Software tools for identification, visualization and analysis of protein tunnels and channels

Bidentate Substrate Binding Mode in Oxalate Decarboxylase

Oxalate decarboxylase is an Mn- and O2-dependent enzyme in the bicupin superfamily that catalyzes the redox-neutral disproportionation of the oxalate monoanion to form carbon dioxide and formate. Its best-studied isozyme is from Bacillus subtilis where it is stress-induced under low pH conditions. Current mechanistic schemes assume a monodentate binding mode of the substrate to the N-terminal active site Mn ion to make space for a presumed O2 molecule, despite the fact that oxalate generally prefers to bind bidentate to Mn. We report on X-band 13C-electron nuclear double resonance (ENDOR) experiments on 13C-labeled oxalate bound to the active-site Mn(II) in wild-type oxalate decarboxylase at high pH, the catalytically impaired W96F mutant enzyme at low pH, and Mn(II) in aqueous solution. The ENDOR spectra of these samples are practically identical, which shows that the substrate binds bidentate (κO, κO') to the active site Mn(II) ion. Domain-based local pair natural orbital coupled cluster singles and doubles (DLPNO-CCSD) calculations of the expected 13C hyperfine coupling constants for bidentate bound oxalate predict ENDOR spectra in good agreement with the experiment, supporting bidentate bound substrate. Geometry optimization of a substrate-bound minimal active site model by density functional theory shows two possible substrate coordination geometries, bidentate and monodentate. The bidentate structure is energetically preferred by ~4.7 kcal/mol. Our results revise a long-standing hypothesis regarding substrate binding in the enzyme and suggest that dioxygen does not bind to the active site Mn ion after substrate binds. The results are in agreement with our recent mechanistic hypothesis of substrate activation via a long-range electron transfer process involving the C-terminal Mn ion.

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.

Water will Find Its Way: Transport through Narrow Tunnels in Hydrolases

An aqueous environment is vital for life as we know it, and water is essential for nearly all biochemical processes at the molecular level. Proteins utilize water molecules in various ways. Consequently, proteins must transport water molecules across their internal network of tunnels to reach the desired action sites, either within them or by functioning as molecular pipes to control cellular osmotic pressure. Despite water playing a crucial role in enzymatic activity and stability, its transport has been largely overlooked, with studies primarily focusing on water transport across membrane proteins. The transport of molecules through a protein's tunnel network is challenging to study experimentally, making molecular dynamics simulations the most popular approach for investigating such events. In this study, we focused on the transport of water molecules across three different α/β-hydrolases: haloalkane dehalogenase, epoxide hydrolase, and lipase. Using a 5 μs adaptive simulation per system, we observed that only a few tunnels were responsible for the majority of water transport in dehalogenase, in contrast to a higher diversity of tunnels in other enzymes. Interestingly, water molecules could traverse narrow tunnels with subangstrom bottlenecks, which is surprising given the commonly accepted water molecule radius of 1.4 Å. Our analysis of the transport events in such narrow tunnels revealed a markedly increased number of hydrogen bonds formed between the water molecules and protein, likely compensating for the steric penalty of the process. Overall, these commonly disregarded narrow tunnels accounted for ∼20% of the total water transport observed, emphasizing the need to surpass the standard geometrical limits on the functional tunnels to properly account for the relevant transport processes. Finally, we demonstrated how the obtained insights could be applied to explain the differences in a mutant of the human soluble epoxide hydrolase associated with a higher incidence of ischemic stroke.

Structural Characterization and Molecular Dynamics Study of the REPI Fusion Protein from Papaver somniferum L

REPI is a pivotal point enzyme in plant benzylisoquinoline alkaloid metabolism as it promotes the evolution of the biosynthetic branch of morphinan alkaloids. Experimental studies of its activity led to the identification of two modules (DRS and DRR) that catalyze two sequential steps of the epimerization of (S)- to (R)-reticuline. Recently, special attention has been paid to its genetic characterization and evolutionary history, but no structural analyses of the REPI protein have been conducted to date. We present here a computational structural characterization of REPI with heme and NADP cofactors in the apo state and in three complexes with substrate (S)-reticuline in DRS and intermediate 1,2-dehydroreticuline in DRS and in DRR. Since no experimental structure exists for REPI, we used its AlphaFold model as a scaffold to build up these four systems, which were submitted to all-atom molecular dynamics (MD) simulations. A comparison of MD results for the four systems revealed key dynamic changes associated with cofactor and ligand binding and provided a dynamic picture of the evolution of their structures and interactions. We also explored the possible dynamic occurrence of tunnels and electrostatic highways potentially involved in alternative mechanisms for channeling the intermediate from DRS to DRR.

