Substrate tunnels in enzymes: structure-function relationships and computational methodology

Non-synonymous ERG11 mutations in M. restricta and M. arunalokei: impact on azole susceptibility

Malassezia are commensal lipid-dependent yeasts and opportunistic pathogens that cause superficial mycoses and systemic infection. Azole antifungals target cell wall ergosterol synthesis and are the first line of antifungal treatment. ERG11 gene mutations and overexpression are major mechanisms conferring azole resistance and resulting in antifungal therapy failure. Malassezia restricta is found ubiquitously on healthy and diseased skin, with azole-resistant isolates described. Malassezia arunalokei is a relatively new, closely related common skin species. Ketoconazole and itraconazole were the most effective at inhibiting both species. Isolates of M. restricta and M. arunalokei from healthy skin of Singapore subjects were cultured, evaluated, and generally susceptible to common over-the-counter azoles, including clotrimazole, except for select less-susceptible strains. Some less-susceptible strains have novel or reported non-synonymous mutations in the ERG11 gene, such as R88C. The QK178RQ ERG11 sequence variation was observed to be associated with differences in M. restricta and M. arunalokei as independent species. In the absence of identified ERG11 mutations, strains with elevated MICs were observed to have elevated ERG11 expression and drug efflux pump expression/activity. We conclude that antifungal susceptibility is determined by a combination of intrinsic (e.g., mutations, gene expression, efflux pump activity) and extrinsic (e.g., skin condition, prior antifungal exposure) factors and that the skin microbiome serves as a reference for the emergence of new mutations and strain phenotypes.

IMPORTANCE

Malassezia over colonization is associated with conditions such as dandruff and seborrheic dermatitis, which give rise to unpleasant itching and swelling on the skin. Azole antifungals such as ketoconazole, clotrimazole, and miconazole are the primary treatments of choice available as over-the-counter creams or shampoos. However, the emergence of antifungal resistance leads to a loss of treatment efficacy and persistent fungal infection. To understand the mechanisms underlying antifungal resistance, we profiled the susceptibility profiles of commensal Malassezia isolates from the skin and identified novel ERG11 mutations. Our results indicate that antifungal susceptibility is determined by a combination of factors (mutations, efflux pump activity, gene expression, copy number) and suggest that the healthy skin microbiome serves as a reference for the emergence of new mutations and strain phenotypes.

Caver Web 2.0: analysis of tunnels and ligand transport in dynamic ensembles of proteins

Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation

Drug efficacy often correlates better with dissociation kinetics than binding affinity alone. To study binding kinetics computationally, it is necessary to identify all of the possible ligand dissociation pathways. The site identification by ligand competitive saturation (SILCS) method involves the precomputation of a set of maps (FragMaps), which describe the free energy landscapes of typical chemical functionalities in and around a target protein or RNA. In the current work, we present and implement a method to use SILCS to identify ligand dissociation pathways, termed "SILCS-Pathway." The A* pathfinding algorithm is utilized to enumerate ligand dissociation pathways between the ligand binding site and the surrounding bulk solvent environment defined on evenly spaced points around the protein based on a Fibonacci lattice. The cost function for the A* algorithm is calculated using the SILCS exclusion maps and the SILCS grid free energy scores, thereby identifying paths that account for local protein flexibility and potential favorable interactions with the ligand. By traversing all evenly distributed bulk solvent points around the protein, we located all possible dissociation pathways and clustered them to identify general ligand unbinding pathways. The procedure is verified by using proteins studied previously with enhanced sampling molecular dynamics (MD) techniques and is shown to be capable of capturing important ligand dissociation routes in a highly computationally efficient manner. The identified pathways will act as the foundation for determining ligand dissociation kinetics using SILCS free energy profiles, which will be described in a subsequent article.

Molecular Understanding of Activity Changes of Alcohol Dehydrogenase in Deep Eutectic Solvents

Deep eutectic solvents (DESs) have emerged as promising solvents for biocatalysis. While their impact on enzyme solvation and stabilization has been studied for several enzyme classes, their role in substrate binding is yet to be investigated. Herein, molecular dynamics (MD) simulations of horse-liver alcohol dehydrogenase (HLADH) are performed in choline chloride-ethylene glycol (ChCl-EG) and choline chloride-glycerol (ChCl-Gly) at varying water concentrations. In the DES solutions, the active site was significantly constricted, and its flexibility reduced when compared to the aqueous medium. Importantly, the cavity size follows a similar trend as the catalytic activity of HLADH and as such explains previously observed activity changes. To understand the impact on the binding of the substrate (cyclohexanone), an umbrella sampling (US) setup was established to calculate the free energy changes along the substrate binding tunnel of HLADH. The US combined with replica exchange and NADH in its cofactor pocket provided the best sampling of the entire active site, explaining why the cyclohexanone binding on HLADH is reduced with increasing DES content. As different components in these multicomponent mixtures influence the substrate binding, we additionally applied the US setup to study the ability of the DES components to be present inside the substrate tunnel. The presented approach may become useful to understand enzyme behaviors in DESs and to enable the design of more enzyme-compatible and tunable solvents.

Water Migration through Enzyme Tunnels Is Sensitive to the Choice of Explicit Water Model

The utilization of tunnels and water transport within enzymes is crucial for their catalytic function as water molecules can stabilize bound substrates and help with unbinding processes of products and inhibitors. Since the choice of water models for molecular dynamics simulations was shown to determine the accuracy of various calculated properties of the bulk solvent and solvated proteins, we have investigated if and to what extent water transport through the enzyme tunnels depends on the selection of the water model. Here, we focused on simulating enzymes with various well-defined tunnel geometries. In a systematic investigation using haloalkane dehalogenase as a model system, we focused on the well-established TIP3P, OPC, and TIP4P-Ew water models to explore their impact on the use of tunnels for water molecule transport. The TIP3P water model showed significantly faster migration, resulting in the transport of approximately 2.5 times more water molecules compared to that of the OPC and 1.7 times greater than that of the TIP4P-Ew. Finally, the transport was 1.4-fold more pronounced in TIP4P-Ew than in OPC. The increase in migration of TIP3P water molecules was mainly due to faster transit times through dehalogenase tunnels. We observed similar behavior in two different enzymes with buried active sites and different tunnel network topologies, i.e., alditol oxidase and cytochrome P450, indicating that our findings are likely not restricted to a particular enzyme family. Overall, this study showcases the critical importance of water models in comprehending the use of enzyme tunnels for small molecule transport. Given the significant role of water availability in various stages of the catalytic cycle and the solvation of substrates, products, and drugs, choosing an appropriate water model may be crucial for accurate simulations of complex enzymatic reactions, rational enzyme design, and predicting drug residence times.

Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchange

Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases.1 In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol2to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.

Reshaping the substrate-binding pocket of acyl-ACP reductase to enhance the production of sustainable aviation fuel in Escherichia coli

To reduce carbon emissions and address environmental concerns, the aviation industry is exploring the use of sustainable aviation fuel (SAF) as an alternative to traditional fossil fuels. In this context, bio-alkane is considered a potentially high-value solution. The present study focuses on the enzymes acyl-acyl carrier protein [ACP] reductase (AAR) and aldehyde-deformylating oxygenase (ADO), which are crucial enzymes for alka(e)ne biosynthesis. By using protein engineering techniques, including semi-rational design and site-directed mutagenesis, we aimed to enhance the substrate specificity of AAR and improve alkane production efficiency. The co-expression of a modified AAR (Y26G/Q40M mutant) with wild-type ADO in Escherichia coli significantly increased alka(e)ne production from 28.92 mg/L to 167.30 mg/L, thus notably demonstrating a 36-fold increase in alkane yield. This research highlights the potential of protein engineering in optimizing SAF production, thereby contributing to the development of more sustainable and efficient SAF production methods and promoting greener air travel.

Advanced strategies for enzyme-electrode interfacing in bioelectrocatalytic systems

Advances in protein engineering-enabled enzyme immobilization technologies have significantly improved enzyme-electrode wiring in enzymatic electrochemical systems, which harness natural biological machinery to either generate electricity or synthesize biochemicals. In this review, we provide guidelines for designing enzyme-electrodes, focusing on how performance variables change depending on electron transfer (ET) mechanisms. Recent advancements in enzyme immobilization technologies are summarized, highlighting their contributions to extending enzyme-electrode sustainability (up to months), enhancing biosensor sensitivity, improving biofuel cell performance, and setting a new benchmark for turnover frequency in bioelectrocatalysis. We also highlight state-of-the-art protein-engineering approaches that enhance enzyme-electrode interfacing through three key principles: protein-protein, protein-ligand, and protein-inorganic interactions. Finally, we discuss prospective avenues in strategic protein design for real-world applications.

Safety Landscape of Therapeutic Nanozymes and Future Research Directions

Oxidative stress and inflammation are at the root of a multitude of diseases. Treatment of these conditions is often necessary but current standard therapies to fight excessive reactive oxygen species (ROS) and inflammation are often ineffective or complicated by substantial safety concerns. Nanozymes are emerging nanomaterials with intrinsic enzyme-like properties that hold great promise for effective cancer treatment, bacterial elimination, and anti-inflammatory/anti-oxidant therapy. While there is rapid progress in tailoring their catalytic activities as evidenced by the recent integration of single-atom catalysts (SACs) to create next-generation nanozymes with superior activity, selectivity, and stability, a better understanding and tuning of their safety profile is imperative for successful clinical translation. This review outlines the current applied safety assessment approaches and provides a comprehensive summary of the safety knowledge of therapeutic nanozymes. Overall, nanozymes so far show good in vitro and in vivo biocompatibility despite considerable differences in their composition and enzymatic activities. However, current safety investigations mostly cover a limited set of basic toxicological endpoints, which do not allow for a thorough and deep assessment. Ultimately, remaining research gaps that should be carefully addressed in future studies are highlighted, to optimize the safety profile of therapeutic nanozymes early in their pre-clinical development.

Impact of water models on the structure and dynamics of enzyme tunnels

Protein hydration plays a vital role in many biological functions, and molecular dynamics simulations are frequently used to study it. However, the accuracy of these simulations is often sensitive to the water model used, a phenomenon particularly evident in intrinsically disordered proteins. Here, we investigated the extent to which the choice of water model alters the behavior of complex networks of tunnels within proteins. Tunnels are essential because they allow the exchange of substrates and products between buried enzyme active sites and the bulk solvent, directly affecting enzyme efficiency and selectivity, making the study of tunnels crucial for a holistic understanding of enzyme function at the molecular level. By performing simulations of haloalkane dehalogenase LinB and its two variants with engineered tunnels using TIP3P and OPC models, we investigated their effects on the overall tunnel topology. We also analyzed the properties of the primary tunnels, including their conformation, bottleneck dimensions, sampling efficiency, and the duration of tunnel openings. Our data demonstrate that all three proteins exhibited similar conformational behavior in both models but differed in the geometrical characteristics of their auxiliary tunnels, consistent with experimental observations. Interestingly, the results indicate that the stability of the open tunnels might be sensitive to the water model used. Because TIP3P can provide comparable data on the overall tunnel network, it is a valid choice when computational resources are limited or compatibility issues impede the use of OPC. However, OPC seems preferable for calculations requiring an accurate description of transport kinetics.

Enhancing β-Galactosidase Performance for Galactooligosaccharides Preparation via Strategic Glucose Re-Tunneling

This study focuses on the characterization and re-engineering of glucose transport in β-galactosidase (BglD) to enhance its catalytic efficiency. Computational prediction methods were employed to identify key residues constituting access tunnels for lactose and glucose, revealing distinct pockets for both substrates. In silico simulated saturation mutagenesis of residues T215 and T473 led to the identification of eight mutant variants exhibiting potential enhancements in glucose transport. Site-directed mutagenesis at T215 and T473 resulted in mutants with consistently enhanced specific activities, turnover rates, and catalytic efficiencies. These mutants also demonstrated improved galactooligosaccharide (GOS) synthesis, yielding an 8.1-10.6% enhancement over wild-type BglD yield. Structural analysis revealed that the mutants exhibited transformed configurations and localizations of glucose conduits, facilitating expedited glucose release. This study's findings suggest that the re-engineered mutants offer promising avenues for enhancing BglD's catalytic efficiency and glucose translocation, thereby improving GOS synthesis. By-product (glucose) re-tunneling is a viable approach for enzyme tunnel engineering and holds significant promise for the molecular evolution of enzymes.

