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/ß-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.

Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters.

ABCG46 of the legume Medicago truncatula is an ABC-type transporter responsible for highly selective translocation of the phenylpropanoids, 4-coumarate, and liquiritigenin, over the plasma membrane. To investigate molecular determinants of the observed substrate selectivity, we applied a combination of phylogenetic and biochemical analyses, AlphaFold2 structure prediction, molecular dynamics simulations, and mutagenesis. We discovered an unusually narrow transient access path to the central cavity of MtABCG46 that constitutes an initial filter responsible for the selective translocation of phenylpropanoids through a lipid bilayer. Furthermore, we identified remote residue F562 as pivotal for maintaining the stability of this filter. The determination of individual amino acids that impact the selective transport of specialized metabolites may provide new opportunities associated with ABCGs being of interest, in many biological scenarios.

Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations.

Nowadays, molecular dynamics (MD) simulations of proteins with hundreds of thousands of snapshots are commonly produced using modern GPUs. However, due to the abundance of data, analyzing transport tunnels present in the internal voids of these molecules, in all generated snapshots, has become challenging. Here, we propose to combine the usage of CAVER3, the most popular tool for tunnel calculation, and the TransportTools Python3 library into a divide-and-conquer approach to speed up tunnel calculation and reduce the hardware resources required to analyze long MD simulations in detail. By slicing an MD trajectory into smaller pieces and performing a tunnel analysis on these pieces by CAVER3, the runtime and resources are considerably reduced. Next, the TransportTools library merges the smaller pieces and gives an overall view of the tunnel network for the complete trajectory without quality loss.

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.

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

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

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.

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/.

Deciphering the Structural Basis of High Thermostability of Dehalogenase from Psychrophilic Bacterium Marinobacter sp. ELB17.

Haloalkane dehalogenases are enzymes with a broad application potential in biocatalysis, bioremediation, biosensing and cell imaging. The new haloalkane dehalogenase DmxA originating from the psychrophilic bacterium Marinobacter sp. ELB17 surprisingly possesses the highest thermal stability (apparent melting temperature Tm,app = 65.9 °C) of all biochemically characterized wild type haloalkane dehalogenases belonging to subfamily II. The enzyme was successfully expressed and its crystal structure was solved at 1.45 Å resolution. DmxA structure contains several features distinct from known members of haloalkane dehalogenase family: (i) a unique composition of catalytic residues; (ii) a dimeric state mediated by a disulfide bridge; and (iii) narrow tunnels connecting the enzyme active site with the surrounding solvent. The importance of narrow tunnels in such paradoxically high stability of DmxA enzyme was confirmed by computational protein design and mutagenesis experiments.

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.

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.

Molecular Gating of an Engineered Enzyme Captured in Real Time.

Enzyme engineering tends to focus on the design of active sites for the chemical steps, while the physical steps of the catalytic cycle are often overlooked. Tight binding of a substrate in an active site is beneficial for the chemical steps, whereas good accessibility benefits substrate binding and product release. Many enzymes control the accessibility of their active sites by molecular gates. Here we analyzed the dynamics of a molecular gate artificially introduced into an access tunnel of the most efficient haloalkane dehalogenase using pre-steady-state kinetics, single-molecule fluorescence spectroscopy, and molecular dynamics. Photoinduced electron-transfer-fluorescence correlation spectroscopy (PET-FCS) has enabled real-time observation of molecular gating at the single-molecule level with rate constants ( kon = 1822 s-1, koff = 60 s-1) corresponding well with those from the pre-steady-state kinetics ( k-1 = 1100 s-1, k1 = 20 s-1). The PET-FCS technique is used here to study the conformational dynamics in a soluble enzyme, thus demonstrating an additional application for this method. Engineering dynamical molecular gates represents a widely applicable strategy for designing efficient biocatalysts.

CAVER Analyst 2.0: analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories.

Motivation: Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results: CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation: CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz. Supplementary information: Supplementary data are available at Bioinformatics online.

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

The traditional way of rationally engineering enzymes to change their biocatalytic properties utilizes the modifications of their active sites. Another emerging approach is the engineering of structural features involved in the exchange of ligands between buried active sites and the surrounding solvent. However, surprisingly little is known about the effects of mutations that alter the access tunnels on the enzymes' catalytic properties, and how these tunnels should be redesigned to allow fast passage of cognate substrates and products. Thus, we have systematically studied the effects of single-point mutations in a tunnel-lining residue of a haloalkane dehalogenase on the binding kinetics and catalytic conversion of both linear and branched haloalkanes. The hotspot residue Y176 was identified using computer simulations and randomized through saturation mutagenesis, and the resulting variants were screened for shifts in binding rates. Strikingly, opposite effects of the substituted residues on the catalytic efficiency toward linear and branched substrates were observed, which was found to be due to substrate-specific requirements in the critical steps of the respective catalytic cycles. We conclude that not only the catalytic sites, but also the access pathways must be tailored specifically for each individual ligand, which is a new paradigm in protein engineering and de novo protein design. A rational approach is proposed here to address more effectively the task of designing ligand-specific tunnels using computational tools.

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.

Computer-assisted engineering of hyperstable fibroblast growth factor 2.

