Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.

The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods. Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system─collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the α-helix, we added the α-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics. This simulation led to 10 folding events in a 200 ns simulation with 32 replicas.

2,6-Disubstituted 7-(naphthalen-2-ylmethyl)-7H-purines as a new class of potent antitubercular agents inhibiting DprE1.

Phenotypic screening of an in-house library of small molecule purine derivatives against Mycobacterium tuberculosis (Mtb) led to the identification of 2-morpholino-7-(naphthalen-2-ylmethyl)-1,7-dihydro-6H-purin-6-one 10 as a potent antimycobacterial agent with MIC99 of 4 µM. Thorough structure-activity relationship studies revealed the importance of 7-(naphthalen-2-ylmethyl) substitution for antimycobacterial activity, yet opened the possibility of structural modifications at positions 2 and 6 of the purine core. As the result, optimized analogues with 6-amino or ethylamino substitution 56 and 64, respectively, were developed. These compounds showed strong in vitro antimycobacterial activity with MIC of 1 µM against Mtb H37Rv and against several clinically isolated drug-resistant strains, had limited toxicity to mammalian cell lines, medium clearance with respect to phase I metabolic deactivation (27 and 16.8 µL/min/mg), sufficient aqueous solubility (>90 µM) and high plasma stability. Interestingly, investigated purines, including compounds 56 and 64, lacked activity against a panel of Gram-negative and Gram-positive bacterial strains, indicating a specific mycobacterial molecular target. To investigate the mechanism of action, Mtb mutants resistant to hit compound 10 were isolated and their genomes were sequenced. Mutations were found in dprE1 (Rv3790), which encodes decaprenylphosphoryl-ß-d-ribose oxidase DprE1, enzyme essential for the biosynthesis of arabinose, a vital component of the mycobacterial cell wall. Inhibition of DprE1 by 2,6-disubstituted 7-(naphthalen-2-ylmethyl)-7H-purines was proved using radiolabelling experiments in Mtb H37Rv in vitro. Finally, structure-binding relationships between selected purines and DprE1 using molecular modeling studies in tandem with molecular dynamic simulations revealed the key structural features for effective drug-target interaction.

Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts.

Here, we demonstrate a method to estimate the uncertainty (confidence intervals and standard errors) of free energy differences calculated by molecular simulations. The widths of confidence intervals and standard errors can be calculated solely from temperature and the number of transitions between states. Uncertainty (95% confidence interval) lower than ±1 kcal/mol can be achieved by a simulation with four forward and four reverse transitions. For a two-state Markovian system, the confidence interval is exact, regardless the number of transitions.

Cytotoxicity and Antitumor Activity of Arglabin and its Derivatives.

BACKGROUND: At present, more than 8000 sesquiterpene lactones have been isolated and described from natural sources, a significant part of which has cytotoxicity and antitumor activity. One of the practically available sesquiterpene lactones is arglabin, which, as a renewable material, is used for the synthesis of new compounds. The article presents data on the study of cytotoxicity and antitumor activity of the arglabin and its derivatives using molecular modeling methods and, in the experiment in vitro and in vivo. AIM: The aim of this work is to study the cytotoxicity and antitumor activity of new compounds based on the sesquiterpene lactone arglabin using molecular modeling and experimental pharmacology. METHODS: ChemDraw programs and a set of AutoDock programs were used for computer simulation. Molecular docking was carried out using the Maestro graphical interface of the Schrödinger Suite software package (Schrödinger, LLC, New York, NY, 2017). Docking modes standard precision and XP (extra precision) were used. In in vitro experiments, the antitumor activity of compound samples was studied in models of 60 human tumor cell lines, and clonogenic C6 rat glioma cells. The antitumor activity of the samples was studied in experiments in vivo on white outbred rats with transplanted tumors and was evaluated by the inhibition of tumor growth and the magnitude of the increase in average life expectancy. CONCLUSION: When studying the antitumor activity on 60 cell lines of tumor cells (NCI60), clonogenic cells of C6 rat glioma, a high antitumor activity of some arglabin derivatives was established. The connection between the structure of arglabin derivatives and their inhibitory effect on farnesyl protein transferase, topoisomerases -I and -II was studied.

Collective Variable for Metadynamics Derived From AlphaFold Output.

AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and ß hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.

The first structure-function study of GH151 α-l-fucosidase uncovers new oligomerization pattern, active site complementation, and selective substrate specificity.

