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MRE11 nuclease is a central player in signaling and processing DNA damage, and in resolving stalled replication forks. Here, we describe the identification and characterization of new MRE11 inhibitors MU147 and MU1409. Both compounds inhibit MRE11 nuclease more specifically and effectively than the relatively weak state-of-the-art inhibitor mirin. They also abrogate double-strand break repair mechanisms that rely on MRE11 nuclease activity, without impairing ATM activation. Inhibition of MRE11 also impairs nascent strand degradation of stalled replication forks and selectively affects BRCA2-deficient cells. Herein, we illustrate that our newly discovered compounds MU147 and MU1409 can be used as chemical probes to further explore the biological role of MRE11 and support the potential clinical relevance of pharmacological inhibition of this nuclease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Masson SAS. All rights reserved.)

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.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

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.

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.

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, k off /k on , 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.

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., (© 2023. The Author(s).)

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

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., (© The Author(s) 2021. Published by Oxford University Press.)

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., (© The Author(s) 2020. Published by Oxford University Press.)

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/.

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.

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., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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 T m,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 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., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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 ( k on = 1822 s -1 , k off = 60 s -1 ) corresponding well with those from the pre-steady-state kinetics ( k -1 = 1100 s -1 , k 1 = 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.

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.

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., (© 2018 Federation of European Biochemical Societies.)

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., (© 2017 Wiley Periodicals, Inc.)

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.

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., (© 2016 Wiley Periodicals, Inc.)

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 S N 2 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.

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 (ΔT m 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., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The enzymatic enantiodiscrimination of linear β-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 β-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., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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.

Haloalkane dehalogenases (HLDs) have recently been discovered in a number of bacteria, including symbionts and pathogens of both plants and humans. However, the biological roles of HLDs in these organisms are unclear. The development of efficient HLD inhibitors serving as molecular probes to explore their function would represent an important step toward a better understanding of these interesting enzymes. Here we report the identification of inhibitors for this enzyme family using two different approaches. The first builds on the structures of the enzymes' known substrates and led to the discovery of less potent nonspecific HLD inhibitors. The second approach involved the virtual screening of 150,000 potential inhibitors against the crystal structure of an HLD from the human pathogen Mycobacterium tuberculosis H37Rv. The best inhibitor exhibited high specificity for the target structure, with an inhibition constant of 3 μM and a molecular architecture that clearly differs from those of all known HLD substrates. The new inhibitors will be used to study the natural functions of HLDs in bacteria, to probe their mechanisms, and to achieve their stabilization., (Copyright © 2016, American Society for Microbiology. All Rights Reserved.)

There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

Wedelolactone, a plant coumestan, was shown to act as anti-cancer agent for breast and prostate carcinomas in vitro and in vivo targeting multiple cellular proteins including androgen receptors, 5-lipoxygenase and topoisomerase IIα. It is cytotoxic to breast, prostate, pituitary and myeloma cancer cell lines in vitro at μM concentrations. In this study, however, a novel biological activity of nM dose of wedelolactone was demonstrated. Wedelolactone acts as agonist of estrogen receptors (ER) α and β as demonstrated by transactivation of estrogen response element (ERE) in cells transiently expressing either ERα or ERβ and by molecular docking of this coumestan into ligand binding pocket of both ERα and ERβ. In breast cancer cells, wedelolactone stimulates growth of estrogen receptor-positive cells, expression of estrogen-responsive genes and activates rapid non-genomic estrogen signalling. All these effects can be inhibited by pretreatment with pure ER antagonist ICI 182,780 and they are not observed in ER-negative breast cancer cells. We conclude that wedelolactone acts as phytoestrogen in breast cancer cells by stimulating ER genomic and non-genomic signalling pathways., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Substrate specificity is a key feature of enzymes determining their applicability in biomaterials and biotechnologies. Experimental testing of activities with novel substrates is a time-consuming and inefficient process, typically resulting in many failures. Here, we present an experimentally validated in silico method for the discovery of novel substrates of enzymes with a known reaction mechanism. The method was developed for a model system of biotechnologically relevant enzymes, haloalkane dehalogenases. On the basis of the parametrization of six different haloalkane dehalogenases with 30 halogenated substrates, mechanism-based geometric criteria for reactivity approximation were defined. These criteria were subsequently applied to the previously experimentally uncharacterized haloalkane dehalogenase DmmA. The enzyme was computationally screened against 41,366 compounds, yielding 548 structurally unique compounds as potential substrates. Eight out of 16 experimentally tested top-ranking compounds were active with DmmA, indicating a 50% success rate for the prediction of substrates. The remaining eight compounds were able to bind to the active site and inhibit enzymatic activity. These results confirmed good applicability of the method for prioritizing active compounds-true substrates and binders-for experimental testing. All validated substrates were large compounds often containing polyaromatic moieties, which have never before been considered as potential substrates for this enzyme family. Whereas four of these novel substrates were specific to DmmA, two substrates showed activity with three other tested haloalkane dehalogenases, i.e., DhaA, DbjA, and LinB. Additional validation of the developed screening strategy with the data set of over 200 known substrates of Candida antarctica lipase B confirmed its applicability for the identification of novel substrates of other biotechnologically relevant enzymes with an available tertiary structure and known reaction mechanism.