Cavity Characterization in Supramolecular Cages

Confining molecular guests within artificial hosts has provided a major driving force in the rational design of supramolecular cages with tailored properties. Over the last 30 years, a set of design strategies have been developed that enabled the controlled synthesis of a myriad of cages. Recently, there has been a growing interest in involving in silico methods in this toolbox. Cavity shape and size are important parameters that can be easily accessed by inexpensive geometric algorithms. Although these algorithms are well developed for the detection of nonartificial cavities (e.g., enzymes), they are not routinely used for the rational design of supramolecular cages. In order to test the capabilities of this tool, we have evaluated the performance and characteristics of seven different cavity characterization software in the context of 22 analogues of well-known supramolecular cages. Among the tested software, KVFinder project and Fpocket proved to be the most software to characterize supramolecular cavities. With the results of this work, we aim to popularize this underused technique within the supramolecular community.

Molecular dynamics simulation and structural analysis of aquaporin Z from an Antarctic Pseudomonas sp. strain AMS3

Aquaporin is a water channel protein that facilitates the movement of water across the cell membrane. Aquaporin from the Antarctic region has been noted for its psychrophilic properties and its ability to perform at a lower temperature but there remains limited understanding of the water mechanism of Antarctic Pseudomonas sp. strain AMS3 However, studies regarding aquaporin isolated from psychrophilic Pseudomonas sp. are still scattered. Recently, the genome sequence of an Antarctic Pseudomonas sp. strain AMS3 revealed a gene sequence encoding for a putative aquaporin designated as AqpZ1 AMS3. In this study, structure analysis and a molecular dynamics (MD) simulation of a predicted model of a fully hydrated aquaporin tetramer embedded in a lipid bilayer was performed at different temperatures for structural flexibility and stability analysis. The MD simulation results revealed that the structures were able to remain stable at low to medium temperatures. The protein was observed to have high flexibility in the loop region as compared to the helices region throughout the simulated temperatures. The selectivity filter and NPA motifs play a major role in solute selectivity and the pore radius of the protein. The structural and functional characterization of this psychrophilic aquaporin provides new insights for the future applications of this protein.Communicated by Ramaswamy H. Sarma.

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.

Probing the function of a ligand-modulated dynamic tunnel in bifunctional proline utilization A (PutA)

In many bacteria, the reactions of proline catabolism are catalyzed by the bifunctional enzyme known as proline utilization A (PutA). PutA catalyzes the two-step oxidation of l-proline to l-glutamate using distinct proline dehydrogenase (PRODH) and l-glutamate-γ-semialdehyde dehydrogenase (GSALDH) active sites, which are separated by over 40 Å and connected by a complex tunnel system. The tunnel system consists of a main tunnel that connects the two active sites and functions in substrate channeling, plus six ancillary tunnels whose functions are unknown. Here we used tunnel-blocking mutagenesis to probe the role of a dynamic ancillary tunnel (tunnel 2a) whose shape is modulated by ligand binding to the PRODH active site. The 1.90 Å resolution crystal structure of Geobacter sulfurreducens PutA variant A206W verified that the side chain of Trp206 cleanly blocks tunnel 2a without perturbing the surrounding structure. Steady-state kinetic measurements indicate the mutation impaired PRODH activity without affecting the GSALDH activity. Single-turnover experiments corroborated a severe impairment of PRODH activity with flavin reduction decreased by nearly 600-fold in A206W relative to wild-type. Substrate channeling is also significantly impacted as A206W exhibited a 3000-fold lower catalytic efficiency in coupled PRODH-GSALDH activity assays, which measure NADH formation as a function of proline. The structure suggests that Trp206 inhibits binding of the substrate l-proline by preventing the formation of a conserved glutamate-arginine ion pair and closure of the PRODH active site. Our data are consistent with tunnel 2a serving as an open space through which the glutamate of the ion pair travels during the opening and closing of the active site in response to binding l-proline. These results confirm the essentiality of the conserved ion pair in binding l-proline and support the hypothesis that the ion pair functions as a gate that controls access to the PRODH active site.