Fibroblast growth factors (FGFs) serve numerous regulatory functions in complex organisms, and their corresponding therapeutic potential is of growing interest to academics and industrial researchers alike. However, applications of these proteins are limited due to their low stability. Here we tackle this problem using a generalizable computer-assisted protein engineering strategy to create a unique modified FGF2 with nine mutations displaying unprecedented stability and uncompromised biological function. The data from the characterization of stabilized FGF2 showed a remarkable prediction potential of in silico methods and provided insight into the unfolding mechanism of the protein. The molecule holds a considerable promise for stem cell research and medical or pharmaceutical applications.

Catalytic Cycle of Haloalkane Dehalogenases Toward Unnatural Substrates Explored by Computational Modeling.

The anthropogenic toxic compound 1,2,3-trichloropropane is poorly degradable by natural enzymes. We have previously constructed the haloalkane dehalogenase DhaA31 by focused directed evolution ( Pavlova, M. et al. Nat. Chem. Biol. 2009 , 5 , 727 - 733 ), which is 32 times more active than the wild-type enzyme and is currently the most active variant known against that substrate. Recent evidence has shown that the structural basis responsible for the higher activity of DhaA31 was poorly understood. Here we have undertaken a comprehensive computational study of the main steps involved in the biocatalytic hydrolysis of 1,2,3-trichloropropane to decipher the structural basis for such enhancements. Using molecular dynamics and quantum mechanics approaches we have surveyed (i) the substrate binding, (ii) the formation of the reactive complex, (iii) the chemical step, and (iv) the release of the products. We showed that the binding of the substrate and its transport through the molecular tunnel to the active site is a relatively fast process. The cleavage of the carbon-halogen bond was previously identified as the rate-limiting step in the wild-type. Here we demonstrate that this step was enhanced in DhaA31 due to a significantly higher number of reactive configurations of the substrate and a decrease of the energy barrier to the SN2 reaction. C176Y and V245F were identified as the key mutations responsible for most of those improvements. The release of the alcohol product was found to be the rate-limiting step in DhaA31 primarily due to the C176Y mutation. Mutational dissection of DhaA31 and kinetic analysis of the intermediate mutants confirmed the theoretical observations. Overall, our comprehensive computational approach has unveiled mechanistic details of the catalytic cycle which will enable a balanced design of more efficient enzymes. This approach is applicable to deepen the biochemical knowledge of a large number of other systems and may contribute to robust strategies in the development of new biocatalysts.

FireProt: web server for automated design of thermostable proteins.

There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.

Ancestral Haloalkane Dehalogenases Show Robustness and Unique Substrate Specificity.

Ancestral sequence reconstruction (ASR) represents a powerful approach for empirical testing structure-function relationships of diverse proteins. We employed ASR to predict sequences of five ancestral haloalkane dehalogenases (HLDs) from the HLD-II subfamily. Genes encoding the inferred ancestral sequences were synthesized and expressed in Escherichia coli, and the resurrected ancestral enzymes (AncHLD1-5) were experimentally characterized. Strikingly, the ancestral HLDs exhibited significantly enhanced thermodynamic stability compared to extant enzymes (ΔTm up to 24 °C), as well as higher specific activities with preference for short multi-substituted halogenated substrates. Moreover, multivariate statistical analysis revealed a shift in the substrate specificity profiles of AncHLD1 and AncHLD2. This is extremely difficult to achieve by rational protein engineering. The study highlights that ASR is an efficient approach for the development of novel biocatalysts and robust templates for directed evolution.

Different Structural Origins of the Enantioselectivity of Haloalkane Dehalogenases toward Linear ß-Haloalkanes: Open-Solvated versus Occluded-Desolvated Active Sites.

The enzymatic enantiodiscrimination of linear ß-haloalkanes is difficult because the simple structures of the substrates prevent directional interactions. Herein we describe two distinct molecular mechanisms for the enantiodiscrimination of the ß-haloalkane 2-bromopentane by haloalkane dehalogenases. Highly enantioselective DbjA has an open, solvent-accessible active site, whereas the engineered enzyme DhaA31 has an occluded and less solvated cavity but shows similar enantioselectivity. The enantioselectivity of DhaA31 arises from steric hindrance imposed by two specific substitutions rather than hydration as in DbjA.

Enzyme Tunnels and Gates As Relevant Targets in Drug Design.

Many enzymes contain tunnels and gates that are essential to their function. Gates reversibly switch between open and closed conformations and thereby control the traffic of small molecules-substrates, products, ions, and solvent molecules-into and out of the enzyme's structure via molecular tunnels. Many transient tunnels and gates undoubtedly remain to be identified, and their functional roles and utility as potential drug targets have received comparatively little attention. Here, we describe a set of general concepts relating to the structural properties, function, and classification of these interesting structural features. In addition, we highlight the potential of enzyme tunnels and gates as targets for the binding of small molecules. The different types of binding that are possible and the potential pharmacological benefits of such targeting are discussed. Twelve examples of ligands bound to the tunnels and/or gates of clinically relevant enzymes are used to illustrate the different binding modes and to explain some new strategies for drug design. Such strategies could potentially help to overcome some of the problems facing medicinal chemists and lead to the discovery of more effective drugs.