interaction in viral proteins. Analysis of ligand binding pocket of RSV proteins indicated that drugs are performing better on RSV B than RSV A. It was also shown that increasing the number of tunnels in RSV A proteins boosts catalytic activity. This study will be helpful in drug discovery and vaccine development.

Evaluation of lipase access tunnels and analysis of substance transport in comparison with experimental data

Lipases (E.C. 3.1.1.3) have buried active sites and used access tunnels in the transport of substrates and products for biotransformation processes. Computational methods are used to predict the trajectory and energy profile of ligands through these tunnels, and they complement the experimental methodologies because they filter data, optimizing laboratory time and experimental costs. Access tunnels of Burkholderia cepacia lipase (BCL), Candida rugosa lipase (CRL), and porcine pancreas lipase (PPL) and the transport of fatty acids, alcohols and esters through the tunnels were evaluated using the online server CaverWeb V1.0, and server calculation results were compared with experimental data (productivity). BCL showed higher productivity with palmitic acid-C16:0 (4029.95 µmol/h mg); CRL obtained productivity for oleic acid-C18:1 (380.80 µmol/h mg), and PPL achieved productivity for lauric acid-C12:0 (71.27 µmol/h mg). The highest probability of transport for BCL is through the tunnels 1 and 2, for CRL through the tunnel 1, and for PPL through the tunnels 1, 2, 3 and 4. Thus, the best in silico result was the transport of the substrates palmitic acid and ethanol and product ethyl palmitate in tunnel 1 of BCL. This result corroborates with the best result for the productivity data (higher productivity for BCL with palmitic acid-4029.95 µmol/h mg). The combination of in silico evaluation and experimental data gave similar results, demonstrating that in silico approaches are a promising alternative for reducing screening tests and minimizing laboratory time in the bio-catalysis area by identifying the lipases with the greatest reaction potential, as in the case of this proposal.

Fusion of a proline-rich oligopeptide to the C-terminus of a ruminal xylanase improves catalytic efficiency

Xylanases are widely used in the degradation of lignocellulose and are important industrial enzymes. Therefore, increasing the catalytic activity of xylanases can improve their efficiency and performance. In this study, we introduced the C-terminal proline-rich oligopeptide of the rumen-derived XynA into XylR, a GH10 family xylanase. The optimum temperature and pH of the fused enzyme (XylR-Fu) were consistent with those of XylR; however, its catalytic efficiency was 2.48-fold higher than that of XylR. Although the proline-rich oligopeptide did not change the enzyme hydrolysis mode, the amount of oligosaccharides released from beechwood xylan by XylR-Fu was 17% higher than that released by XylR. This increase may be due to the abundance of proline in the oligopeptide, which plays an important role in substrate binding. Furthermore, circular dichroism analysis indicated that the proline-rich oligopeptide might increase the rigidity of the overall structure, thereby enhancing the affinity to the substrate and catalytic activity of the enzyme. Our study shows that the proline-rich oligopeptide enhances the catalytic efficiency of GH10 xylanases and provides a better understanding of the C-terminal oligopeptide-function relationships. This knowledge can guide the rational design of GH10 xylanases to improve their catalytic activity and provides clues for further applications of xylanases in industry.

Lowering energy consumption for fermentable sugar production from Ramulus mori: Engineered xylanase synergy and improved pretreatment strategy

Biorefinery of Ramulus mori with lower energy consumption through improved enzyme and pretreatment strategies was reported. Directed evolution and saturation mutagenesis were used for the modification of xylanase, the yield of fermentable sugars and the degree of synergy (DS) were determined for different pretreatment (seawater/non-seawater) and enzyme treatment groups (xylanase/cellulase/co-treatment). The dominant mutant I133A/Q143Y of Bispora sp. xylanase XYL10C_ΔN was obtained with improved specific activity (1860 U/mg), catalytic efficiency (1150 mL/s∙mg) at 40 °C, and thermostability (T₅₀ increased by 7 °C). With the pretreatment of seawater immersion, the highest yield of fermentable sugars for Ramulus mori at 40 °C reached 199 μmol/g when hydrolyzed with cellulase and I133A/Q143Y, with the highest DS of 2.6; this was 4.5-fold that of the group hydrolyzed by cellulase alone with non-seawater pretreatment. Thus, bioconversion of reducing sugar from Ramulus mori was improved significantly at lower temperatures, which provides an efficient and energy-saving wayfor biofuel production.