Unlabelled: The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics., Availability and Implementation: CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Multienzyme processes represent an important area of biocatalysis. Their efficiency can be enhanced by optimization of the stoichiometry of the biocatalysts. Here we present a workflow for maximizing the efficiency of a three-enzyme system catalyzing a five-step chemical conversion. Kinetic models of pathways with wild-type or engineered enzymes were built, and the enzyme stoichiometry of each pathway was optimized. Mathematical modeling and one-pot multienzyme experiments provided detailed insights into pathway dynamics, enabled the selection of a suitable engineered enzyme, and afforded high efficiency while minimizing biocatalyst loadings. Optimizing the stoichiometry in a pathway with an engineered enzyme reduced the total biocatalyst load by an impressive 56 %. Our new workflow represents a broadly applicable strategy for optimizing multienzyme processes., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The crystal structure of the novel haloalkane dehalogenase DbeA from Bradyrhizobium elkanii USDA94 revealed the presence of two chloride ions buried in the protein interior. The first halide-binding site is involved in substrate binding and is present in all structurally characterized haloalkane dehalogenases. The second halide-binding site is unique to DbeA. To elucidate the role of the second halide-binding site in enzyme functionality, a two-point mutant lacking this site was constructed and characterized. These substitutions resulted in a shift in the substrate-specificity class and were accompanied by a decrease in enzyme activity, stability and the elimination of substrate inhibition. The changes in enzyme catalytic activity were attributed to deceleration of the rate-limiting hydrolytic step mediated by the lower basicity of the catalytic histidine.

We emphasize the importance of dynamics and hydration for enzymatic catalysis and protein design by transplanting the active site from a haloalkane dehalogenase with high enantioselectivity to nonselective dehalogenase. Protein crystallography confirms that the active site geometry of the redesigned dehalogenase matches that of the target, but its enantioselectivity remains low. Time-dependent fluorescence shifts and computer simulations revealed that dynamics and hydration at the tunnel mouth differ substantially between the redesigned and target dehalogenase.

Fructose-1-phosphate (F1P) is the preferred effector of the catabolite repressor/activator (Cra) protein of the soil bacterium Pseudomonas putida but its ability to bind other metabolic intermediates in vivo is unclear. The Cra protein of this microorganism (Cra(PP)) was submitted to mobility shift assays with target DNA sequences (the PfruB promoter) and candidate effectors fructose-1,6-bisphosphate (FBP), glucose 6-phosphate (G6P), and fructose-6-phosphate (F6P). 1 mM F1P was sufficient to release most of the Cra protein from its operators but more than 10 mM of FBP or G6P was required to free the same complex. However, isothermal titration microcalorimetry failed to expose any specific interaction between Cra(PP) and FBP or G6P. To solve this paradox, transcriptional activity of a PfruB-lacZ fusion was measured in wild-type and ΔfruB cells growing on substrates that change the intracellular concentrations of F1P and FBP. The data indicated that PfruB activity was stimulated by fructose but not by glucose or succinate. This suggested that Cra(PP) represses expression in vivo of the cognate fruBKA operon in a fashion dependent just on F1P, ruling out any other physiological effector. Molecular docking and dynamic simulations of the Cra-agonist interaction indicated that both metabolites can bind the repressor, but the breach in the relative affinity of Cra(PP) for F1P vs FBP is three orders of magnitude larger than the equivalent distance in the Escherichia coli protein. This assigns the Cra protein of P. putida the sole role of transducing the presence of fructose in the medium into a variety of direct and indirect physiological responses.

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Anthropogenic halogenated compounds were unknown to nature until the industrial revolution, and microorganisms have not had sufficient time to evolve enzymes for their degradation. The lack of efficient enzymes and natural pathways can be addressed through a combination of protein and metabolic engineering. We have assembled a synthetic route for conversion of the highly toxic and recalcitrant 1,2,3-trichloropropane to glycerol in Escherichia coli, and used it for a systematic study of pathway bottlenecks. Optimal ratios of enzymes for the maximal production of glycerol, and minimal toxicity of metabolites were predicted using a mathematical model. The strains containing the expected optimal ratios of enzymes were constructed and characterized for their viability and degradation efficiency. Excellent agreement between predicted and experimental data was observed. The validated model was used to quantitatively describe the kinetic limitations of currently available enzyme variants and predict improvements required for further pathway optimization. This highlights the potential of forward engineering of microorganisms for the degradation of toxic anthropogenic compounds.

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such "smart" libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.

Haloalkane dehalogenases catalyze the hydrolysis of carbon-halogen bonds in various chlorinated, brominated and iodinated compounds. These enzymes have a conserved pair of halide-stabilizing residues that are important in substrate binding and stabilization of the transition state and the halide ion product via hydrogen bonding. In all previously known haloalkane dehalogenases, these residues are either a pair of tryptophans or a tryptophan-asparagine pair. The newly-isolated haloalkane dehalogenase DatA from Agrobacterium tumefaciens C58 (EC 3.8.1.5) possesses a unique halide-stabilizing tyrosine residue, Y109, in place of the conventional tryptophan. A variant of DatA with the Y109W mutation was created and the effects of this mutation on the structure and catalytic properties of the enzyme were studied using spectroscopy and pre-steady-state kinetic experiments. Quantum mechanical and molecular dynamics calculations were used to obtain a detailed analysis of the hydrogen-bonding patterns within the active sites of the wild-type and the mutant, as well as of the stabilization of the ligands as the reaction proceeds. Fluorescence quenching experiments suggested that replacing the tyrosine with tryptophan improves halide binding by 3.7-fold, presumably as a result of the introduction of an additional hydrogen bond. Kinetic analysis revealed that the mutation affected the substrate specificity of the enzyme and reduced its K(0.5) for selected halogenated substrates by a factor of 2-4, without impacting the rate-determining hydrolytic step. We conclude that DatA is the first natural haloalkane dehalogenase that stabilizes its substrate in the active site using only a single hydrogen bond, which is a new paradigm in catalysis by this enzyme family., (© 2013 The Authors Journal compilation © 2013 FEBS.)