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 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.

Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems.

Freshwater ecosystems are amongst the most threatened ecosystems on Earth. Currently, climate change is one of the most important drivers of freshwater transformation and its effects include changes in the composition, biodiversity and functioning of freshwater ecosystems. Understanding the capacity of freshwater species to tolerate the environmental fluctuations induced by climate change is critical to the development of effective conservation strategies. In the last few years, epigenetic mechanisms were increasingly put forward in this context because of their pivotal role in gene-environment interactions. In addition, the evolutionary role of epigenetically inherited phenotypes is a relatively recent but promising field. Here, we examine and synthesize the impacts of climate change on freshwater ecosystems, exploring the potential role of epigenetic mechanisms in both short- and long-term adaptation of species. Following this wrapping-up of current evidence, we particularly focused on bringing together the most promising future research avenues towards a better understanding of the effects of climate change on freshwater biodiversity, specifically highlighting potential molecular targets and the most suitable freshwater species for future epigenetic studies in this context.

Transgenerational Inheritance of DNA Hypomethylation in Daphnia magna in Response to Salinity Stress.

Epigenetic mechanisms have been found to play important roles in environmental stress response and regulation. These can, theoretically, be transmitted to future unexposed generations, yet few studies have shown persisting stress-induced transgenerational effects, particularly in invertebrates. Here, we focus on the aquatic microcrustacean Daphnia, a parthenogenetic model species, and its response to salinity stress. Salinity is a serious threat to freshwater ecosystems and a relevant form of environmental perturbation affecting freshwater ecosystems. We exposed one generation of D. magna to high levels of salinity (F0) and found that the exposure provoked specific methylation patterns that were transferred to the three consequent nonexposed generations (F1, F2, and F3). This was the case for the hypomethylation of six protein-coding genes with important roles in the organisms' response to environmental change: DNA damage repair, cytoskeleton organization, and protein synthesis. This suggests that epigenetic changes in Daphnia are particularly targeted to genes involved in coping with general cellular stress responses. Our results highlight that epigenetic marks are affected by environmental stressors and can be transferred to subsequent unexposed generations. Epigenetic marks could therefore prove to be useful indicators of past or historic pollution in this parthenogenetic model system. Furthermore, no life history costs seem to be associated with the maintenance of hypomethylation across unexposed generations in Daphnia following a single stress exposure.

Effects of a herbicide and copper mixture on the quality of marine plankton.

Pesticides and metals are often used in agriculture and are therefore often simultaneously discharged to nearby estuarine and marine areas. The effects of this organic-inorganic chemical mixture on food quality of aquatic organisms are currently unknown. In this study we test if a mixture of copper (inorganic) and the herbicide Primextra® Gold TZ (organic) affects the quality of the diatom Thalassiosira weissflogii and the copepod Acartia tonsa - two key species that fuel the local food-web. We quantified quality (i.e. energy content as food for the next trophic level) in terms of fatty acids, proteins and thiobarbituric acid reacting substances. We found non-additive effects (positive and negative) of the metal-herbicide mixture on the diatom and copepod species. In general, nutritionally important biochemical parameters of Acartia tonsa were most sensitive to the chemical stressors.

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.

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.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules.

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aß42 peptide involved in Alzheimer's disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aß42 by interacting nonspecifically with charged residues. SPA impacted Aß42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone ß-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aß42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target biomolecules other than Aß peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules.

Design, synthesis and neuroprotective evaluation of novel tacrine-benzothiazole hybrids as multi-targeted compounds against Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial disorder with several target proteins contributing to its etiology. In search for multifunctional anti-AD drug candidates, taking into account that the acetylcholinesterase (AChE) and beta-amyloid (Aß) aggregation are particularly important targets for inhibition, the tacrine and benzothiazole (BTA) moieties were conjugated with suitable linkers in a novel series of hybrids. The designed compounds (7a-7e) were synthesized and in vitro as well as in ex vivo evaluated for their capacity for the inhibition of acetylcholinesterase (AChE) and Aß self-induced aggregation, and also for the protection of neuronal cells death (SHSY-5Y cells, AD and MCI cybrids). All the tacrine-BTA hybrids displayed high in vitro activities, namely with IC50 values in the low micromolar to sub-micromolar concentration range towards the inhibition of AChE, and high percentages of inhibition of the self-induced Aß aggregation. Among them, compound 7a, with the shortest linker, presented the best inhibitory activity of AChE (IC50=0.34 µM), while the highest activity as anti-Aß42 self-aggregation, was evidenced for compound 7b (61.3%, at 50µM. The docking studies demonstrated that all compounds are able to interact with both catalytic active site (CAS) and peripheral anionic site (PAS) of AChE. Our results show that compounds 7d and 7e improved cell viability in cells treated with Aß42 peptide. Overall, these multi-targeted hybrid compounds appear as promising lead compounds for the treatment of Alzheimer's disease.