Loop engineering of a thermostable GH10 xylanase to improve low-temperature catalytic performance for better synergistic biomass-degrading abilities

Lignocellulosic biorefining for producing biofuels poses technical challenges. It is usually conducted over a long time using heat, making it energy intensive. In this study, we lowered the energy consumption of this process through an optimized enzyme and pretreatment strategy. First, the dominant mutant M137E/N269G of Bispora sp. MEY-1XYL10C_ΔN was obtained by directed evolution with highcatalytic efficiency (970 mL/s∙mg)and specific activity (2090 U/mg)at 37 °C, and thermostability was improved (T₅₀ increased by5 °C). After pretreatment with seawater immersionfollowing steam explosion,bagasse was co-treated with cellulase and M137E/N269G under mild conditions (37 °C), the resulting highest yield of fermentable sugars reached 219 µmol/g of bagasse,46% higher than that of the non-seawater treatment group, with the highest degree of synergy of 2.0. Pretreatment with seawater following steam explosion and synergistic hydrolysis through high activity xylanase and cellulase helped to achieve low energy degradation of lignocellulosic biomass.

Chemical modification of enzymes to improve biocatalytic performance

Improvement in intrinsic enzymatic features is in many instances a prerequisite for the scalable applicability of many industrially important biocatalysts. To this end, various strategies of chemical modification of enzymes are maturing and now considered as a distinct way to improve biocatalytic properties. Traditional chemical modification methods utilize reactivities of amine, carboxylic, thiol and other side chains originating from canonical amino acids. On the other hand, noncanonical amino acid- mediated 'click' (bioorthogoal) chemistry and dehydroalanine (Dha)-mediated modifications have emerged as an alternate and promising ways to modify enzymes for functional enhancement. This review discusses the applications of various chemical modification tools that have been directed towards the improvement of functional properties and/or stability of diverse array of biocatalysts.

Improvement of XYL10C_∆N catalytic performance through loop engineering for lignocellulosic biomass utilization in feed and fuel industries

BACKGROUND

Xylanase, an important accessory enzyme that acts in synergy with cellulase, is widely used to degrade lignocellulosic biomass. Thermostable enzymes with good catalytic activity at lower temperatures have great potential for future applications in the feed and fuel industries, which have distinct demands; however, the potential of the enzymes is yet to be researched.

RESULTS

In this study, a structure-based semi-rational design strategy was applied to enhance the low-temperature catalytic performance of Bispora sp. MEY-1 XYL10C_∆N wild-type (WT). Screening and comparisons were performed for the WT and mutant strains. Compared to the WT, the mutant M53S/F54L/N207G exhibited higher specific activity (2.9-fold; 2090 vs. 710 U/mg) and catalytic efficiency (2.8-fold; 1530 vs. 550 mL/s mg) at 40 °C, and also showed higher thermostability (the melting temperature and temperature of 50% activity loss after 30 min treatment increased by 7.7 °C and 3.5 °C, respectively). Compared with the cellulase-only treatment, combined treatment with M53S/F54L/N207G and cellulase increased the reducing sugar contents from corn stalk, wheat bran, and corn cob by 1.6-, 1.2-, and 1.4-folds, with 1.9, 1.2, and 1.6 as the highest degrees of synergy, respectively.

CONCLUSIONS

This study provides useful insights into the underlying mechanism and methods of xylanase modification for industrial utilization. We identified loop2 as a key functional area affecting the low-temperature catalytic efficiency of GH10 xylanase. The thermostable mutant M53S/F54L/N207G was selected for the highest low-temperature catalytic efficiency and reducing sugar yield in synergy with cellulase in the degradation of different types of lignocellulosic biomass.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Entrapment of glucose oxidase within gold converts it to a general monosaccharide-oxidase

We report that entrapping glucose oxidase (GOx) within metallic gold, expands its activity to become an oxidase for monosaccharides that do not have a natural enzyme with that activity-fructose and xylose-and that this entrapment also removes the enantioselectivity, rendering this enzyme capable of oxidizing the "wrong" L-enantiomer of glucose. These observations suggest that in this biomaterial adsorptive interactions of the outer regions of the protein with the gold cage, pull apart and widen the tunnel between the two monomeric units of GOx, to a degree that its stereoselectivity is compromised; then, the active sites which are more versatile than currently attributed to, are free and capable of acting on the foreign sugars. To test this proposition, we entrapped in gold L-asparaginase, which is also a dimeric enzyme (a dimer of tight dimers), and found, again, that this metallic biomaterial widens the activity of that enzyme, to include the D-amino acid counter enantiomer as well. Detailed kinetic analyses for all substrates are provided for the gold bio-composites, including determination of the difference between the activation energies towards two opposite enantiomers.