Efficiency enhancement in Aspergillus niger α-L-rhamnosidase reverse hydrolysis by using a tunnel site rational design strategy

There has been ongoing interest in improving the efficiency of glycoside hydrolase for synthesizing glycoside compounds through protein engineering, given the potential applications of glycoside compounds. In this study, a strategy of modifying the substrate access tunnel was proposed to enhance the efficiency of reverse hydrolysis catalyzed by Aspergillus niger α-L-rhamnosidase. Analysis of the tunnel dynamics identified Tyr299 as a key modifiable residue in the substrate access tunnel. The location of Tyr299 was near the enzyme surface and at the outermost end of the substrate access tunnel, suggested its role in substrate recognition and throughput. Based on the properties of side chains, six mutants were designed and expressed by Pichia pastoris. Compared to WT, the reverse hydrolysis efficiencies of mutants Y299P and Y299W were increased by 21.3 % and 11.1 %, respectively. The calculation results of binding free energy showed that the binding free energy was inversely proportional to the reverse hydrolysis efficiency. Further, when binding free energy levels were comparable, the mutants with shorter side chains displayed a higher reverse hydrolysis efficiency. These results proved that substrate access tunnel modification was an effective method to improve the reverse hydrolysis efficacy of α-L-rhamnosidase and also provided new insights for modifying other glycoside hydrolases.

Exploring the selectivity of cytochrome P450 for enhanced novel anticancer agent synthesis

Cytochrome P450 monooxygenases are an extensive and unique class of enzymes, which can regio- and stereo-selectively functionalise hydrocarbons by way of oxidation reactions. These enzymes are naturally occurring but have also been extensively applied in a synthesis context, where they are used as efficient biocatalysts. Recently, a biosynthetic pathway where a cytochrome P450 monooxygenase catalyses a critical step of the pathway was uncovered, leading to the production of a number of products that display high antitumour potency. In this work, we use computational techniques to gain insight into the factors that determine the relative yields of the different products. We use conformational search algorithms to understand the substrate stereochemistry. On a machine-learned 3D protein structure, we use molecular docking to obtain a library of favourable poses for substrate-protein interaction. With molecular dynamics, we investigate the most favourable poses for reactivity on a molecular level, allowing us to investigate which protein-substrate interactions favour a given product and thus gain insight into the product selectivity.

Advances in Computational Intelligence-Based Methods of Structure and Function Prediction of Proteins

Proteins serve as the building blocks of life and play essential roles in almost every cellular process [...].

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.

Under the Influence of Water: Molecular Recognition of Organic Hydrophilic Molecules in Water with a Prismarene Host Driven by Hydration Effects

A water-soluble prism[5]arene host can form endo-cavity complexes with hydrophilic organic substances in water by displacing frustrated water molecules from its deep cavity. Water molecules structured at both rims of the prismarene host can mediate hydrogen bonding interactions with the guest. Water-mediated hydrogen bonding interactions were invoked here to elucidate the elevated binding affinities and selectivity of the prismarene host toward hydrophilic organic guests. We show that water at the interface of a host-guest complex can act as an extension of the host structure, facilitating the accommodation of neutral guests within the binding site. This study highlights the crucial role of water in facilitating supramolecular interactions between a deep-cavity prismarene host and organic hydrophilic guests in aqueous medium.

Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites

Because most proteins have buried active sites, protein tunnels or channels play a crucial role in the transport of small molecules into buried cavities for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, a recent example being energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasted the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a nontrivial system of a haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The most knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting drug development efforts and the rational design of enzymes with buried active sites.

Targeting host-specific metabolic pathways-opportunities and challenges for anti-infective therapy

Microorganisms can takeover critical metabolic pathways in host cells to fuel their replication. This interaction provides an opportunity to target host metabolic pathways, in addition to the pathogen-specific ones, in the development of antimicrobials. Host-directed therapy (HDT) is an emerging strategy of anti-infective therapy, which targets host cell metabolism utilized by facultative and obligate intracellular pathogens for entry, replication, egress or persistence of infected host cells. This review provides an overview of the host lipid metabolism and links it to the challenges in the development of HDTs for viral and bacterial infections, where pathogens are using important for the host lipid enzymes, or producing their own analogous of lecithin-cholesterol acyltransferase (LCAT) and lipoprotein lipase (LPL) thus interfering with the human host's lipid metabolism.

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.

Macromolecular Interactions of Lipoprotein Lipase (LPL)

Lipoprotein lipase (LPL) is a critical enzyme in humans that provides fuel to peripheral tissues. LPL hydrolyzes triglycerides from the cores of lipoproteins that are circulating in plasma and interacts with receptors to mediate lipoprotein uptake, thus directing lipid distribution via catalytic and non-catalytic functions. Functional losses in LPL or any of its myriad of regulators alter lipid homeostasis and potentially affect the risk of developing cardiovascular disease-either increasing or decreasing the risk depending on the mutated protein. The extensive LPL regulatory network tunes LPL activity to allocate fatty acids according to the energetic needs of the organism and thus is nutritionally responsive and tissue dependent. Multiple pharmaceuticals in development manipulate or mimic these regulators, demonstrating their translational importance. Another facet of LPL biology is that the oligomeric state of the enzyme is also central to its regulation. Recent structural studies have solidified the idea that LPL is regulated not only by interactions with other binding partners but also by self-associations. Here, we review the complexities of the protein-protein and protein-lipid interactions that govern LPL structure and function.