Differential gene expression in Iberian green frogs (Pelophylax perezi) inhabiting a deactivated uranium mine.

Iberian green frogs (Pelophylax perezi) were found inhabiting a deactivated uranium mine, especially an effluent pond, seriously contaminated with metals and radionuclides. These animals were previously assessed for oxidative stress parameters and did not revealed significant alterations. In order to better understand which mechanisms may be involved in the ability to withstand permanent contamination gene expression analysis was performed in the liver, through suppression subtractive hybridization (SSH). The SSH outcome in the liver revealed the up-regulation of genes coding for the ribosomal protein L7a and for several proteins typical from blood plasma: fibrinogen, hemoglobin and albumin. Besides their normal function, some of these proteins can play an important role as protective agents against oxidative stress. This work provides new insights on possible basal protection mechanisms that may act in organisms exposed chronically to contamination.

Atypical homodimerization revealed by the structure of the (S)-enantioselective haloalkane dehalogenase DmmarA from Mycobacterium marinum.

Haloalkane dehalogenases (HLDs) are a family of α/ß-hydrolase fold enzymes that employ SN2 nucleophilic substitution to cleave the carbon-halogen bond in diverse chemical structures, the biological role of which is still poorly understood. Atomic-level knowledge of both the inner organization and supramolecular complexation of HLDs is thus crucial to understand their catalytic and noncatalytic functions. Here, crystallographic structures of the (S)-enantioselective haloalkane dehalogenase DmmarA from the waterborne pathogenic microbe Mycobacterium marinum were determined at 1.6 and 1.85 Šresolution. The structures show a canonical αßα-sandwich HLD fold with several unusual structural features. Mechanistically, the atypical composition of the proton-relay catalytic triad (aspartate-histidine-aspartate) and uncommon active-site pocket reveal the molecular specificities of a catalytic apparatus that exhibits a rare (S)-enantiopreference. Additionally, the structures reveal a previously unobserved mode of symmetric homodimerization, which is predominantly mediated through unusual L5-to-L5 loop interactions. This homodimeric association in solution is confirmed experimentally by data obtained from small-angle X-ray scattering. Utilizing the newly determined structures of DmmarA, molecular modelling techniques were employed to elucidate the underlying mechanism behind its uncommon enantioselectivity. The (S)-preference can be attributed to the presence of a distinct binding pocket and variance in the activation barrier for nucleophilic substitution.

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration.

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

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning.

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

Domino-like effect of C112R mutation on ApoE4 aggregation and its reduction by Alzheimer's Disease drug candidate.

BACKGROUND: Apolipoprotein E (ApoE) ε4 genotype is the most prevalent risk factor for late-onset Alzheimer's Disease (AD). Although ApoE4 differs from its non-pathological ApoE3 isoform only by the C112R mutation, the molecular mechanism of its proteinopathy is unknown. METHODS: Here, we reveal the molecular mechanism of ApoE4 aggregation using a combination of experimental and computational techniques, including X-ray crystallography, site-directed mutagenesis, hydrogen-deuterium mass spectrometry (HDX-MS), static light scattering and molecular dynamics simulations. Treatment of ApoE ε3/ε3 and ε4/ε4 cerebral organoids with tramiprosate was used to compare the effect of tramiprosate on ApoE4 aggregation at the cellular level. RESULTS: We found that C112R substitution in ApoE4 induces long-distance (> 15 Å) conformational changes leading to the formation of a V-shaped dimeric unit that is geometrically different and more aggregation-prone than the ApoE3 structure. AD drug candidate tramiprosate and its metabolite 3-sulfopropanoic acid induce ApoE3-like conformational behavior in ApoE4 and reduce its aggregation propensity. Analysis of ApoE ε4/ε4 cerebral organoids treated with tramiprosate revealed its effect on cholesteryl esters, the storage products of excess cholesterol. CONCLUSIONS: Our results connect the ApoE4 structure with its aggregation propensity, providing a new druggable target for neurodegeneration and ageing.