AQUA-DUCT 1.0: structural and functional analysis of macromolecules from an intramolecular voids perspective

Motivation

Tunnels, pores, channels, pockets and cavities contribute to proteins architecture and performance. However, analysis and characteristics of transportation pathways and internal binding cavities are performed separately. We aimed to provide universal tool for analysis of proteins integral interior with access to detailed information on the ligands transportation phenomena and binding preferences.

Results

AQUA-DUCT version 1.0 is a comprehensive method for macromolecules analysis from the intramolecular voids perspective using small ligands as molecular probes. This version gives insight into several properties of macromolecules and facilitates protein engineering and drug design by the combination of the tracking and local mapping approach to small ligands.

Availability and implementation

http://www.aquaduct.pl.

Supplementary information

Supplementary data are available at Bioinformatics online.

Electrostatic Effect of Functional Surfaces on the Activity of Adsorbed Enzymes: Simulations and Experiments

The efficient immobilization of haloalkane dehalogenase (DhaA) on carriers with retaining of its catalytic activity is essential for its application in environmental remediation. In this work, adsorption orientation and conformation of DhaA on different functional surfaces were investigated by computer simulations; meanwhile, the mechanism of varying the catalytic activity was also probed. The corresponding experiments were then carried out to verify the simulation results. (The simulations of DhaA on SAMs provided parallel insights into DhaA adsorption in carriers. Then, the theory-guided experiments were carried out to screen the best surface functional groups for DhaA immobilization.) The electrostatic interaction was considered as the main impact factor for the regulation of enzyme orientation, conformation, and enzyme bioactivity during DhaA adsorption. The synergy of overall conformation, enzyme substrate tunnel structural parameters, and distance between catalytic active sites and surfaces codetermined the catalytic activity of DhaA. Specifically, it was found that the positively charged surface with suitable surface charge density was helpful for the adsorption of DhaA and retaining its conformation and catalytic activity and was favorable for higher enzymatic catalysis efficiency in haloalkane decomposition and environmental remediation. The neutral, negatively charged surfaces and positively charged surfaces with high surface charge density always caused relatively larger DhaA conformation change and decreased catalytic activity. This study develops a strategy using a combination of simulation and experiment, which can be essential for guiding the rational design of the functionalization of carriers for enzyme adsorption, and provides a practical tool to rationally screen functional groups for the optimization of adsorbed enzyme functions on carriers. More importantly, the strategy is general and can be applied to control behaviors of different enzymes on functional carrier materials.

CaverDock: a molecular docking-based tool to analyse ligand transport through protein tunnels and channels

MOTIVATION

Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins' external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding processes experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding.

RESULTS

CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimized docking algorithm of AutoDock Vina for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. The duration of the simulations takes from minutes to a few hours. Here we describe the implementation of the method and demonstrate CaverDock's usability by: (i) comparison of the results with other available tools, (ii) determination of the robustness with large ensembles of ligands and (iii) the analysis and comparison of the ligand trajectories in engineered tunnels. Thorough testing confirms that CaverDock is applicable for the fast analysis of ligand binding and unbinding in fundamental enzymology and protein engineering.

AVAILABILITY AND IMPLEMENTATION

User guide and binaries for Ubuntu are freely available for non-commercial use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code is available upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural and catalytic effects of surface loop-helix transplantation within haloalkane dehalogenase family

Engineering enzyme catalytic properties is important for basic research as well as for biotechnological applications. We have previously shown that the reshaping of enzyme access tunnels via the deletion of a short surface loop element may yield a haloalkane dehalogenase variant with markedly modified substrate specificity and enantioselectivity. Here, we conversely probed the effects of surface loop-helix transplantation from one enzyme to another within the enzyme family of haloalkane dehalogenases. Precisely, we transplanted a nine-residue long extension of L9 loop and α4 helix from DbjA into the corresponding site of DbeA. Biophysical characterization showed that this fragment transplantation did not affect the overall protein fold or oligomeric state, but lowered protein stability (ΔT m = -5 to 6 °C). Interestingly, the crystal structure of DbeA mutant revealed the unique structural features of enzyme access tunnels, which are known determinants of catalytic properties for this enzyme family. Biochemical data confirmed that insertion increased activity of DbeA with various halogenated substrates and altered its enantioselectivity with several linear β-bromoalkanes. Our findings support a protein engineering strategy employing surface loop-helix transplantation for construction of novel protein catalysts with modified catalytic properties.

Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands’ transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The server is built on top of the abundantly used tunnel detection tool Caver 3.02 and CaverDock 1.0 enabling the study of the ligand transport. The program is easy-to-use as the only required inputs are a protein structure for a tunnel identification and a list of ligands for the transport analysis. The automated guidance procedures assist the users to set up the calculation in a way to obtain biologically relevant results. The identified tunnels, their properties, energy profiles and trajectories for ligands’ passages can be calculated and visualized. The tool is very fast (2–20 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https://loschmidt.chemi.muni.cz/caverweb.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

The impact of tunnel mutations on enzymatic catalysis depends on the tunnel-substrate complementarity and the rate-limiting step

Transport of ligands between bulk solvent and the buried active sites is a critical event in the catalytic cycle of many enzymes. The rational design of transport pathways is far from trivial due to the lack of knowledge about the effect of mutations on ligand transport. The main and an auxiliary tunnel of haloalkane dehalogenase LinB have been previously engineered for improved dehalogenation of 1,2-dibromoethane (DBE). The first chemical step of DBE conversion was enhanced by L177W mutation in the main tunnel, but the rate-limiting product release was slowed down because the mutation blocked the main access tunnel and hindered protein dynamics. Three additional mutations W140A + F143L + I211L opened-up the auxiliary tunnel and enhanced the product release, making this four-point variant the most efficient catalyst with DBE. Here we study the impact of these mutations on the catalysis of bulky aromatic substrates, 4-(bromomethyl)-6,7-dimethoxycoumarin (COU) and 8-chloromethyl-4,4'-difluoro-3,5-dimethyl-4-bora-3a,4a-diaza-s-indacene (BDP). The rate-limiting step of DBE conversion is the product release, whereas the catalysis of COU and BDP is limited by the chemical step. The catalysis of COU is mainly impaired by the mutation L177W, whereas the conversion of BDP is affected primarily by the mutations W140A + F143L + I211L. The combined computational and kinetic analyses explain the differences in activities between the enzyme-substrate pairs. The effect of tunnel mutations on catalysis depends on the rate-limiting step, the complementarity of the tunnels with the substrates and is clearly specific for each enzyme-substrate pair.

Tunnel engineering to accelerate product release for better biomass-degrading abilities in lignocellulolytic enzymes

BACKGROUND

For enzymes with buried active sites, transporting substrates/products ligands between active sites and bulk solvent via access tunnels is a key step in the catalytic cycle of these enzymes. Thus, tunnel engineering is becoming a powerful strategy to refine the catalytic properties of these enzymes. The tunnel-like structures have been described in enzymes catalyzing bulky substrates like glycosyl hydrolases, while it is still uncertain whether these structures involved in ligands exchange. Till so far, no studies have been reported on the application of tunnel engineering strategy for optimizing properties of enzymes catalyzing biopolymers.

RESULTS

In this study, xylanase S7-xyl (PDB: 2UWF) with a deep active cleft was chosen as a study model to evaluate the functionalities of tunnel-like structures on the properties of biopolymer-degrading enzymes. Three tunnel-like structures in S7-xyl were identified and simultaneously reshaped through multi-sites saturated mutagenesis; the most advantageous mutant 254RL1 (V207N/Q238S/W241R) exhibited 340% increase in specific activity compared to S7-xyl. Deconvolution analysis revealed that all three mutations contributed synergistically to the improved activity of 254RL1. Enzymatic characterization showed that larger end products were released in 254RL1, while substrate binding and structural stability were not changed. Dissection of the structural alterations revealed that both the tun_1 and tun_2 in 254RL1 have larger bottleneck radius and shorter length than those of S7-xyl, suggesting that these tunnel-like structures may function as products transportation pathways. Attributed to the improved catalytic efficiency, 254RL1 represents a superior accessory enzyme to enhance the hydrolysis efficiency of cellulase towards different pretreated lignocellulose materials. In addition, tunnel engineering strategy was also successfully applied to improve the catalytic activities of three other xylanases including xylanase NG27-xyl from Bacillus sp. strain NG-27, TSAA1-xyl from Geobacillus sp. TSAA1 and N165-xyl from Bacillus sp. N16-5, with 80%, 20% and 170% increase in specific activity, respectively.

