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The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a K D lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Motivation: Molecular dynamics simulation is very useful but computationally demanding method of studying dynamics of biomolecular systems. Many enhanced sampling methods were developed in order to obtain the desired results in available computational time. Metadynamics and its variants are common enhanced sampling methods used for this purpose. Metadynamics simulations allow the user to gather large amounts of data, which have to be analyzed to elucidate the properties of the studied system., Results: Here, we present metadynminer.py, a Python package that allows easy and user-friendly analysis and visualization of the results obtained from metadynamics simulations. The built-in functions automate frequent tasks and make the package easy to use for new users, while its many customization options and object-oriented nature allow for integration into specialized data analysis workflows by more advanced users., Availability and Implementation: The "metadynminer.py" Python package is available under the GPL-3.0 license via PyPi and Conda. The development version is available on GitHub along with issue support (https://github.com/Jan8be/metadynminer.py). Documentation, tutorial and Jupyter Notebook (provided through the public mybinder.org service) are available at https://metadynreporter.cz., (© The Author(s) 2024. Published by Oxford University Press.)

The use of Fpocket and virtual screening techniques enabled us to identify potential allosteric druggable pockets within the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Of the compounds screened, compound 1 was identified as a promising inhibitor, lowering a SARS-CoV-2 RdRp activity to 57 % in an enzymatic assay at 10 μM concentration. The structure of compound 1 was subsequently optimized in order to preserve or enhance inhibitory activity. This involved the substitution of problematic ester and aromatic nitro groups with more inert functionalities. The N,N'-diphenylurea scaffold with two NH groups was identified as essential for the compound's activity but also exhibited high toxicity in Calu-3 cells. To address this issue, a scaffold hopping approach was employed to replace the urea core with potentially less toxic urea isosteres. This approach yielded several structural analogues with notable activity, specifically 2,2'-bisimidazol (in compound 55 with residual activity RA=42 %) and (1H-imidazol-2-yl)urea (in compounds 59 and 60, with RA=50 and 28 %, respectively). Despite these advances, toxicity remained a major concern. These compounds represent a promising starting point for further structure-activity relationship studies of allosteric inhibitors of SARS-CoV-2 RdRp, with the goal of reducing their cytotoxicity and improving aqueous solubility., (© 2024 The Authors. ChemMedChem published by Wiley-VCH GmbH.)

Fluorination of carbohydrate ligands of lectins is a useful approach to examine their binding profile, improve their metabolic stability and lipophilicity, and convert them into 19 F NMR-active probes. However, monofluorination of monovalent carbohydrate ligands often leads to a decreased or completely lost affinity. By chemical glycosylation, we synthesized the full series of methyl β-glycosides of N,N'-diacetylchitobiose (GlcNAcβ(1-4)GlcNAcβ1-OMe) and LacdiNAc (GalNAcβ(1-4)GlcNAcβ1-OMe) systematically monofluorinated at all hydroxyl positions. A competitive enzyme-linked lectin assay revealed that the fluorination at the 6'-position of chitobioside resulted in an unprecedented increase in affinity to wheat germ agglutinin (WGA) by one order of magnitude. For the first time, we have characterized the binding profile of a previously underexplored WGA ligand LacdiNAc. Surprisingly, 4'-fluoro-LacdiNAc bound WGA even stronger than unmodified LacdiNAc. These observations were interpreted using molecular dynamic calculations along with STD and transferred NOESY NMR techniques, which gave evidence for the strengthening of CH/π interactions after deoxyfluorination of the side chain of the non-reducing GlcNAc. These results highlight the potential of fluorinated glycomimetics as high-affinity ligands of lectins and 19 F NMR-active probes., 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 © 2024 Elsevier Inc. All rights reserved.)