Synthesis, Characterization, and Antimicrobial Evaluation of Schiff Base-mixed Ligand Complexes with Divalent Metal Ions Derived from Amoxicillin and Vanillin/Nicotinamide

INTRODUCTION

This study focuses on the development of novel antimicrobial agents. A Schiff base ligand, 6-(2-(4-hydroxy-3-methoxybenzylideneamino)-2-(4-hydroxyphenyl)acetamido)-3,3-dimethyl-7-oxo- 4-thia-1-azabicyclo [3.2.0] heptane-2-carboxylic acid, synthesized through the condensation of amoxicillin and vanillin in methanol, served as the foundation. Polydentate mixed ligand complexes were then formed by reacting the Schiff base with metal ions (Fe(II), Co(II), Ni(II), Cu(II), and Zn(II)) and nicotinamide in specific ratios.

METHODS

Characterization involved various techniques, such as 1H-NMR, FT-IR, UV-Vis, and elemental analysis for the ligand, and Atomic Absorption, FT-IR, UV-Vis, magnetic susceptibility, and conductance measurements for the Schiff base-metal ion complexes.

RESULTS

Quantum chemical features of both ligands and metal complexes were computed, refining their electronic and molecular structures theoretically. Antimicrobial activity against Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Salmonella typhi, Acinetobacter baumannii, and Pseudomonas aeruginosa was assessed for the starting materials, ligands, and synthesized complexes, revealing significant effects on certain species. In-silico binding modes with Escherichia coli (PDB ID: 5iq9) were determined through molecular docking.

CONCLUSION

This study underscores the potential applications of the Schiff base ligands and their metal complexes in developing new antimicrobial agents.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

Structural insights into hydrolytic defluorination of difluoroacetate by microbial fluoroacetate dehalogenases

Fluorine forms the strongest single bond to carbon with the highest bond dissociation energy among natural products. However, fluoroacetate dehalogenases (FADs) have been shown to hydrolyze this bond in fluoroacetate under mild reaction conditions. Furthermore, two recent studies demonstrated that the FAD RPA1163 from Rhodopseudomonas palustris can also accept bulkier substrates. In this study, we explored the substrate promiscuity of microbial FADs and their ability to defluorinate polyfluorinated organic acids. Enzymatic screening of eight purified dehalogenases with reported fluoroacetate defluorination activity revealed significant hydrolytic activity against difluoroacetate in three proteins. Product analysis using liquid chromatography-mass spectrometry identified glyoxylic acid as the final product of enzymatic DFA defluorination. The crystal structures of DAR3835 from Dechloromonas aromatica and NOS0089 from Nostoc sp. were determined in the apo-state along with the DAR3835 H274N glycolyl intermediate. Structure-based site-directed mutagenesis of DAR3835 demonstrated a key role for the catalytic triad and other active site residues in the defluorination of both fluoroacetate and difluoroacetate. Computational analysis of the dimer structures of DAR3835, NOS0089, and RPA1163 indicated the presence of one substrate access tunnel in each protomer. Moreover, protein-ligand docking simulations suggested similar catalytic mechanisms for the defluorination of both fluoroacetate and difluoroacetate, with difluoroacetate being defluorinated via two consecutive defluorination reactions producing glyoxylate as the final product. Thus, our findings provide molecular insights into substrate promiscuity and catalytic mechanism of FADs, which are promising biocatalysts for applications in synthetic chemistry and bioremediation of fluorochemicals.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

Heterogeneity in M. tuberculosis β-lactamase inhibition by Sulbactam

For decades, researchers have elucidated essential enzymatic functions on the atomic length scale by tracing atomic positions in real-time. Our work builds on possibilities unleashed by mix-and-inject serial crystallography (MISC) at X-ray free electron laser facilities. In this approach, enzymatic reactions are triggered by mixing substrate or ligand solutions with enzyme microcrystals. Here, we report in atomic detail (between 2.2 and 2.7 Å resolution) by room-temperature, time-resolved crystallography with millisecond time-resolution (with timepoints between 3 ms and 700 ms) how the Mycobacterium tuberculosis enzyme BlaC is inhibited by sulbactam (SUB). Our results reveal ligand binding heterogeneity, ligand gating, cooperativity, induced fit, and conformational selection all from the same set of MISC data, detailing how SUB approaches the catalytic clefts and binds to the enzyme noncovalently before reacting to a trans-enamine. This was made possible in part by the application of singular value decomposition to the MISC data using a program that remains functional even if unit cell parameters change up to 3 Å during the reaction.

Leveraging a Structural Blueprint to Rationally Engineer the Rieske Oxygenase TsaM

Rieske nonheme iron oxygenases use two metallocenters, a Rieske-type [2Fe-2S] cluster and a mononuclear iron center, to catalyze oxidation reactions on a broad range of substrates. These enzymes are widely used by microorganisms to degrade environmental pollutants and to build complexity in a myriad of biosynthetic pathways that are industrially interesting. However, despite the value of this chemistry, there is a dearth of understanding regarding the structure-function relationships in this enzyme class, which limits our ability to rationally redesign, optimize, and ultimately exploit the chemistry of these enzymes. Therefore, in this work, by leveraging a combination of available structural information and state-of-the-art protein modeling tools, we show that three "hotspot" regions can be targeted to alter the site selectivity, substrate preference, and substrate scope of the Rieske oxygenase p-toluenesulfonate methyl monooxygenase (TsaM). Through mutation of six to 10 residues distributed between three protein regions, TsaM was engineered to behave as either vanillate monooxygenase (VanA) or dicamba monooxygenase (DdmC). This engineering feat means that TsaM was rationally engineered to catalyze an oxidation reaction at the meta and ortho positions of an aromatic substrate, rather than its favored native para position, and that TsaM was redesigned to perform chemistry on dicamba, a substrate that is not natively accepted by the enzyme. This work thus contributes to unlocking our understanding of structure-function relationships in the Rieske oxygenase enzyme class and expands foundational principles for future engineering of these metalloenzymes.