Fucosylated compounds are abundantly present in nature and are associated with many biological processes, therefore carrying great potential for use in medicine and biotechnology. Efficient ways to modify fucosylated compounds are still being developed. Promising results are provided by glycosyl hydrolases with transglycosylating activities, such as α-l-fucosidase isoenzyme 2 from Paenibacillus thiaminolyticus (family GH151 of Carbohydrate-Active enZYmes). Currently, there is no 3D structure representing this glycoside hydrolase family and only a few members have been investigated. Here, we present the first structure-function study of a GH151 member, providing the key insights into its specific oligomerization and active site properties. According to the crystal structure, small-angle X-ray scattering data and catalytic investigation, this enzyme functions as a tetramer of a new type and represents the second known case of active site complementation among all α-l-fucosidases. Mutation of the active site-complementing residue histidine 503 to alanine confirmed its influence on α-l-fucosidase activity and, specifically, on substrate binding. Several unique features of GH151 family α-l-fucosidases were revealed, including the oligomerization pattern, active site accessibility and complementation, and substrate selectivity. Some common properties of GH151 glycosyl hydrolases then would be the overall three-domain structure and conservation of the central domain loop 2 function, including its complementation role and the formation of the carbohydrate-binding platform in the active site vicinity.

Property Map Collective Variable as a Useful Tool for a Force Field Correction.

The accuracy of biomolecular simulations depends on the accuracy of an empirical molecular mechanics potential known as a force field: a set of parameters and expressions to estimate the potential from atomic coordinates. Accurate parametrization of force fields for small organic molecules is a challenge due to their high diversity. One of the possible approaches is to apply a correction to the existing force fields. Here, we propose an approach to estimate the density functional theory (DFT)-derived force field correction which is calculated during the run of molecular dynamics without significantly affecting its speed. Using the formula known as a property map collective variable, we approximate the force field correction by a weighted average of this force field correction calculated only for a small series of reference structures. To validate this method, we used seven AMBER force fields, and we show how it is possible to convert one force field to behave like the other one. We also present the force field correction for the important anticancer drug Imatinib as a use case example. Our method appears to be suitable for adjusting the force field for general drug-like molecules. We provide a pipeline that generates the correction; this pipeline is available at https://pmcvff-correction.cerit-sc.cz/.

Structural Basis of the Function of Yariv Reagent-An Important Tool to Study Arabinogalactan Proteins.

Arabinogalactan proteins are very abundant, heavily glycosylated plant cell wall proteins. They are intensively studied because of their crucial role in plant development as well as their function in plant defence. Research of these biomacromolecules is complicated by the lack of tools for their analysis and characterisation due to their extreme heterogeneity. One of the few available tools for detection, isolation, characterisation, and functional studies of arabinogalactan proteins is Yariv reagents. Yariv reagent is a synthetic aromatic glycoconjugate originally prepared as an antigen for immunization. Later, it was found that this compound can precipitate arabinogalactan proteins, namely, their ß-D-(1→3)-galactan structures. Even though this compound has been intensively used for decades, the structural basis of arabinogalactan protein precipitation by Yariv is not known. Multiple biophysical studies have been published, but none of them attempted to elucidate the three-dimensional structure of the Yariv-galactan complex. Here we use a series of molecular dynamics simulations of systems containing one or multiple molecules of ß-D-galactosyl Yariv reagent with or without oligo ß-D-(1→3)-galactan to predict the structure of the complex. According to our model of Yariv-galactan complexes, Yariv reagent forms stacked oligomers stabilized by π-π and CH/π interactions. These oligomers may contain irregularities. Galactan structures crosslink these Yariv oligomers. The results were compared with studies in literature.

Quo vadis Cardiac Glycoside Research?

Cardiac glycosides (CGs), toxins well-known for numerous human and cattle poisoning, are natural compounds, the biosynthesis of which occurs in various plants and animals as a self-protective mechanism to prevent grazing and predation. Interestingly, some insect species can take advantage of the CG's toxicity and by absorbing them, they are also protected from predation. The mechanism of action of CG's toxicity is inhibition of Na+/K+-ATPase (the sodium-potassium pump, NKA), which disrupts the ionic homeostasis leading to elevated Ca2+ concentration resulting in cell death. Thus, NKA serves as a molecular target for CGs (although it is not the only one) and even though CGs are toxic for humans and some animals, they can also be used as remedies for various diseases, such as cardiovascular ones, and possibly cancer. Although the anticancer mechanism of CGs has not been fully elucidated, yet, it is thought to be connected with the second role of NKA being a receptor that can induce several cell signaling cascades and even serve as a growth factor and, thus, inhibit cancer cell proliferation at low nontoxic concentrations. These growth inhibitory effects are often observed only in cancer cells, thereby, offering a possibility for CGs to be repositioned for cancer treatment serving not only as chemotherapeutic agents but also as immunogenic cell death triggers. Therefore, here, we report on CG's chemical structures, production optimization, and biological activity with possible use in cancer therapy, as well as, discuss their antiviral potential which was discovered quite recently. Special attention has been devoted to digitoxin, digoxin, and ouabain.

Na+/K+-ATPase Revisited: On Its Mechanism of Action, Role in Cancer, and Activity Modulation.