Parallel tempering molecular dynamics simulation, also known as temperature replica exchange simulation, is a popular enhanced sampling method used to study biomolecular systems. This method makes it possible to calculate the free energy differences between states of the system for a series of temperatures. We developed a method to easily calculate the errors (standard errors or confidence intervals) of these predictions using a modified version of our recently introduced JumpCount method. The number of transitions between states (e.g., protein folding events) is counted for each temperature. This number of transitions, together with the temperature, fully determines the value of the standard error or the confidence interval of the free energy difference. We also address the issue of convergence in the situation where all replicas start from one state by developing an estimator of the equilibrium constant from simulations that are not fully equilibrated. The prerequisite of the method is the Markovianity of the process studied., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

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.

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 MIC 99 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 H 37 Rv 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-β-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 H 37 Rv 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., 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 © 2023 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

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.

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., Competing Interests: Competing Interests: The authors have declared that no competing interests exist

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., (© 2022 Federation of European Biochemical Societies.)

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 β 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., Competing Interests: The authors declare that the investigation was carried out in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Spiwok, Kurečka and Křenek.)

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/.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Přerovská, Pavlů, Hancharyk, Rodionova, Vavříková and Spiwok.)

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 Ca 2+ 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.

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 β-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., (© 2020 Phycological Society of America.)

The ability to predict the transglycosylation activity of glycosidases by in silico analysis was investigated. The transglycosylation abilities of 7 different β-d-galactosidases from GH family 2 were tested experimentally using 7 different acceptors and p -nitrophenyl-β-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., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest in the publication., (© King Abdulaziz City for Science and Technology 2021.)

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.

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., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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., Competing Interests: No competing financial interests exist., (© Massimiliano Zanin et al., 2021; Published by Mary Ann Liebert, Inc.)

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 β-d-galactopyranosyl-(1→1)-thio-β-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., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (Copyright © 2020 Spiwok and Kříž.)

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.

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 IC 50 (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.

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 CO 2 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.

The state of a molecular system can be described in terms of collective variables. These low-dimensional descriptors of molecular structure can be used to monitor the state of the simulation, to calculate free energy profiles or to accelerate rare events by a bias potential or a bias force. Frequent calculation of some complex collective variables may slow down the simulation or analysis of trajectories. Moreover, many collective variables cannot be explicitly calculated for newly sampled structures. In order to address this problem, we developed a new package called anncolvar . This package makes it possible to build and train an artificial neural network model that approximates a collective variable. It can be used to generate an input for the open-source enhanced sampling simulation PLUMED package, so the collective variable can be monitored and biased by methods available in this program. The computational efficiency and the accuracy of anncolvar are demonstrated on selected molecular systems (cyclooctane derivative, Trp-cage miniprotein) and selected collective variables (Isomap, molecular surface area).

The cytotoxicity of mouse natural killer (NK) cells in response to pathological changes in target cells is regulated via the Nkrp1b receptor. Here, we characterized the Nkrp1b structure and structural features (stalk, loop, and oligomerization state) that affect its interactions. To study the Nkrp1b protein structure and the functional importance of its stalk, two Nkrp1b protein variants differing by the presence of the stalk were prepared. These variants were studied using a combination of structural mass spectrometry approaches with computational modeling to derive structural models. In addition, information about biological activity and localization in mammalian cells was acquired using scanning microscopy techniques and western blotting. Based on these methods, we obtained the structure of Nkrp1b ectodomain in its monomeric and dimeric conformations, identified the dimerization interface, and determined disulfide connections within the molecule. We found that Nkrp1b occurs as a mixture of monomers and homodimers, both in vitro and in vivo. SIGNIFICANCE: Despite the long-standing assumption that Nkrp1 proteins are homodimers connected by disulfide bonds in the stalk region, our data showed that both Nkrp1b protein variants form monomers and homodimers irrespective of the presence of the stalk. We demonstrated that the stalk is not crucial for protein dimerization or ligand binding and that Nkrp1b interacts with its natural ligands only in its monomeric conformation; therefore, dimers may have another regulatory function. Using a unique combination of computational, biochemical, and biological methods, we revealed the structural conformation and behavior of Nkrp1b in its native state. In addition, it is a first report utilizing the intermolecular chemical cross-linking of light- and heavy-labeled protein chains together with ion mobility-mass spectrometry to design the structural models of protein homodimers., (Copyright © 2018 Elsevier B.V. All rights reserved.)