Structure of dimeric lipoprotein lipase reveals a pore adjacent to the active site

Lipoprotein lipase (LPL) hydrolyzes triglycerides from circulating lipoproteins, releasing free fatty acids. Active LPL is needed to prevent hypertriglyceridemia, which is a risk factor for cardiovascular disease (CVD). Using cryogenic electron microscopy (cryoEM), we determined the structure of an active LPL dimer at 3.9 Å resolution. This structure reveals an open hydrophobic pore adjacent to the active site residues. Using modeling, we demonstrate that this pore can accommodate an acyl chain from a triglyceride. Known LPL mutations that lead to hypertriglyceridemia localize to the end of the pore and cause defective substrate hydrolysis. The pore may provide additional substrate specificity and/or allow unidirectional acyl chain release from LPL. This structure also revises previous models on how LPL dimerizes, revealing a C-terminal to C-terminal interface. We hypothesize that this active C-terminal to C-terminal conformation is adopted by LPL when associated with lipoproteins in capillaries.

Rice OsHsp16.9A interacts with OsHsp101 to confer thermotolerance

Class I small heat shock proteins (CI sHSPs), OsHsp16.9A and OsHsp18.0, share 74% identity in amino acid sequences and accumulate in response to heat shock treatments. Individual rice transformants overexpressing OsHsp16.9A and OsHsp18.0 exhibit distinct thermoprotection/thermotolerance modes. Under high temperature stress, OsHsp16.9A-overexpressing lines showed higher seed germination rate, seedling survival, and pollen germination than wild-type controls, while OsHsp18.0 overexpression provided higher thermoprotection/thermotolerance for seedling survival. To elucidate the functional roles of OsHsp16.9A, mass spectrometry was used to identify OsHsp16.9A-interacting proteins. OsHsp101 was consistently identified in the OsHsp16.9A protein complex in several mass spectrometry analyses of seed proteins from OsHsp16.9A-overexpressing lines. Both OsHsp16.9A and OsHsp101 proteins accumulated during similar developmental stages of rice seeds and formed a heat-stable complex under high temperature treatments in in vitro assays. Co-localization of OsHsp16.9A and OsHsp101 was observed via ratiometric bimolecular fluorescence complementation analyses. Amino acid mutation studies revealed that OsHsp16.9A glutamate residue 74 and amino acid residues 23-36 were essential for OsHsp16.9A-OsHsp101 interaction. Moreover, overexpressing OsHsp16.9A in OsHsp101 knockdown mutants did not increase the seed germination rate under heat stress, which further confirmed the functional roles of OsHsp16.9A-OsHsp101 interaction in conferring thermotolerance to rice plants.

Structure of Dimeric Lipoprotein Lipase Reveals a Pore for Hydrolysis of Acyl Chains

Lipoprotein lipase (LPL) hydrolyzes triglycerides from circulating lipoproteins, releasing free fatty acids. Active LPL is needed to prevent hypertriglyceridemia, which is a risk factor for cardiovascular disease (CVD). Using cryogenic electron microscopy (cryoEM), we determined the structure of an active LPL dimer at 3.9 Ã… resolution. This is the first structure of a mammalian lipase with an open, hydrophobic pore adjacent to the active site. We demonstrate that the pore can accommodate an acyl chain from a triglyceride. Previously, it was thought that an open lipase conformation was defined by a displaced lid peptide, exposing the hydrophobic pocket surrounding the active site. With these previous models after the lid opened, the substrate would enter the active site, be hydrolyzed and then released in a bidirectional manner. It was assumed that the hydrophobic pocket provided the only ligand selectivity. Based on our structure, we propose a new model for lipid hydrolysis, in which the free fatty acid product travels unidirectionally through the active site pore, entering and exiting opposite sides of the protein. By this new model, the hydrophobic pore provides additional substrate specificity and provides insight into how LPL mutations in the active site pore may negatively impact LPL activity, leading to chylomicronemia. Structural similarity of LPL to other human lipases suggests that this unidirectional mechanism could be conserved but has not been observed due to the difficulty of studying lipase structure in the presence of an activating substrate. We hypothesize that the air/water interface formed during creation of samples for cryoEM triggered interfacial activation, allowing us to capture, for the first time, a fully open state of a mammalian lipase. Our new structure also revises previous models on how LPL dimerizes, revealing an unexpected C-terminal to C-terminal interface. The elucidation of a dimeric LPL structure highlights the oligomeric diversity of LPL, as now LPL homodimer, heterodimer, and helical filament structures have been elucidated. This diversity of oligomerization may provide a form of regulation as LPL travels from secretory vesicles in the cell, to the capillary, and eventually to the liver for lipoprotein remnant uptake. We hypothesize that LPL dimerizes in this active C-terminal to C-terminal conformation when associated with mobile lipoproteins in the capillary.

Transient binding sites at the surface of haloalkane dehalogenase LinB as locations for fine-tuning enzymatic activity

The haloalkane dehalogenase LinB is a well-known enzyme that contains buried active site and is used for many modelling studies. Using classical molecular dynamics simulations of enzymes and substrates, we searched for transient binding sites on the surface of the LinB protein by calculating maps of enzyme-ligand interactions that were then transformed into sparse matrices. All residues considered as functionally important for enzyme performance (e.g., tunnel entrances) were excluded from the analysis to concentrate rather on non-obvious surface residues. From a set of 130 surface residues, twenty-six were proposed as a promising improvement of enzyme performance. Eventually, based on rational selection and filtering out the potentially unstable mutants, a small library of ten mutants was proposed to validate the possibility of fine-tuning the LinB protein. Nearly half of the predicted mutant structures showed improved activity towards the selected substrates, which demonstrates that the proposed approach could be applied to identify non-obvious yet beneficial mutations for enzyme performance especially when obvious locations have already been explored.