Maintenance of Na+ and K+ gradients across the cell plasma membrane is an essential process for mammalian cell survival. An enzyme responsible for this process, sodium-potassium ATPase (NKA), has been currently extensively studied as a potential anticancer target, especially in lung cancer and glioblastoma. To date, many NKA inhibitors, mainly of natural origin from the family of cardiac steroids (CSs), have been reported and extensively studied. Interestingly, upon CS binding to NKA at nontoxic doses, the role of NKA as a receptor is activated and intracellular signaling is triggered, upon which cancer cell death occurs, which lies in the expression of different NKA isoforms than in healthy cells. Two major CSs, digoxin and digitoxin, originally used for the treatment of cardiac arrhythmias, are also being tested for another indication-cancer. Such drug repositioning has a big advantage in smoother approval processes. Besides this, novel CS derivatives with improved performance are being developed and evaluated in combination therapy. This article deals with the NKA structure, mechanism of action, activity modulation, and its most important inhibitors, some of which could serve not only as a powerful tool to combat cancer, but also help to decipher the so-far poorly understood NKA regulation.

Transglycosylation abilities of ß-d-galactosidases from GH family 2.

The ability to predict the transglycosylation activity of glycosidases by in silico analysis was investigated. The transglycosylation abilities of 7 different ß-d-galactosidases from GH family 2 were tested experimentally using 7 different acceptors and p-nitrophenyl-ß-d-galactopyranoside as a donor of galactosyl moiety. Similar transglycosylation abilities were confirmed for all enzymes originating from bacteria belonging to Enterobacteriaceae, which were able to use all tested acceptor molecules. Higher acceptor selectivity was observed for all others used bacterial strains. Structure models of all enzymes were constructed using homology modeling. Ligand-docking method was used for enzymes-transglycosylation products models construction and evaluation. Results obtained by in silico analysis were compared with results arisen out of experimental testing. The experiments confirmed that significant differences in transglycosylation abilities are caused by small differences in active sites composition of analyzed enzymes. According to obtained result, it is possible to conclude that homology modeling may serve as a quick starting point for detection or exclusion of enzymes with defined transglycosylation abilities, which can be used for subsequent synthesis of e.g., pharmaceutically interesting glycosides. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02715-w.

An Early Stage Researcher's Primer on Systems Medicine Terminology.

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Deep learning in systems medicine.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Arabinogalactan-like Glycoproteins from Ulva lactuca (Chlorophyta) Show Unique Features Compared to Land Plants AGPs.

Arabinogalactan proteins (AGPs) encompass a diverse group of plant cell wall proteoglycans, which play an essential role in plant development, signaling, plant-microbe interactions, and many others. Although they are widely distributed throughout the plant kingdom and extensively studied, they remain largely unexplored in the lower plants, especially in seaweeds. Ulva species have high economic potential since various applications were previously described including bioremediation, biofuel production, and as a source of bioactive compounds. This article presents the first experimental confirmation of AGP-like glycoproteins in Ulva species and provides a simple extraction protocol of Ulva lactuca AGP-like glycoproteins, their partial characterization and unique comparison to scarcely described Solanum lycopersicum AGPs. The reactivity with primary anti-AGP antibodies as well as Yariv reagent showed a great variety between Ulva lactuca and Solanum lycopersicum AGP-like glycoproteins. While the amino acid analysis of the AGP-like glycoproteins purified by the ß-d-glucosyl Yariv reagent showed a similarity between algal and land plant AGP-like glycoproteins, neutral saccharide analysis revealed unique glycosylation of the Ulva lactuca AGP-like glycoproteins. Surprisingly, arabinose and galactose were not the most prevalent monosaccharides and the most outstanding was the presence of 3-O-methyl-hexose, which has never been described in the AGPs. The exceptional structure of the Ulva lactuca AGP-like glycoproteins implies a specialized adaptation to the marine environment and might bring new insight into the evolution of the plant cell wall.

Time-Lagged t-Distributed Stochastic Neighbor Embedding (t-SNE) of Molecular Simulation Trajectories.

Molecular simulation trajectories represent high-dimensional data. Such data can be visualized by methods of dimensionality reduction. Non-linear dimensionality reduction methods are likely to be more efficient than linear ones due to the fact that motions of atoms are non-linear. Here we test a popular non-linear t-distributed Stochastic Neighbor Embedding (t-SNE) method on analysis of trajectories of 200 ns alanine dipeptide dynamics and 208 µs Trp-cage folding and unfolding. Furthermore, we introduce a time-lagged variant of t-SNE in order to focus on rarely occurring transitions in the molecular system. This time-lagged t-SNE efficiently separates states according to distance in time. Using this method it is possible to visualize key states of studied systems (e.g., unfolded and folded protein) as well as possible kinetic traps using a two-dimensional plot. Time-lagged t-SNE is a visualization method and other applications, such as clustering and free energy modeling, must be done with caution.