Mimicking natural strategies to create multi-environment enzymatic reactors: From natural cell compartments to artificial polyelectrolyte reactors

Engineering microenvironments for sequential enzymatic reactions has attracted specific interest within different fields of research as an effective strategy to improve the catalytic performance of enzymes. While in industry most enzymatic reactions occur in a single compartment carrier, living cells are however able to conduct multiple reactions simultaneously within confined sub-compartments, or organelles. Engineering multi-compartments with regulated environments and transformation properties enhances enzyme activity and stability and thus increases the overall yield of final products. In this review, we discuss current and potential methods to fabricate artificial cells for sequential enzymatic reactions, which are inspired by mechanisms and metabolic pathways developed by living cells. We aim to advance the understanding of living cell complexity and its compartmentalization and present solutions to mimic these processes in vitro. Particular attention has been given to layer-by-layer assembly of polyelectrolytes for developing multi-compartments. We hope this review paves the way for the next steps toward engineering of smart artificial multi-compartments with adoptive stimuli-responsive properties, mimicking living cells to improve catalytic properties and efficiency of the enzymes and enhance their stability.

Protein Dynamics and Substrate Protonation States Mediate the Catalytic Action of trans-4-Hydroxy-l-Proline Dehydratase

The enzyme trans-4-hydroxy-l-proline (Hyp) dehydratase (HypD) is among the most abundant glycyl radical enzymes (GREs) in the healthy human gut microbiome and is considered a promising antibiotic target for the prominent antibiotic-resistant pathogen Clostridium difficile. Although an enzymatic mechanism has been proposed, the role of the greater HypD protein environment in mediating radical reactivity is not well understood. To fill this gap in understanding, we investigate HypD across multiple time- and length-scales using electronic structure modeling and classical molecular dynamics. We observe that the Hyp substrate protonation state significantly alters both its enzyme-free reactivity and its dynamics within the enzyme active site. Accurate coupled-cluster modeling suggests the deprotonated form of Hyp to be the most reactive protonation state for C5-H_pro-S activation. In the protein environment, hydrophobic interactions modulate the positioning of the Cys434 radical to enhance the reactivity of C5-H_pro-S abstraction. Long-time dynamics reveal that changing Hyp protonation states triggers the switching of a Leu643-gated water tunnel, a functional feature that has not yet been observed for members of the GRE superfamily.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

A Bittersweet Computational Journey among Glycosaminoglycans

Glycosaminoglycans (GAGs) are linear polysaccharides. In proteoglycans (PGs), they are attached to a core protein. GAGs and PGs can be found as free molecules, associated with the extracellular matrix or expressed on the cell membrane. They play a role in the regulation of a wide array of physiological and pathological processes by binding to different proteins, thus modulating their structure and function, and their concentration and availability in the microenvironment. Unfortunately, the enormous structural diversity of GAGs/PGs has hampered the development of dedicated analytical technologies and experimental models. Similarly, computational approaches (in particular, molecular modeling, docking and dynamics simulations) have not been fully exploited in glycobiology, despite their potential to demystify the complexity of GAGs/PGs at a structural and functional level. Here, we review the state-of-the art of computational approaches to studying GAGs/PGs with the aim of pointing out the “bitter” and “sweet” aspects of this field of research. Furthermore, we attempt to bridge the gap between bioinformatics and glycobiology, which have so far been kept apart by conceptual and technical differences. For this purpose, we provide computational scientists and glycobiologists with the fundamentals of these two fields of research, with the aim of creating opportunities for their combined exploitation, and thereby contributing to a substantial improvement in scientific knowledge.

Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology

Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Simulation of Ligand Transport in Receptors Using CaverDock

Interactions between enzymes and small molecules lie in the center of many fundamental biochemical processes. Their analysis using molecular dynamics simulations have high computational demands, geometric approaches fail to consider chemical forces, and molecular docking offers only static information. Recently, we proposed to combine molecular docking and geometric approaches in an application called CaverDock. CaverDock is discretizing enzyme tunnel into discs, iteratively docking with restraints into one disc after another and searching for a trajectory of the ligand passing through the tunnel. Here, we focus on the practical side of its usage describing the whole method: from getting the application, and processing the data through a workflow, to interpreting the results. Moreover, we shared the best practices, recommended how to solve the most common issues, and demonstrated its application on three use cases.

CaverDock: A Novel Method for the Fast Analysis of Ligand Transport

Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find a broad use in the fields of computational enzymology, drug design, and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.

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.

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.

Applications of water molecules for analysis of macromolecule properties

Water molecules maintain proteins' structures, functions, stabilities and dynamics. They can occupy certain positions or pass quickly via a protein's interior. Regardless of their behaviour, water molecules can be used for the analysis of proteins' structural features and biochemical properties. Here, we present a list of several software programs that use the information provided by water molecules to: i) analyse protein structures and provide the optimal positions of water molecules for protein hydration, ii) identify high-occupancy water sites in order to analyse ligand binding modes, and iii) detect and describe tunnels and cavities. The analysis of water molecules' distribution and trajectories sheds a light on proteins' interactions with small molecules, on the dynamics of tunnels and cavities, on protein composition and also on the functionality, transportation network and location of functionally relevant residues. Finally, the correct placement of water molecules in protein crystal structures can significantly improve the reliability of molecular dynamics simulations.

Identification of tunnels as in potato hydrolases

Enzymes with an active center hidden in the middle of the molecule in a tunnel-like cavity constitute an interesting object of analysis due to the highly specialized environment for the course of the catalytic reaction. Identifying the tunnel is a challenge in itself. Moreover, the structural conditioning for the course of the reaction provides information on the diversity of the environment, which must necessarily meet the conditions of high specificity. The use of a fuzzy oil drop model to identify residues constituting the walls of the tunnel located in the center of the protein seems highly justified. The fuzzy oil drop model, which assumes the highest concentration of hydrophobicity in the center of the molecule, in these enzymes shows a significant hydrophobicity deficit resulting from the absence of any residues in the central part of the molecule. Comparison of the expected distribution in consistent with the 3D Gaussian distribution where the observed distribution resulting from the interaction of residues in the protein shows significant differences precisely in the positions of residues located near the center of the molecule. The inside characteristics of the tunnel are the background for the enzymatic reaction. This environment additionally constitutes an external force field, which creates favorable conditions for carrying out the catalytic process. The use of fuzzy oil drop model has been verified using the potato (solanum tuberosum) epoxide hydrolase I. This forms the preliminary basis for testing the fuzzy oil drop model. The data presented here provides an impetus for a large scale analysis of all proteins containing tunnels in enzyme structures available in the Protein Data Bank (PDB).

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.

Engineering enzyme access tunnels

Enzymes are efficient and specific catalysts for many essential reactions in biotechnological and pharmaceutical industries. Many times, the natural enzymes do not display the catalytic efficiency, stability or specificity required for these industrial processes. The current enzyme engineering methods offer solutions to this problem, but they mainly target the buried active site where the chemical reaction takes place. Despite being many times ignored, the tunnels and channels connecting the environment with the active site are equally important for the catalytic properties of enzymes. Changes in the enzymatic tunnels and channels affect enzyme activity, specificity, promiscuity, enantioselectivity and stability. This review provides an overview of the emerging field of enzyme access tunnel engineering with case studies describing design of all the aforementioned properties. The software tools for the analysis of geometry and function of the enzymatic tunnels and channels and for the rational design of tunnel modifications will also be discussed. The combination of new software tools and enzyme engineering strategies will provide enzymes with access tunnels and channels specifically tailored for individual industrial processes.