Engineering Rieske oxygenase activity one piece at a time

Enzyme engineering plays a central role in the development of biocatalysts for biotechnology, chemical and pharmaceutical manufacturing, and environmental remediation. Rational design of proteins has historically relied on targeting active site residues to confer a protein with desirable catalytic properties. However, additional "hotspots" are also known to exist beyond the active site. Structural elements such as subunit-subunit interactions, entrance tunnels, and flexible loops influence enzyme catalysis and serve as potential "hotspots" for engineering. For the Rieske oxygenases, which use a Rieske cluster and mononuclear iron center to catalyze a challenging set of reactions, these outside of the active site regions are increasingly being shown to drive catalytic outcomes. Therefore, here, we highlight recent work on structurally characterized Rieske oxygenases that implicates architectural pieces inside and outside of the active site as key dictators of catalysis, and we suggest that these features may warrant attention in efforts aimed at Rieske oxygenase engineering.

Heterogeneity in the M. tuberculosis β-Lactamase Inhibition by Sulbactam

For decades, researchers have been determined to elucidate essential enzymatic functions on the atomic lengths scale by tracing atomic positions in real time. Our work builds on new possibilities unleashed by mix-and-inject serial crystallography (MISC) 1-5 at X-ray free electron laser facilities. In this approach, enzymatic reactions are triggered by mixing substrate or ligand solutions with enzyme microcrystals 6 . Here, we report in atomic detail and with millisecond time-resolution how the Mycobacterium tuberculosis enzyme BlaC is inhibited by sulbactam (SUB). Our results reveal ligand binding heterogeneity, ligand gating 7-9 , cooperativity, induced fit 10,11 and conformational selection 11-13 all from the same set of MISC data, detailing how SUB approaches the catalytic clefts and binds to the enzyme non-covalently before reacting to a trans- enamine. This was made possible in part by the application of the singular value decomposition 14 to the MISC data using a newly developed program that remains functional even if unit cell parameters change during the reaction.

Geometry-Based versus Small-Molecule Tracking Method for Tunnel Identification: Benefits and Pitfalls

Different methods for tunnel identification, geometry-based and small-molecule tracking approaches, were compared to provide their benefits and pitfalls. Results obtained for both crystal structures and molecular dynamics (MD) simulations were analyzed to investigate if a more computationally demanding method would be beneficial. Careful examination of the results is essential for the low-diameter tunnel description, and assessment of the tunnel functionality based only on their geometrical parameters is challenging. We showed that the small-molecule tracking approach can provide a detailed description of the system; however, it can also be the most computationally demanding.

Understanding Life at High Temperatures: Relationships of Molecular Channels in Enzymes of Methanogenic Archaea and Their Growth Temperatures

Analyses of protein structures have shown the existence of molecular channels in enzymes from Prokaryotes. Those molecular channels suggest a critical role of spatial voids in proteins, above all, in those enzymes functioning under high temperature. It is expected that these spaces within the protein structure are required to access the active site and to maximize availability and thermal stability of their substrates and cofactors. Interestingly, numerous substrates and cofactors have been reported to be highly temperature-sensitive biomolecules. Methanogens represent a singular phylogenetic group of Archaea that performs anaerobic respiration producing methane during growth. Methanogens inhabit a variety of environments including the full range of temperatures for the known living forms. Herein, we carry out a dimensional analysis of molecular tunnels in key enzymes of the methanogenic pathway from methanogenic Archaea growing optimally over a broad temperature range. We aim to determine whether the dimensions of the molecular tunnels are critical for those enzymes from thermophiles. Results showed that at increasing growth temperature the dimensions of molecular tunnels in the enzymes methyl-coenzyme M reductase and heterodisulfide reductase become increasingly restrictive and present strict limits at the highest growth temperatures, i.e., for hyperthermophilic methanogens. However, growth at lower temperature allows a wide dimensional range for the molecular spaces in these enzymes. This is in agreement with previous suggestions on a potential major role of molecular tunnels to maintain biomolecule stability and activity of some enzymes in microorganisms growing at high temperatures. These results contribute to better understand archaeal growth at high temperatures. Furthermore, an optimization of the dimensions of molecular tunnels would represent an important adaptation required to maintain the activity of key enzymes of the methanogenic pathway for those methanogens growing optimally at high temperatures.

Comparative computational study to augment UbiA prenyltransferases inherent in purple photosynthetic bacteria cultured from mangrove microbial mats in Qatar for coenzyme Q10 biosynthesis

Coenzyme Q10 (CoQ10) is a powerful antioxidant with a myriad of applications in healthcare and cosmetic industries. The most effective route of CoQ10 production is microbial biosynthesis. In this study, four CoQ10 biosynthesizing purple photosynthetic bacteria: Rhodobacter blasticus, Rhodovulum adriaticum, Afifella pfennigii and Rhodovulum marinum, were identified using 16S rRNA sequencing of enriched microbial mat samples obtained from Purple Island mangroves (Qatar). The membrane bound enzyme 4-hydroxybenzoate octaprenyltransferase (UbiA) is pivotal for bacterial biosynthesis of CoQ10. The identified bacteria could be inducted as efficient industrial bio-synthesizers of CoQ10 by engineering their UbiA enzymes. Therefore, the mutation sites and substitution residues for potential functional enhancement were determined by comparative computational study. Two mutation sites were identified within the two conserved Asp-rich motifs, and the effect of proposed mutations in substrate binding affinity of the UbiA enzymes was assessed using multiple ligand simultaneous docking (MLSD) studies, as a groundwork for experimental studies.

DOPAnization of tyrosine in α-synuclein by tyrosine hydroxylase leads to the formation of oligomers

Parkinson's disease is a progressive neurodegenerative disorder characterized by the preferential loss of tyrosine hydroxylase (TH)-expressing dopaminergic neurons in the substantia nigra. Although the abnormal accumulation and aggregation of α-synuclein have been implicated in the pathogenesis of Parkinson's disease, the underlying mechanisms remain largely elusive. Here, we found that TH converts Tyr136 in α-synuclein into dihydroxyphenylalanine (DOPA; Y136DOPA) through mass spectrometric analysis. Y136DOPA modification was clearly detected by a specific antibody in the dopaminergic neurons of α-synuclein-overexpressing mice as well as human α-synucleinopathies. Furthermore, dopanized α-synuclein tended to form oligomers rather than large fibril aggregates and significantly enhanced neurotoxicity. Our findings suggest that the dopanization of α-synuclein by TH may contribute to oligomer and/or seed formation causing neurodegeneration with the potential to shed light on the pathogenesis of Parkinson's disease.