α-l-Fucosidase isoenzyme 1 from bacterium Paenibacillus thiaminolyticus is a member of the glycoside hydrolase family GH29 capable of cleaving l-fucose from nonreducing termini of oligosaccharides and glycoconjugates. Here we present the first crystal structure of this protein revealing a novel quaternary state within this family. The protein is in a unique hexameric assembly revealing the first observed case of active site complementation by a residue from an adjacent monomer in this family. Mutation of the complementing tryptophan residue caused changes in the catalytic properties including a shift of the pH optimum, a change of affinity to an artificial chromogenic substrate and a decreased reaction rate for a natural substrate. The wild-type enzyme was active on most of the tested naturally occurring oligosaccharides and capable of transglycosylation on a variety of acceptor molecules, including saccharides, alcohols or chromogenic substrates. Mutation of the complementing residue changed neither substrate specificity nor the preference for the type of transglycosylation acceptor molecule; however, the yields of the reactions were lower in both cases. Maltose molecules bound to the enzyme in the crystal structure identified surface carbohydrate-binding sites, possibly participating in binding of larger oligosaccharides.

Microtubule dynamics is one of the major targets for new chemotherapeutic agents. This communication presents the synthesis and biological profiling of steroidal dimers based on estradiol, testosterone and pregnenolone bridged by 2,6-bis(azidomethyl)pyridine between D rings. The biological profiling revealed unique properties of the estradiol dimer including cytotoxic activities on a panel of 11 human cell lines, ability to arrest in the G2/M phase of the cell cycle accompanied with the attenuation of DNA/RNA synthesis. Thorough investigation precluded a genomic mechanism of action and revealed that the estradiol dimer acts at the cytoskeletal level by inhibiting tubulin polymerization. Further studies showed that estradiol dimer, but none of the other structurally related dimeric steroids, inhibited assembly of purified tubulin (IC 50 , 3.6 μM). The estradiol dimer was more potent than 2-methoxyestradiol, an endogenous metabolite of 17β-estradiol and well-studied microtubule polymerization inhibitor with antitumor effects that was evaluated in clinical trials. Further, it was equipotent to nocodazole (IC 50 , 1.5 μM), an antimitotic small molecule of natural origin. Both estradiol dimer and nocodazole completely and reversibly depolymerized microtubules in interphase U2OS cells at 2.5 μM concentration. At lower concentrations (50 nM), estradiol dimer decreased the microtubule dynamics and growth life-time and produced comparable effect to nocodazole on the microtubule dynamicity. In silico modeling predicted that estradiol dimer binds to the colchicine-binding site in the tubulin dimer. Finally, dimerization of the steroids abolished their ability to induce transactivation by estrogen receptor α and androgen receptors. Although other steroids were reported to interact with microtubules, the estradiol dimer represents a new structural type of steroid inhibitor of tubulin polymerization and microtubule dynamics, bearing antimitotic and cytotoxic activity in cancer cell lines., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: The wild relatives of crop species represent a potentially valuable source of novel genetic variation, particularly in the context of improving the crop's level of tolerance to abiotic stress. The mechanistic basis of these tolerances remains largely unexplored. Here, the focus was to characterize the transcriptomic response of the nodes (meristematic tissue) of couch grass (a relative of barley) to dehydration stress, and to compare it to that of the barley crown formed by both a drought tolerant and a drought sensitive barley cultivar., Results: Many of the genes up-regulated in the nodes by the stress were homologs of genes known to be mediated by abscisic acid during the response to drought, or were linked to either development or lipid metabolism. Transporters also featured prominently, as did genes acting on root architecture. The resilience of the couch grass node arise from both their capacity to develop an altered, more effective root architecture, but also from their formation of a lipid barrier on their outer surface and their ability to modify both their lipid metabolism and transporter activity when challenged by dehydration stress., Conclusions: Our analysis revealed the nature of dehydration stress response in couch grass. We suggested the tolerance is associated with lipid metabolism, the induction of transporters and the re-programming of development coordinated by ABA. We also proved the applicability of barley microarray for couch grass stress-response analysis.