Structural Biology of Proline Catabolic Enzymes

SIGNIFICANCE

Proline catabolism refers to the 4-electron oxidation of proline to glutamate catalyzed by the enzymes proline dehydrogenase (PRODH) and l-glutamate γ-semialdehyde dehydrogenase (GSALDH, or ALDH4A1). These enzymes and the intermediate metabolites of the pathway have been implicated in tumor growth and suppression, metastasis, hyperprolinemia metabolic disorders, schizophrenia susceptibility, life span extension, and pathogen virulence and survival. In some bacteria, PRODH and GSALDH are combined into a bifunctional enzyme known as proline utilization A (PutA). PutAs are not only virulence factors in some pathogenic bacteria but also fascinating systems for studying the coordination of metabolic enzymes via substrate channeling. Recent Advances: The past decade has seen an explosion of structural data for proline catabolic enzymes. This review surveys these structures, emphasizing protein folds, substrate recognition, oligomerization, kinetic mechanisms, and substrate channeling in PutA.

CRITICAL ISSUES

Major unsolved structural targets include eukaryotic PRODH, the complex between monofunctional PRODH and monofunctional GSALDH, and the largest of all PutAs, trifunctional PutA. The structural basis of PutA-membrane association is poorly understood. Fundamental aspects of substrate channeling in PutA remain unknown, such as the identity of the channeled intermediate, how the tunnel system is activated, and the roles of ancillary tunnels.

FUTURE DIRECTIONS

New approaches are needed to study the molecular and in vivo mechanisms of substrate channeling. With the discovery of the proline cycle driving tumor growth and metastasis, the development of inhibitors of proline metabolic enzymes has emerged as an exciting new direction. Structural biology will be important in these endeavors.

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.

An Algorithm for Computing Side Chain Conformational Variations of a Protein Tunnel/Channel

In this paper, a novel method to compute side chain conformational variations for a protein molecule tunnel (or channel) is proposed. From the conformational variations, we compute the flexibly deformed shapes of the initial tunnel, and present a way to compute the maximum size of the ligand that can pass through the deformed tunnel. By using the two types of graphs corresponding to amino acids and their side chain rotamers, the suggested algorithm classifies amino acids and rotamers which possibly have collisions. Based on the divide and conquer technique, local side chain conformations are computed first, and then a global conformation is generated by combining them. With the exception of certain cases, experimental results show that the algorithm finds up to 327,680 valid side chain conformations from 12⁸~12³³ conformation candidates within three seconds.

Computational Analysis of Protein Tunnels and Channels

Protein tunnels connecting the functional buried cavities with bulk solvent and protein channels, enabling the transport through biological membranes, represent the structural features that govern the exchange rates of ligands, ions, and water solvent. Tunnels and channels are present in a vast number of known proteins and provide control over their function. Modification of these structural features by protein engineering frequently provides proteins with improved properties. Here we present a detailed computational protocol employing the CAVER software that is applicable for: (1) the analysis of tunnels and channels in protein structures, and (2) the selection of hot-spot residues in tunnels or channels that can be mutagenized for improved activity, specificity, enantioselectivity, or stability.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.

Analysis of aquaporins from the euryhaline barnacle Balanus improvisus reveals differential expression in response to changes in salinity

Barnacles are sessile macro-invertebrates, found along rocky shores in coastal areas worldwide. The euryhaline bay barnacle Balanus improvisus (Darwin, 1854) (= Amphibalanus improvisus) can tolerate a wide range of salinities, but the molecular mechanisms underlying the osmoregulatory capacity of this truly brackish species are not well understood. Aquaporins are pore-forming integral membrane proteins that facilitate transport of water, small solutes and ions through cellular membranes, and that have been shown to be important for osmoregulation in many organisms. The knowledge of the function of aquaporins in crustaceans is, however, limited and nothing is known about them in barnacles. We here present the repertoire of aquaporins from a thecostracan crustacean, the barnacle B. improvisus, based on genome and transcriptome sequencing. Our analyses reveal that B. improvisus contains eight genes for aquaporins. Phylogenetic analysis showed that they represented members of the classical water aquaporins (Aqp1, Aqp2), the aquaglyceroporins (Glp1, Glp2), the unorthodox aquaporin (Aqp12) and the arthropod-specific big brain aquaporin (Bib). Interestingly, we also found two big brain-like proteins (BibL1 and BibL2) constituting a new group of aquaporins not yet described in arthropods. In addition, we found that the two water-specific aquaporins were expressed as C-terminal splice variants. Heterologous expression of some of the aquaporins followed by functional characterization showed that Aqp1 transported water and Glp2 water and glycerol, agreeing with the predictions of substrate specificity based on 3D modeling and phylogeny. To investigate a possible role for the B. improvisus aquaporins in osmoregulation, mRNA expression changes in adult barnacles were analysed after long-term acclimation to different salinities. The most pronounced expression difference was seen for AQP1 with a substantial (>100-fold) decrease in the mantle tissue in low salinity (3 PSU) compared to high salinity (33 PSU). Our study provides a base for future mechanistic studies on the role of aquaporins in osmoregulation.

