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.

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.

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.

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.

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.

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.

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.

Screening of world approved drugs against highly dynamical spike glycoprotein of SARS-CoV-2 using CaverDock and machine learning.

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer's three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community.

Impacts of wildfires in aquatic organisms: biomarker responses and erythrocyte nuclear abnormalities in Gambusia holbrooki exposed in situ.

Wildfires are an environmental concern due to the loss of forest area and biodiversity, but also because their role as drivers of freshwater systems contamination by metals. In this context, the fish Gambusia holbrooki was used as a model, deployed for in situ exposure in watercourses standing within a recently burnt area and further assessment of toxic effects. The fish were exposed during 4 days at four different sites: one upstream and another downstream the burnt area and two within the burnt area. Biochemical biomarkers for oxidative stress and damage were assessed. The extent of lipoperoxidative damage was monitored by quantifying malondialdehyde and DNA damage evaluated through erythrocyte nuclear abnormalities observation. Chemical analysis revealed higher metal levels within the burnt area, and exposed fish consistently showed pro-oxidative responses therein, particularly an increase of gill glutathione peroxidase and glutathione reductase activity, the records doubling compared to samples from sites in the unburnt area; also the activity of glutathione-S-transferases comparatively increased (by 2-fold in the liver) in samples from the burnt area, and malondialdehyde was produced twice as much therein and in samples downstream the burnt area reflecting oxidative damage. Consistently, the frequency of erythrocyte nuclear abnormalities was higher at sites within and downstream the burnt area. This study supports the use of sensitive oxidative stress and genotoxicity biomarkers for an early detection of potentially noxious ecological effects of wildfires runoff.

Screening of Natural Compounds as P-Glycoprotein Inhibitors against Multidrug Resistance.

Multidrug resistance (MDR) is a common problem when fighting cancer with chemotherapy. P-glycoprotein (P-gp, or MDR1) is an active pump responsible for the efflux of xenobiotics out of the cell, including anti-cancer drugs. It is a validated target against MDR. No crystal structure of the human P-gp is available to date, and only recently several cryo-EM structures have been solved. In this paper, we present a comprehensive computational approach that includes constructing the full-length three-dimensional structure of the human P-gp and its refinement using molecular dynamics. We assessed its flexibility and conformational diversity, compiling a dynamical ensemble that was used to dock a set of lignan compounds, previously reported as active P-gp inhibitors, and disclose their binding modes. Based on the statistical analysis of the docking results, we selected a system for performing the structure-based virtual screening of new potential P-gp inhibitors. We tested the method on a library of 87 natural flavonoids described in the literature, and 10 of those were experimentally assayed. The results reproduced the theoretical predictions only partially due to various possible factors. However, at least two of the predicted natural flavonoids were demonstrated to be effective P-gp inhibitors. They were able to increase the accumulation of doxorubicin inside the human promyelocytic leukemia HL60/MDR cells overexpressing P-gp and potentiate the antiproliferative activity of this anti-cancer drug.

Web-based tools for computational enzyme design.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Computational design of enzymes for biotechnological applications.

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

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst.

Computational design of protein catalysts with enhanced stabilities for use in research and enzyme technologies is a challenging task. Using force-field calculations and phylogenetic analysis, we previously designed the haloalkane dehalogenase DhaA115 which contains 11 mutations that confer upon it outstanding thermostability (T m = 73.5 °C; ΔT m > 23 °C). An understanding of the structural basis of this hyperstabilization is required in order to develop computer algorithms and predictive tools. Here, we report X-ray structures of DhaA115 at 1.55 Å and 1.6 Å resolutions and their molecular dynamics trajectories, which unravel the intricate network of interactions that reinforce the αßα-sandwich architecture. Unexpectedly, mutations toward bulky aromatic amino acids at the protein surface triggered long-distance (∼27 Å) backbone changes due to cooperative effects. These cooperative interactions produced an unprecedented double-lock system that: (i) induced backbone changes, (ii) closed the molecular gates to the active site, (iii) reduced the volumes of the main and slot access tunnels, and (iv) occluded the active site. Despite these spatial restrictions, experimental tracing of the access tunnels using krypton derivative crystals demonstrates that transport of ligands is still effective. Our findings highlight key thermostabilization effects and provide a structural basis for designing new thermostable protein catalysts.

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/.

Copper sulphate impact on the antioxidant defence system of the marine bivalves Cerastoderma edule and Scrobicularia plana.

Anthropogenic activities, such as agriculture and industrial activities, are a main source of pollution contributing for the degradation of water quality and thus affecting the living organisms of aquatic systems. Copper is widely used at these practices being often released into the aquatic systems and may cause negative effects in its communities. This study proposes to determine the effects of copper in the antioxidant defence system of two size classes (big and small sizes) of Scrobicularia plana and Cerastoderma edule, two marine bivalve species with commercial interest. It was observed the behaviour activity of the organisms during the exposure to copper sulphate (CS) and was determined the enzymatic activities of glutathione-S-transferases (GST), glutathione reductase (GR) and glutathione peroxidase (GPx) (both selenium-dependent (SeGPx) and total (tGPx)) in the muscle tissue (foot). Lipid peroxidation (LPO) was evaluated through thiobarbituric acid reactive substances (TBARS) measurement in the foot. Changes in the behaviour and enzymatic activity were observed. Lipid peroxidation was observed at C. edule and S. plana big and small size classes, respectively, according to TBARS levels. The foot showed to be a good tissue to be used in biochemical analysis to detect the presence of toxicants.

NMDA Receptor Opening and Closing-Transitions of a Molecular Machine Revealed by Molecular Dynamics.

We report the first complete description of the molecular mechanisms behind the transition of the N-methyl-d-aspartate (NMDA) receptor from the state where the transmembrane domain (TMD) and the ion channel are in the open configuration to the relaxed unliganded state where the channel is closed. Using an aggregate of nearly 1 µs of unbiased all-atom implicit membrane and solvent molecular dynamics (MD) simulations we identified distinct structural states of the NMDA receptor and revealed functionally important residues (GluN1/Glu522, GluN1/Arg695, and GluN2B/Asp786). The role of the "clamshell" motion of the ligand binding domain (LBD) lobes in the structural transition is supplemented by the observed structural similarity at the level of protein domains during the structural transition, combined with the overall large rearrangement necessary for the opening and closing of the receptor. The activated and open states of the receptor are structurally similar to the liganded crystal structure, while in the unliganded receptor the extracellular domains perform rearrangements leading to a clockwise rotation of up to 45 degrees around the longitudinal axis of the receptor, which closes the ion channel. The ligand-induced rotation of extracellular domains transferred by LBD-TMD linkers to the membrane-anchored ion channel is responsible for the opening and closing of the transmembrane ion channel, revealing the properties of NMDA receptor as a finely tuned molecular machine.

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.

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.