Galectin-3 (Gal-3) is recognized as a prognostic marker in several cancer types. Its involvement in tumor development and proliferation makes this lectin a promising target for early cancer diagnosis and anti-cancer therapies. Gal-3 recognizes poly- N -acetyllactosamine (LacNAc)-based carbohydrate motifs of glycoproteins and glycolipids with a high specificity for internal LacNAc epitopes. This study analyzes the mode and kinetics of binding of Gal-3 to a series of multivalent neo-glycoproteins presenting complex poly-LacNAc-based oligosaccharide ligands on a scaffold of bovine serum albumin. These neo-glycoproteins rank among the strongest Gal-3 ligands reported, with K d reaching sub-nanomolar values as determined by surface plasmon resonance. Significant differences in the binding kinetics were observed within the ligand series, showing the tetrasaccharide capped with N , N' -diacetyllactosamine (LacdiNAc) as the strongest ligand of Gal-3 in this study. A molecular model of the Gal-3 carbohydrate recognition domain with docked oligosaccharide ligands is presented that shows the relations in the binding site at the molecular level. The neo-glycoproteins presented herein may be applied for selective recognition of Gal-3 both on the cell surface and in blood serum., Competing Interests: The authors declare no conflict of interest.

Flying Gaussian method simulates multiple replicas of the studied system and enhances sampling by disfavoring replicas to simultaneously sample similar states. The bias potential used for this enhancement is highly dynamic when looking at individual replica, which raises concerns about the accuracy of free-energy surfaces predicted by reweighing methods. Here we show that the bias potential can be considered as static when looking at the simulation from a multisystem perspective. We present two equations that can be used to predict the free-energy surface, and we demonstrate their convergence.

Many carbohydrate-binding proteins contain aromatic amino acid residues in their binding sites. These residues interact with carbohydrates in a stacking geometry via CH/π interactions. These interactions can be found in carbohydrate-binding proteins, including lectins, enzymes and carbohydrate transporters. Besides this, many non-protein aromatic molecules (natural as well as artificial) can bind saccharides using these interactions. Recent computational and experimental studies have shown that carbohydrate-aromatic CH/π interactions are dispersion interactions, tuned by electrostatics and partially stabilized by a hydrophobic effect in solvated systems., Competing Interests: The author declares no conflict of interest.

Metadynamics method has been widely used to enhance sampling in molecular simulations. Its original form suffers two major drawbacks, poor convergence in complex (especially biomolecular) systems and its serial nature. The first drawback has been addressed by introduction of a convergent variant known as well-tempered metadynamics. The second was addressed by introduction of a parallel multisystem metadynamics referred to as altruistic metadynamics. Here, we combine both approaches into well-tempered altruistic metadynamics. We provide mathematical arguments and trial simulations to show that it accurately predicts free energy surfaces.

Metadynamics is an important collective-coordinate-based enhanced sampling simulation method. Its performance depends significantly on the capability of collective coordinates to describe the studied molecular processes. Collective coordinates based on comparison with reference landmark structures can be used to enhance sampling in highly complex systems; however, they may slow down simulations due to high number of structure-structure distance (e.g., mean-square deviation) calculations. Here we introduce an approximation of root-mean-square or mean-square deviation that significantly reduces numbers of computationally expensive operations. We evaluate its accuracy and theoretical performance gain with metadynamics simulations on two molecular systems.