CONCLUSIONS

This study represents a pilot study of engineering and functional verification of tunnel-like structures in enzymes catalyzing biopolymer. The specific activities of four xylanases with buried active sites were successfully improved by tunnel engineering. It is highly likely that tunnel reshaping can be used to engineer better biomass-degrading abilities in other lignocellulolytic enzymes with buried active sites.

Coarse-grain simulations on NMR conformational ensembles highlight functional residues in proteins

Dynamics are a key feature of protein function, and this is especially true of gating residues, which occupy cavity or tunnel lining positions in the protein structure, and will reversibly switch between open and closed conformations in order to control the diffusion of small molecules within a protein's internal matrix. Earlier work on globins and hydrogenases have shown that these gating residues can be detected using a multiscale scheme combining all-atom classic molecular dynamics simulations and coarse-grain calculations of the resulting conformational ensemble mechanical properties. Here, we show that the structural variations observed in the conformational ensembles produced by NMR spectroscopy experiments are sufficient to induce noticeable mechanical changes in a protein, which in turn can be used to identify residues important for function and forming a mechanical nucleus in the protein core. This new approach, which combines experimental data and rapid coarse-grain calculations and no longer needs to resort to time-consuming all-atom simulations, was successfully applied to five different protein families.

Prediction of Ligand Transport along Hydrophobic Enzyme Nanochannels

Buried active sites of enzymes are connected to the bulk solvent through a network of hydrophobic channels. We developed a discretized model that can accurately predict ligand transport along hydrophobic channels up to six orders of magnitude faster than any other existing method. The non-dimensional nature of the model makes it applicable to any hydrophobic channel/ligand combination.

Computational Study of Protein-Ligand Unbinding for Enzyme Engineering

The computational prediction of unbinding rate constants is presently an emerging topic in drug design. However, the importance of predicting kinetic rates is not restricted to pharmaceutical applications. Many biotechnologically relevant enzymes have their efficiency limited by the binding of the substrates or the release of products. While aiming at improving the ability of our model enzyme haloalkane dehalogenase DhaA to degrade the persistent anthropogenic pollutant 1,2,3-trichloropropane (TCP), the DhaA31 mutant was discovered. This variant had a 32-fold improvement of the catalytic rate toward TCP, but the catalysis became rate-limited by the release of the 2,3-dichloropropan-1-ol (DCP) product from its buried active site. Here we present a computational study to estimate the unbinding rates of the products from DhaA and DhaA31. The metadynamics and adaptive sampling methods were used to predict the relative order of kinetic rates in the different systems, while the absolute values depended significantly on the conditions used (method, force field, and water model). Free energy calculations provided the energetic landscape of the unbinding process. A detailed analysis of the structural and energetic bottlenecks allowed the identification of the residues playing a key role during the release of DCP from DhaA31 via the main access tunnel. Some of these hot-spots could also be identified by the fast CaverDock tool for predicting the transport of ligands through tunnels. Targeting those hot-spots by mutagenesis should improve the unbinding rates of the DCP product and the overall catalytic efficiency with TCP.

Filling the Void: Introducing Aromatic Interactions into Solvent Tunnels To Enhance Lipase Stability in Methanol