DOPAnization of tyrosine in α-synuclein by tyrosine hydroxylase leads to the formation of oligomers

Parkinson's disease is a progressive neurodegenerative disorder characterized by the preferential loss of tyrosine hydroxylase (TH)-expressing dopaminergic neurons in the substantia nigra. Although the abnormal accumulation and aggregation of α-synuclein have been implicated in the pathogenesis of Parkinson's disease, the underlying mechanisms remain largely elusive. Here, we found that TH converts Tyr136 in α-synuclein into dihydroxyphenylalanine (DOPA; Y136DOPA) through mass spectrometric analysis. Y136DOPA modification was clearly detected by a specific antibody in the dopaminergic neurons of α-synuclein-overexpressing mice as well as human α-synucleinopathies. Furthermore, dopanized α-synuclein tended to form oligomers rather than large fibril aggregates and significantly enhanced neurotoxicity. Our findings suggest that the dopanization of α-synuclein by TH may contribute to oligomer and/or seed formation causing neurodegeneration with the potential to shed light on the pathogenesis of Parkinson's disease.

Evolution and molecular basis of substrate specificity in a 3-ketoacyl-CoA synthase gene cluster from Populus trichocarpa

Very-long-chain fatty acids (VLCFAs) are precursors to sphingolipids, glycerophospholipids, and plant cuticular waxes. In plants, members of a large 3-ketoacyl-CoA synthase (KCS) gene family catalyze the substrate-specific elongation of VLCFAs. Although it is well understood that KCSs have evolved to use diverse substrates, the underlying molecular determinants of their specificity are still unclear. In this study, we exploited the sequence similarity of a KCS gene cluster from Populus trichocarpa to examine the evolution and molecular determinants of KCS substrate specificity. Functional characterization of five members (PtKCS1, 2, 4, 8, 9) in yeast showed divergent product profiles based on VLCFA length, saturation, and position of the double bond. In addition, homology models, rationally designed chimeras, and site-directed mutants were used to identify two key regions (helix-4 and position 277) as being major determinants of substrate specificity. These results were corroborated with chimeras involving a more distantly related KCS, PtCER6 (the poplar ortholog of the Arabidopsis CER6), and used to show that helix-4 is necessary for the modulatory effect of PtCER2-like 5 on KCS substrate specificity. The role of position 277 in limiting product length was further tested by substitution with smaller amino acids, which shifted specificity towards longer products. Finally, treatment with KCS inhibitors (K3 herbicides) showed varying inhibitor sensitivities between the duplicated paralogs despite their sequence similarity. Together, this work sheds light on the molecular mechanisms driving substrate diversification in the KCS family and lays the groundwork for tailoring the production of specific VLCFAs.

In-Silico Characterization of Estrogen Reactivating β-Glucuronidase Enzyme in GIT Associated Microbiota of Normal Human and Breast Cancer Patients

Estrogen circulating in blood has been proved to be a strong biomarker for breast cancer. A β-glucuronidase enzyme (GUS) from human gastrointestinal tract (GIT) microbiota including probiotics has significant involvement in enhancing the estrogen concentration in blood through deconjugation of glucuronidated estrogens. The present project has been designed to explore GIT microbiome-encoded GUS enzymes (GUSOME) repertoire in normal human and breast cancer patients. For this purpose, a total of nineteen GUS enzymes from human GIT microbes, i.e., seven from healthy and twelve from breast cancer patients have been focused on. Protein sequences of enzymes retrieved from UniProt database were subjected to ProtParam, CELLO2GO, SOPMA (secondary structure prediction method), PDBsum (Protein Database summaries), PHYRE2 (Protein Homology/AnalogY Recognition Engine), SAVES v6.0 (Structure Validation Server), MEME version 5.4.1 (Multiple Em for Motif Elicitation), Caver Web server v 1.1, Interproscan and Predicted Antigenic Peptides tool. Analysis revealed the number of amino acids, isoelectric point, extinction coefficient, instability index and aliphatic index of GUS enzymes in the range of 586−795, 4.91−8.92, 89,980−155,075, 25.88−40.93 and 71.01−88.10, respectively. Sub-cellular localization of enzyme was restricted to cytoplasm and inner-membrane in case of breast cancer patients’ bacteria as compared to periplasmic space, outer membrane and extracellular space in normal GIT bacteria. The 2-D structure analysis showed α helix, extended strand, β turn and random coil in the range of 27.42−22.66%, 22.04−25.91%, 5.39−8.30% and 41.75−47.70%, respectively. The druggability score was found to be 0.05−0.45 and 0.06−0.80 in normal and breast cancer patients GIT, respectively. The radius, length and curvature of catalytic sites were observed to be 1.1−2.8 Å, 1.4−15.9 Å and 0.65−1.4, respectively. Ten conserved protein motifs with p < 0.05 and width 25−50 were found. Antigenic propensity-associated sequences were 20−29. Present study findings hint about the use of the bacterial GUS enzymes against breast cancer tumors after modifications via site-directed mutagenesis of catalytic sites involved in the activation of estrogens and through destabilization of these enzymes.

Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)

Modifications of the activity of Cytochrome 450 (CYP) enzymes by compounds in food might impair medical treatments. These CYP-mediated food–drug interactions (FDI) play a major role in drug clearance in the intestine and liver. Inter-individual variation in both CYP expression and structure is an important determinant of FDI. Traditional targeted approaches have highlighted a limited number of dietary inhibitors and single-nucleotide variations (SNVs), each determining personal CYP activity and inhibition. These approaches are costly in time, money and labor. Here, we review computational tools and databases that are already available and are relevant to predicting CYP-mediated FDIs. Computer-aided approaches such as protein–ligand interaction modeling and the virtual screening of big data narrow down hundreds of thousands of items in databanks to a few putative targets, to which the research resources could be further directed. Structure-based methods are used to explore the structural nature of the interaction between compounds and CYP enzymes. However, while collections of chemical, biochemical and genetic data are available today and call for the implementation of big-data approaches, ligand-based machine-learning approaches for virtual screening are still scarcely used for FDI studies. This review of CYP-mediated FDIs promises to attract scientists and the general public.