Molecular modelling studies of kdr mutations in voltage gated sodium channel revealed significant conformational variations contributing to insecticide resistance

Voltage gated sodium channels (VGSC) of mosquito vectors are the primary targets of dichlorodiphenyltrichloroethane (DDT) and other synthetic pyrethroids used in public health programmes. The knockdown resistant (kdr) mutations in VGSC are associated with the insecticide resistance especially in Anophelines. The present study is aimed to emphasize and demarcate the impact of three kdr-mutations such as L1014S, L1014F and L1014H on insecticide resistance. The membrane model of sodium transport domain of VGSC (STD-VGSC) was constructed using de novo approach based on domain and trans-membrane predictions. The comparative molecular modelling studies of wild type and mutant models of STD-VGSC revealed that L1014F mutant was observed to be near native to the wild type model in all the respects, but, L1014S and L1014H mutations showed drastic variations in the energy levels, root mean square fluctuations (RMSF) that resulted in conformational variations. The predicted binding sites also showed variable cavity volumes and RMSF in L1014S and L1014H mutants. Further, DDT also found be bound in near native manner to wild type in L1014F mutant and with variable orientation and affinities in L1014S and L1014H mutants. The variations and fluctuations observed in mutant structures explained that each mutation has its specific impact on the conformation of VGSC and its binding with DDT. The study provides new insights into the structure-function-correlations of mutant STD-VGSC structures and demonstrates the role and effects of kdr mutations on insecticide resistance in mosquito vectors.

Protein Binding Pocket Dynamics

The dynamics of protein binding pockets are crucial for their interaction specificity. Structural flexibility allows proteins to adapt to their individual molecular binding partners and facilitates the binding process. This implies the necessity to consider protein internal motion in determining and predicting binding properties and in designing new binders. Although accounting for protein dynamics presents a challenge for computational approaches, it expands the structural and physicochemical space for compound design and thus offers the prospect of improved binding specificity and selectivity. A cavity on the surface or in the interior of a protein that possesses suitable properties for binding a ligand is usually referred to as a binding pocket. The set of amino acid residues around a binding pocket determines its physicochemical characteristics and, together with its shape and location in a protein, defines its functionality. Residues outside the binding site can also have a long-range effect on the properties of the binding pocket. Cavities with similar functionalities are often conserved across protein families. For example, enzyme active sites are usually concave surfaces that present amino acid residues in a suitable configuration for binding low molecular weight compounds. Macromolecular binding pockets, on the other hand, are located on the protein surface and are often shallower. The mobility of proteins allows the opening, closing, and adaptation of binding pockets to regulate binding processes and specific protein functionalities. For example, channels and tunnels can exist permanently or transiently to transport compounds to and from a binding site. The influence of protein flexibility on binding pockets can vary from small changes to an already existent pocket to the formation of a completely new pocket. Here, we review recent developments in computational methods to detect and define binding pockets and to study pocket dynamics. We introduce five different classes of protein pocket dynamics: (1) appearance/disappearance of a subpocket in an existing pocket; (2) appearance/disappearance of an adjacent pocket on the protein surface in the direct vicinity of an already existing pocket; (3) pocket breathing, which may be caused by side-chain fluctuations or backbone or interdomain vibrational motion; (4) opening/closing of a channel or tunnel, connecting a pocket inside the protein with solvent, including lid motion; and (5) the appearance/disappearance of an allosteric pocket at a site on a protein distinct from an already existing pocket with binding of a ligand to the allosteric binding site affecting the original pocket. We suggest that the class of pocket dynamics, as well as the type and extent of protein motion affecting the binding pocket, should be factors considered in choosing the most appropriate computational approach to study a given binding pocket. Furthermore, we examine the relationship between pocket dynamics classes and induced fit, conformational selection, and gating models of ligand binding on binding kinetics and thermodynamics. We discuss the implications of protein binding pocket dynamics for drug design and conclude with potential future directions for computational analysis of protein binding pocket dynamics.