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase.

NanoLuc, a superior ß-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. Here experimental and computational techniques are combined, revealing that imidazopyrazinone luciferins bind to an intra-barrel catalytic site but also to an allosteric site shaped on the enzyme surface. Structurally, binding to the allosteric site prevents simultaneous binding to the catalytic site, and vice versa, through concerted conformational changes. We demonstrate that restructuration of the allosteric site can boost the luminescent reaction in the remote active site. Mechanistically, an intra-barrel arginine coordinates the imidazopyrazinone component of luciferin, which reacts with O2 via a radical charge-transfer mechanism, and then it also protonates the resulting excited amide product to form a light-emitting neutral species. Concomitantly, an aspartate, supported by two tyrosines, fine-tunes the blue color emitter to secure a high emission intensity. This information is critical to engineering the next-generation of ultrasensitive bioluminescent reporters.

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling.

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

Tools for computational design and high-throughput screening of therapeutic enzymes.

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Effects of Eucalypt ashes from moderate and high severity wildfires on the skin microbiome of the Iberian frog (Rana iberica).

Forest fires can threaten amphibians because ash-associated contaminants transported by post-fire runoff impact both terrestrial and aquatic ecosystems. Still, the effects of these contaminants on the skin microbiome of amphibians have been overlooked. Thus, the main objective of this study was to assess the effects of ash from different severity wildfires (moderate and high) on the skin microbiome of the Iberian frog (Rana iberica). Bacterial isolates sampled from R. iberica skin microbiome were tested for their antimicrobial activity against the pathogen Aeromonas salmonicida. The isolates with antimicrobial activity were identified and further exposed to several concentrations (0, 6.25, 12.5, 25, 50, 75, and 100%) of Eucalypt (Eucalyptus globulus) aqueous extracts (AAEs) of ash from both a moderate and a high severity wildfire. The results showed that 53% of the bacterial isolates presented antimicrobial activity, with Pseudomonas being the most common genus. Exposure to AAEs had diverse effects on bacterial growth since a decrease, an increase or no effects on growth were observed. For both ash types, increasing AAEs concentrations led to an increase in the number of bacteria whose growth was negatively affected. Ash from the high severity fire showed more adverse effects on bacterial growth than those from moderate severity, likely due to the higher metal concentrations of the former. This study revealed that bacteria living in Iberian frogs' skin could be impaired by ash-related contaminants, potentially weakening the individual's immune system. Given the foreseen increase in wildfires' frequency and severity under climate change, this work raises awareness of the risks faced by amphibian communities in fire-prone regions, emphasising the importance of a rapid implementation of post-fire emergency measures for the preservation and conservation of this group of animals.

Bifunctional phenolic-choline conjugates as anti-oxidants and acetylcholinesterase inhibitors.

Because of the complex cascade of molecular events that can occur in the brain of an Alzheimer's disease (AD) patient, the therapy of this neurodegenerative disease seems more likely to be achieved by multifunctional drugs. Herein, a new series of dual-targeting ligands have been developed and in vitro bioevaluated. Their architecture is based on conjugating the acetylcholinesterase inhibition and anti-oxidant properties in one molecular entity. Specifically, a series of naturally occurring phenolic acids with recognized anti-oxidant properties (derivatives of caffeic acid, rosmarinic acid, and trolox) have been conjugated with choline to account for the recognition by acetylcholinesterase (AChE). The synthesized hybrid compounds evidenced AChE inhibitory capacity of micromolar range (rationalized by molecular modeling studies) and good antioxidant properties. Their effects on human neuroblastoma cells, previously treated with beta-amyloid peptides and 1-methyl-4-phenylpyridinium ion neurotoxins (to simulate AD and Parkinson's disease, respectively), also demonstrated a considerable capacity for protection against the cytotoxicity of these stressors.