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.

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.

Impact of protein conformational diversity on AlphaFold predictions

MOTIVATION

After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm.

RESULTS

Using a curated collection of apo-holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions.

AVAILABILITY AND IMPLEMENTATION

Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enhancement of Methane Catalysis Rates in Methylosinus trichosporium OB3b

Particulate methane monooxygenase (pMMO), a membrane-bound enzyme having three subunits (α, β, and γ) and copper-containing centers, is found in most of the methanotrophs that selectively catalyze the oxidation of methane into methanol. Active sites in the pMMO of Methylosinus trichosporium OB3b were determined by docking the modeled structure with ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene. The docking energy between the modeled pMMO structure and ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene was -5.2, -5.7, -4.2, and -3.8 kcal/mol, respectively, suggesting the existence of more than one active site within the monomeric subunits due to the presence of multiple binding sites within the pMMO monomer. The evaluation of tunnels and cavities of the active sites and the docking results showed that each active site is specific to the radius of the substrate. To increase the catalysis rates of methane in the pMMO of M. trichosporium OB3b, selected amino acid residues interacting at the binding site of ethylbenzene, toluene, 1,3-dibutadiene, and trichloroethylene were mutated. Based on screening the strain energy, docking energy, and physiochemical properties, five mutants were downselected, B:Leu31Ser, B:Phe96Gly, B:Phe92Thr, B:Trp106Ala, and B:Tyr110Phe, which showed the docking energy of -6.3, -6.7, -6.3, -6.5, and -6.5 kcal/mol, respectively, as compared to the wild type (-5.2 kcal/mol) with ethylbenzene. These results suggest that these five mutants would likely increase methane oxidation rates compared to wild-type pMMO.

In Silico Evaluation of Enzymatic Tunnels in the Biotransformation of α-Tocopherol Esters

Motivation: α-Tocopherol is a molecule obtained primarily from plant sources that are important for the pharmaceutical and cosmetics industry. However, this component has some limitations such as sensitivity to oxygen, presence of light, and high temperatures. For this molecule to become more widely used, it is important to carry out a structural modification so that there is better stability and thus it can carry out its activities. To carry out this structural modification, some modifications are carried out, including the application of biotransformation using enzymes as biocatalysts. Thus, the application of a computational tool that helps in understanding the transport mechanisms of molecules in the tunnels present in the enzymatic structures is of fundamental importance because it promotes a computational screening facilitating bench applications.

Objective: The aim of this work was to perform a computational analysis of the biotransformation of α-tocopherol into tocopherol esters, observing the tunnels present in the enzymatic structures as well as the energies which correspond to the transport of molecules.

Method: To carry out this work, 9 lipases from different organisms were selected; their structures were analyzed by identifying the tunnels (quantity, conformation, and possibility of transport) and later the calculations of substrate transport for the biotransformation reaction in the identified tunnels were carried out. Additionally, the transport of the product obtained in the reaction through the tunnels was also carried out.

Results: In this work, the quantity of existing tunnels in the morphological conformational characteristics in the lipases was verified. Thus, the enzymes with fewer tunnels were RML (3 tunnels), LBC and RNL (4 tunnels), PBLL (5 tunnels), CALB (6 tunnels), HLG (7 tunnels), and LCR and LTL (8 tunnels) and followed by the enzyme LPP with the largest number of tunnels (39 tunnels). However, the enzyme that was most likely to transport substrates in terms of α-tocopherol biotransformation (in relation to the E_max and E_a energies of ligands and products) was CALB, as it obtains conformational and transport characteristics of molecules with a particularity. The most conditions of transport analysis were α-tocopherol tunnel 3 (E_max: −4.6 kcal/mol; E_a: 1.1 kcal/mol), vinyl acetate tunnel 1 (E_max: −2.4 kcal/mol; E_a: 0.1 kcal/mol), and tocopherol acetate tunnel 2 (E_max: −3.7 kcal/mol; E_a: 2 kcal/mol).

Antibodies targeting enzyme inhibition as potential tools for research and drug development

Antibodies have transformed biomedical research and are now being used for different experimental applications. Generally, the interaction of enzymes with their specific antibodies can lead to a reduction in their enzymatic activity. The effect of the antibody is dependent on its narrow i.e. the regions of the enzyme to which it is directed. The mechanism of this inhibition is rarely a direct combination of the antibodies with the catalytic site, but is rather due to steric hindrance, barring the substrate access to the active site. In several systems, however, the interaction with the antibody induces conformational changes on the enzyme that can either inhibit or enhance its catalytic activity. The extent of enzyme inhibition or enhancement is, therefore, a reflection of the nature and distribution of the various antigenic determinants on the enzyme molecule. Currently, the mode of action of many enzymes has been elucidated at the molecular level. We here review the molecular mechanisms and recent trends by which antibodies inhibit the catalytic activity of enzymes and provide examples of how specific antibodies can be useful for the neutralization of biologically active molecules.

TransportTools: a library for high-throughput analyses of internal voids in biomolecules and ligand transport through them

SUMMARY

Information regarding pathways through voids in biomolecules and their roles in ligand transport is critical to our understanding of the function of many biomolecules. Recently, the advent of high-throughput molecular dynamics simulations has enabled the study of these pathways, and of rare transport events. However, the scale and intricacy of the data produced requires dedicated tools in order to conduct analyses efficiently and without excessive demand on users. To fill this gap, we developed the TransportTools, which allows the investigation of pathways and their utilization across large, simulated datasets. TransportTools also facilitates the development of custom-made analyses.

AVAILABILITY AND IMPLEMENTATION

TransportTools is implemented in Python3 and distributed as pip and conda packages. The source code is available at https://github.com/labbit-eu/transport_tools. Data are available in a repository and can be accessed via a link: https://doi.org/10.5281/zenodo.5642954.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.