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

Regioselective 3-O-Substitution of Unprotected Thiodigalactosides: Direct Route to Galectin Inhibitors.

The synthesis of tailored bioactive carbohydrates usually comprises challenging (de)protection steps, which lowers synthetic yields and increases time demands. We present here a regioselective single-step introduction of benzylic substituents at 3-hydroxy groups of ß-d-galactopyranosyl-(1→1)-thio-ß-d-galactopyranoside (TDG) employing dibutyltin oxide in good yields. These glycomimetics act as inhibitors of galectins-human lectins, which are biomedically attractive targets for therapeutic inhibition in, for example, cancerogenesis. The affinity of the prepared glycomimetics to galectin-1 and galectin-3 was studied in enzyme-linked immunosorbent (ELISA)-type assays and their potential to inhibit galectin binding on the cell surface was shown. We used our original in vivo biotinylated galectin constructs for easy detection by flow cytometry. The results of the biological experiments were compared with data from molecular modeling with both galectins. The present work reveals a facile and elegant synthetic route for the preparation of TDG-derived glycomimetics that exhibit differing selectivity and affinity to galectins depending on the choice of 3-O-substitution.

PEGylated Purpurin 18 with Improved Solubility: Potent Compounds for Photodynamic Therapy of Cancer.

Purpurin 18 derivatives with a polyethylene glycol (PEG) linker were synthesized as novel photosensitizers (PSs) with the goal of using them in photodynamic therapy (PDT) for cancer. These compounds, derived from a second-generation PS, exhibit absorption at long wavelengths; considerable singlet oxygen generation and, in contrast to purpurin 18, have higher hydrophilicity due to decreased logP. Together, these properties make them potentially ideal PSs. To verify this, we screened the developed compounds for cell uptake, intracellular localization, antitumor activity and induced cell death type. All of the tested compounds were taken up into cancer cells of various origin and localized in organelles known to be important PDT targets, specifically, mitochondria and the endoplasmic reticulum. The incorporation of a zinc ion and PEGylation significantly enhanced the photosensitizing efficacy, decreasing IC50 (half maximal inhibitory compound concentration) in HeLa cells by up to 170 times compared with the parental purpurin 18. At effective PDT concentrations, the predominant type of induced cell death was apoptosis. Overall, our results show that the PEGylated derivatives presented have significant potential as novel PSs with substantially augmented phototoxicity for application in the PDT of cervical, prostate, pancreatic and breast cancer.

Atomistic simulation of carbohydrate-protein complex formation: Hevein-32 domain.

Interactions between proteins and their small molecule ligands are of great importance for the process of drug design. Here we report an unbiased molecular dynamics simulation of systems containing hevein domain (HEV32) with N-acetylglucosamine mono-, di- or trisaccharide. Carbohydrate molecules were placed outside the binding site. Three of six simulations (6 × 2 µs) led to binding of a carbohydrate ligand into the binding mode in agreement with the experimentally determined structure. Unbinding was observed in one simulation (monosaccharide). There were no remarkable intermediates of binding for mono and disaccharide. Trisaccharide binding was initiated by formation of carbohydrate-aromatic CH/π interactions. Our results indicate that binding of ligands followed the model of conformational selection because the conformation of the protein ready for ligand binding was observed before the binding. This study extends the concept of docking by dynamics on carbohydrate-protein interactions.

Archangelolide: A sesquiterpene lactone with immunobiological potential from Laserpitium archangelica.

Sesquiterpene lactones are secondary plant metabolites with sundry biological effects. In plants, they are synthesized, among others, for pesticidal and antimicrobial effects. Two such compounds, archangelolide and trilobolide of the guaianolide type, are structurally similar to the well-known and clinically tested lactone thapsigargin. While trilobolide has already been studied by us and others, there are only scarce reports on the biological activity of archangelolide. Here we present the preparation of its fluorescent derivative based on a dansyl moiety using azide-alkyne Huisgen cycloaddition having obtained the two sesquiterpene lactones from the seeds of Laserpitium archangelica Wulfen using supercritical CO2 extraction. We show that dansyl-archangelolide localizes in the endoplasmic reticulum of living cells similarly to trilobolide; localization in mitochondria was also detected. This led us to a more detailed study of the anticancer potential of archangelolide. Interestingly, we found that neither archangelolide nor its dansyl conjugate did exhibit cytotoxic effects in contrast to the structurally closely related counterparts trilobolide and thapsigargin. We explain this observation by a molecular dynamics simulation, in which, in contrast to trilobolide, archangelolide did not bind into the sarco/endoplasmic reticular calcium ATPase cavity utilized by thapsigargin. Last, but not least, archangelolide exhibited anti-inflammatory activity, which makes it promising compound for medicinal purposes.