HotSpot Wizard 2.0: automated design of site-specific mutations and smart libraries in protein engineering

HotSpot Wizard 2.0 is a web server for automated identification of hot spots and design of smart libraries for engineering proteins' stability, catalytic activity, substrate specificity and enantioselectivity. The server integrates sequence, structural and evolutionary information obtained from 3 databases and 20 computational tools. Users are guided through the processes of selecting hot spots using four different protein engineering strategies and optimizing the resulting library's size by narrowing down a set of substitutions at individual randomized positions. The only required input is a query protein structure. The results of the calculations are mapped onto the protein's structure and visualized with a JSmol applet. HotSpot Wizard lists annotated residues suitable for mutagenesis and can automatically design appropriate codons for each implemented strategy. Overall, HotSpot Wizard provides comprehensive annotations of protein structures and assists protein engineers with the rational design of site-specific mutations and focused libraries. It is freely available at http://loschmidt.chemi.muni.cz/hotspotwizard.

CAVER: Algorithms for Analyzing Dynamics of Tunnels in Macromolecules

The biological function of a macromolecule often requires that a small molecule or ion is transported through its structure. The transport pathway often leads through void spaces in the structure. The properties of transport pathways change significantly in time; therefore, the analysis of a trajectory from molecular dynamics rather than of a single static structure is needed for understanding the function of pathways. The identification and analysis of transport pathways are challenging because of the high complexity and diversity of macromolecular shapes, the thermal motion of their atoms, and the large amount of conformations needed to properly describe conformational space of protein structure. In this paper, we describe the principles of the CAVER 3.0 algorithms for the identification and analysis of properties of transport pathways both in static and dynamic structures. Moreover, we introduce the improved clustering solution for finding tunnels in macromolecules, which is included in the latest CAVER 3.02 version. Voronoi diagrams are used to identify potential pathways in each snapshot of a molecular dynamics trajectory and clustering is then used to find the correspondence between tunnels from different snapshots. Furthermore, the geometrical properties of pathways and their evolution in time are computed and visualized.

Computational screening and characterization of putative vaccine candidates of Plasmodium vivax

Plasmodium vivax is the most prevalent species of malaria affecting millions of people annually worldwide and demands effective interventions to develop a successful vaccine. In this milieu, we have dedicated noteworthy efforts to characterize the proteome of P. vivax to give a lead for the epitope-based vaccine development. Membrane proteins of P. vivax were collected from SWISS PROT database and 10 antigenic proteins were identified among them by in silico analysis using multiple servers. T-cell and B-cell epitopes were identified and their immunity was assessed. Their ability to trigger humoral and cell-mediated responses was determined. Three dimensional models were constructed for the antigenic proteins using Modeller, Phyre2, and Modloop tools and their quality was validated using PROCHECK and ProSA-web validation servers. Further, the binding affinity and molecular interactions of these antigenic proteins were characterized by performing protein-protein docking against transmission-blocking anti-malaria antibody Fab2A8 (PDB ID: 3S62) using Z-dock module of Discovery Studio 4.0. The presence of potential B & T-cell epitopes, major histocompatibility complex-binding sites, and their efficient interactions with Fab2A8 antibody suggests the use of predicted antigenic proteins for the construction of multi-epitope peptide vaccine against P. vivax.

PrinCCes: Continuity-based geometric decomposition and systematic visualization of the void repertoire of proteins

Grooves and pockets on the surface, channels through the protein, the chambers or cavities, and the tunnels connecting the internal points to each other or to the external fluid environment are fundamental determinants of a wide range of biological functions. PrinCCes (Protein internal Channel & Cavity estimation) is a computer program supporting the visualization of voids. It includes a novel algorithm for the decomposition of the entire void volume of the protein or protein complex to individual entities. The decomposition is based on continuity. An individual void is defined by uninterrupted extension in space: a spherical probe can freely move between any two internal locations of a continuous void. Continuous voids are detected irrespective of their topological complexity, they may contain any number of holes and bifurcations. The voids of a protein can be visualized one by one or in combinations as triangulated surfaces. The output is automatically exported to free VMD (Visual Molecular Dynamics) or Chimera software, allowing the 3D rotation of the surfaces and the production of publication quality images. PrinCCes with graphic user interface and command line versions are available for MS Windows and Linux. The source code and executable can be downloaded at any of the following links: http://scholar.semmelweis.hu/czirjakgabor/s/princces/#t1 https://github.com/CzirjakGabor/PrinCCes http://1drv.ms/1bP9iJ3.