An enhanced stability of enzymes in organic solvents is desirable under industrial conditions. The potential of lipases as biocatalysts is mainly limited by their denaturation in polar alcohols. In this study, we focused on selected solvent tunnels in lipase from Geobacillus stearothermophilus T6 to improve its stability in methanol during biodiesel synthesis. Using rational mutagenesis, bulky aromatic residues were incorporated to occupy solvent channels and induce aromatic interactions leading to a better inner core packing. The chemical and structural characteristics of each solvent tunnel were systematically analyzed. Selected residues were replaced with Phe, Tyr, or Trp. Overall, 16 mutants were generated and screened in 60% methanol, from which 3 variants showed an enhanced stability up to 81-fold compared with that of the wild type. All stabilizing mutations were found in the longest tunnel detected in the "closed-lid" X-ray structure. The combination of Phe substitutions in an A187F/L360F double mutant resulted in an increase in unfolding temperature (T_m ) of 7°C in methanol and a 3-fold increase in biodiesel synthesis yield from waste chicken oil. A kinetic analysis with p-nitrophenyl laurate revealed that all mutants displayed lower hydrolysis rates (k _cat), though their stability properties mostly determined the transesterification capability. Seven crystal structures of different variants were solved, disclosing new π-π or CH/π intramolecular interactions and emphasizing the significance of aromatic interactions for improved solvent stability. This rational approach could be implemented for the stabilization of other enzymes in organic solvents.IMPORTANCE Enzymatic synthesis in organic solvents holds increasing industrial opportunities in many fields; however, one major obstacle is the limited stability of biocatalysts in such a denaturing environment. Aromatic interactions play a major role in protein folding and stability, and we were inspired by this to redesign enzyme voids. The rational protein engineering of solvent tunnels of lipase from Geobacillus stearothermophilus is presented here, offering a promising approach to introduce new aromatic interactions within the enzyme core. We discovered that longer tunnels leading from the surface to the enzyme active site were more beneficial targets for mutagenesis for improving lipase stability in methanol during biodiesel biosynthesis. A structural analysis of the variants confirmed the generation of new interactions involving aromatic residues. This work provides insights into stability-driven enzyme design by targeting the solvent channel void.

Exploring Solanum tuberosum Epoxide Hydrolase Internal Architecture by Water Molecules Tracking

Several different approaches are used to describe the role of protein compartments and residues in catalysis and to identify key residues suitable for the modification of the activity or selectivity of the desired enzyme. In our research, we applied a combination of molecular dynamics simulations and a water tracking approach to describe the water accessible volume of Solanum tuberosum epoxide hydrolase. Using water as a molecular probe, we were able to identify small cavities linked with the active site: (i) one made up of conserved amino acids and indispensable for the proper positioning of catalytic water and (ii) two others in which modification can potentially contribute to enzyme selectivity and activity. Additionally, we identified regions suitable for de novo tunnel design that could also modify the catalytic properties of the enzyme. The identified hot-spots extend the list of the previously targeted residues used for modification of the regioselectivity of the enzyme. Finally, we have provided an example of a simple and elegant process for the detailed description of the network of cavities and tunnels, which can be used in the planning of enzyme modifications and can be easily adapted to the study of any other protein.

Engineering methionine γ-lyase from Citrobacter freundii for anticancer activity

Methionine deprivation of cancer cells, which are deficient in methionine biosynthesis, has been envisioned as a therapeutic strategy to reduce cancer cell viability. Methionine γ-lyase (MGL), an enzyme that degrades methionine, has been exploited to selectively remove the amino acid from cancer cell environment. In order to increase MGL catalytic activity, we performed sequence and structure conservation analysis of MGLs from various microorganisms. Whereas most of the residues in the active site and at the dimer interface were found to be conserved, residues located in the C-terminal flexible loop, forming a wall of the active site entry channel, were found to be variable. Therefore, we carried out site-saturation mutagenesis at four independent positions of the C-terminal flexible loop, P357, V358, P360 and A366 of MGL from Citrobacter freundii, generating libraries that were screened for activity. Among the active variants, V358Y exhibits a 1.9-fold increase in the catalytic rate and a 3-fold increase in K_M, resulting in a catalytic efficiency similar to wild type MGL. V358Y cytotoxic activity was assessed towards a panel of cancer and nonmalignant cell lines and found to exhibit IC₅₀ lower than the wild type. The comparison of the 3D-structure of V358Y MGL with other MGL available structures indicates that the C-terminal loop is either in an open or closed conformation that does not depend on the amino acid at position 358. Nevertheless, mutations at this position allosterically affects catalysis.

Modulating D-amino acid oxidase (DAAO) substrate specificity through facilitated solvent access

D-amino acid oxidase (DAAO) degrades D-amino acids to produce α-ketoacids, hydrogen peroxide and ammonia. DAAO has often been investigated and engineered for industrial and clinical applications. We combined information from literature with a detailed analysis of the structure to engineer mammalian DAAOs. The structural analysis was complemented with molecular dynamics simulations to characterize solvent accessibility and product release mechanisms. We identified non-obvious residues located on the loops on the border between the active site and the secondary binding pocket essential for pig and human DAAO substrate specificity and activity. We engineered DAAOs by mutating such critical residues and characterised the biochemical activity of the resulting variants. The results highlight the importance of the selected residues in modulating substrate specificity, product egress and enzyme activity, suggesting further steps of DAAO re-engineering towards desired clinical and industrial applications.