BetaCavityWeb: a webserver for molecular voids and channels

Molecular cavities, which include voids and channels, are critical for molecular function. We present a webserver, BetaCavityWeb, which computes these cavities for a given molecular structure and a given spherical probe, and reports their geometrical properties: volume, boundary area, buried area, etc. The server's algorithms are based on the Voronoi diagram of atoms and its derivative construct: the beta-complex. The correctness of the computed result and computational efficiency are both mathematically guaranteed. BetaCavityWeb is freely accessible at the Voronoi Diagram Research Center (VDRC) (http://voronoi.hanyang.ac.kr/betacavityweb).

CHEXVIS: a tool for molecular channel extraction and visualization

Background

Understanding channel structures that lead to active sites or traverse the molecule is important in the study of molecular functions such as ion, ligand, and small molecule transport. Efficient methods for extracting, storing, and analyzing protein channels are required to support such studies. Further, there is a need for an integrated framework that supports computation of the channels, interactive exploration of their structure, and detailed visual analysis of their properties.

Results

We describe a method for molecular channel extraction based on the alpha complex representation. The method computes geometrically feasible channels, stores both the volume occupied by the channel and its centerline in a unified representation, and reports significant channels. The representation also supports efficient computation of channel profiles that help understand channel properties. We describe methods for effective visualization of the channels and their profiles. These methods and the visual analysis framework are implemented in a software tool, ChExVis. We apply the method on a number of known channel containing proteins to extract pore features. Results from these experiments on several proteins show that ChExVis performance is comparable to, and in some cases, better than existing channel extraction techniques. Using several case studies, we demonstrate how ChExVis can be used to study channels, extract their properties and gain insights into molecular function.

Conclusion

ChExVis supports the visual exploration of multiple channels together with their geometric and physico-chemical properties thereby enabling the understanding of the basic biology of transport through protein channels. The ChExVis web-server is freely available at http://vgl.serc.iisc.ernet.in/chexvis/. The web-server is supported on all modern browsers with latest Java plug-in.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0545-9) contains supplementary material, which is available to authorized users.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Anaerobic oxidation of long-chain n-alkanes by the hyperthermophilic sulfate-reducing archaeon, Archaeoglobus fulgidus

The thermophilic sulfate-reducing archaeon Archaeoglobus fulgidus strain VC-16 (DSM 4304), which is known to oxidize fatty acids and n-alkenes, was shown to oxidize saturated hydrocarbons (n-alkanes in the range C10-C21) with thiosulfate or sulfate as a terminal electron acceptor. The amount of n-hexadecane degradation observed was in stoichiometric agreement with the theoretically expected amount of thiosulfate reduction. One of the pathways used by anaerobic microorganisms to activate alkanes is addition to fumarate that involves alkylsuccinate synthase as a key enzyme. A search for genes encoding homologous enzymes in A. fulgidus identified the pflD gene (locus-tag AF1449) that was previously annotated as a pyruvate formate lyase. A phylogenetic analysis revealed that this gene is of bacterial origin and was likely acquired by A. fulgidus from a bacterial donor through a horizontal gene transfer. Based on three-dimensional modeling of the corresponding protein and molecular dynamic simulations, we hypothesize an alkylsuccinate synthase activity for this gene product. The pflD gene expression was upregulated during the growth of A. fulgidus on an n-alkane (C16) compared with growth on a fatty acid. Our results suggest that anaerobic alkane degradation in A. fulgidus may involve the gene pflD in alkane activation through addition to fumarate. These findings highlight the possible importance of hydrocarbon oxidation at high temperatures by A. fulgidus in hydrothermal vents and the deep biosphere.

Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes

Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or -holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost.