KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development.

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

The cytochrome bc1 complex inhibitor Ametoctradin has an unusual binding mode.

Ametoctradin is an agricultural fungicide that selectively inhibits the cytochrome bc1 complex of oomycetes. Previous spectrophotometric studies using the purified cytochrome bc1 complex from Pythium sp. showed that Ametoctradin binds to the Qo-site of the enzyme. However, as modeling studies suggested a binding mode like that of the substrate ubiquinol, the possibility for a dual Qo- and Qi-site binding mode was left open. In this work, binding studies and enzyme assays with mitochondrial membrane preparations from Pythium sp. and an S. cerevisiae strain with a modified Qi-site were used to investigate further the binding mode of Ametoctradin. The results obtained argue that the compound could bind to both the Qo- and Qi-sites of the cytochrome bc1 complex and that its position or binding pose in the Qi-site differs from that of Cyazofamid and Amisulbrom, the two Qi-site-targeting, anti-oomycetes compounds. Furthermore, the data support the argument that Ametoctradin prefers binding to the reduced cytochrome bc1 complex. Thus, Ametoctradin has an unusual binding mode and further studies with this compound may offer the opportunity to better understand the catalytic cycle of the cytochrome bc1 complex.

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.

Binding of the respiratory chain inhibitor ametoctradin to the mitochondrial bc1 complex.

BACKGROUND: Ametoctradin is an agricultural fungicide that inhibits the mitochondrial bc1 complex of oomycetes. The bc1 complex has two quinone binding sites that can be addressed by inhibitors. Depending on their binding sites and binding modes, the inhibitors show different degrees of cross-resistance that need to be considered when designing spray programmes for agricultural fungicides. The binding site of ametoctradin was unknown. RESULTS: Cross-resistance analyses, the reduction of isolated Pythium sp. bc1 complex in the presence of different inhibitors and molecular modelling studies were used to analyse the binding site and binding mode of ametoctradin. All three approaches provide data supporting the argument that ametoctradin binds to the Pythium bc1 complex similarly to stigmatellin. CONCLUSION: The binding mode of ametoctradin differs from other agricultural fungicides such as cyazofamid and the strobilurins. This explains the lack of cross-resistance with strobilurins and related inhibitors, where resistance is mainly caused by G143A amino acid exchange. Accordingly, mixtures or alternating applications of these fungicides and ametoctradin can help to minimise the risk of the emergence of new resistant isolates.

Structure-activity relationships of thiostrepton derivatives: implications for rational drug design.

The bacterial ribosome is a major target of naturally occurring thiopeptides antibiotics. Studying thiopeptide (e.g. thiostrepton) binding to the GAR's 23S·L11 ribosomal subunit using docking methods is challenging. Regarding the target, the binding site is composed of a flexible protein-RNA nonbonded interface whose available crystal structure is of medium resolution. Regarding the ligands, the thiopeptides are chemically complex, flexible, and contain macrocycles. In this study we developed a combined MD-docking-MD workflow that allows us to study thiopeptide-23S·L11 binding. It is shown that docking thiostrepton-like ligands to an MD-refined receptor structure instead of the medium resolution crystal leads to better convergence to the native-like docking pose and a better reproduction of experimental binding affinities. By applying an energy decomposition analysis, we identify key structural binding elements within GAR's rRNA-protein binding site and within the ligand structures.

Generalized workflow for generating highly predictive in silico off-target activity models.

Chemical structure data and corresponding measured bioactivities of compounds are nowadays easily available from public and commercial databases. However, these databases contain heterogeneous data from different laboratories determined under different protocols and, in addition, sometimes even erroneous entries. In this study, we evaluated the use of data from bioactivity databases for the generation of high quality in silico models for off-target mediated toxicity as a decision support in early drug discovery and crop-protection research. We chose human acetylcholinesterase (hAChE) inhibition as an exemplary end point for our case study. A standardized and thorough quality management routine for input data consisting of more than 2,200 chemical entities from bioactivity databases was established. This procedure finally enables the development of predictive QSAR models based on heterogeneous in vitro data from multiple laboratories. An extended applicability domain approach was used, and regression results were refined by an error estimation routine. Subsequent classification augmented by special consideration of borderline candidates leads to high accuracies in external validation achieving correct predictive classification of 96%. The standardized process described herein is implemented as a (semi)automated workflow and thus easily transferable to other off-targets and assay readouts.

A chip-calorimetric approach to the analysis of Ag nanoparticle caused inhibition and inactivation of beads-grown bacterial biofilms.

With the increasing complexity of model systems for the investigation of antibacterial effects of nanoparticles, the demands on appropriate analysis methods are rising. In case of biofilms grown on small particles, the high inhomogeneity of the samples represents a major challenge for traditional biofilm analysis. For this purpose, we developed a new calorimetric method which allows non-invasive and real-time investigation of the effects of nanoparticles on beads-grown biofilms which meets the requirements for an increased sample throughput. The method employs a newly developed chip calorimeter that is able to detect changes in the metabolic activity of biofilm samples within minutes. Using this novel device, the antibacterial effect of silver nanoparticles on Pseudomonas putida biofilms grown on agarose beads was investigated. The superparamagnetic properties of the embedded particles within the agarose beads allow an automated sample throughput. Growth inhibition and inactivation effects of silver nanoparticles (AgNPs) on biofilm bacteria were quantified by analyzing the metabolic heat production rate. As a result, a concentration dependent manner of growth inhibition and inactivation was found demonstrating the suitability and sensitivity of the methodology.

Principal component and clustering analysis on molecular dynamics data of the ribosomal L11·23S subdomain.

With improvements in computer speed and algorithm efficiency, MD simulations are sampling larger amounts of molecular and biomolecular conformations. Being able to qualitatively and quantitatively sift these conformations into meaningful groups is a difficult and important task, especially when considering the structure-activity paradigm. Here we present a study that combines two popular techniques, principal component (PC) analysis and clustering, for revealing major conformational changes that occur in molecular dynamics (MD) simulations. Specifically, we explored how clustering different PC subspaces effects the resulting clusters versus clustering the complete trajectory data. As a case example, we used the trajectory data from an explicitly solvated simulation of a bacteria's L11·23S ribosomal subdomain, which is a target of thiopeptide antibiotics. Clustering was performed, using K-means and average-linkage algorithms, on data involving the first two to the first five PC subspace dimensions. For the average-linkage algorithm we found that data-point membership, cluster shape, and cluster size depended on the selected PC subspace data. In contrast, K-means provided very consistent results regardless of the selected subspace. Since we present results on a single model system, generalization concerning the clustering of different PC subspaces of other molecular systems is currently premature. However, our hope is that this study illustrates a) the complexities in selecting the appropriate clustering algorithm, b) the complexities in interpreting and validating their results, and c) by combining PC analysis with subsequent clustering valuable dynamic and conformational information can be obtained.

Influence of thiostrepton binding on the ribosomal GTPase associated region characterized by molecular dynamics simulation.

The thiostrepton antibiotic inhibits bacterial protein synthesis by binding to a cleft formed by the ribosomal protein L11 and 23S's rRNA helices 43-44 on the 70S ribosome. It was proposed from crystal structures that the ligand restricts L11's N-terminal movement and thus prevents proper translation factor binding. An exact understanding of thiostrepton's impact on the binding site's dynamics at atomistic resolution is still missing. Here we report an all-atom molecular dynamics simulations of the binary L11·rRNA and the ternary L11·rRNA·thiostrepton complex (rRNA = helices 43-44). We demonstrate that thiostrepton directly impacts the binding site's atomic and biomacromolecular dynamics.

Potentials and limitations of miniaturized calorimeters for bioprocess monitoring.

In theory, heat production rates are very well suited for analysing and controlling bioprocesses on different scales from a few nanolitres up to many cubic metres. Any bioconversion is accompanied by a production (exothermic) or consumption (endothermic) of heat. The heat is tightly connected with the stoichiometry of the bioprocess via the law of Hess, and its rate is connected to the kinetics of the process. Heat signals provide real-time information of bioprocesses. The combination of heat measurements with respirometry is theoretically suited for the quantification of the coupling between catabolic and anabolic reactions. Heat measurements have also practical advantages. Unlike most other biochemical sensors, thermal transducers can be mounted in a protected way that prevents fouling, thereby minimizing response drifts. Finally, calorimetry works in optically opaque solutions and does not require labelling or reactants. It is surprising to see that despite all these advantages, calorimetry has rarely been applied to monitor and control bioprocesses with intact cells in the laboratory, industrial bioreactors or ecosystems. This review article analyses the reasons for this omission, discusses the additional information calorimetry can provide in comparison with respirometry and presents miniaturization as a potential way to overcome some inherent weaknesses of conventional calorimetry. It will be discussed for which sample types and scientific question miniaturized calorimeter can be advantageously applied. A few examples from different fields of microbiological and biotechnological research will illustrate the potentials and limitations of chip calorimetry. Finally, the future of chip calorimetry is addressed in an outlook.

PLIO: an ontology for formal description of protein-ligand interactions.

MOTIVATION: Biomedical ontologies have proved to be valuable tools for data analysis and data interoperability. Protein-ligand interactions are key players in drug discovery and development; however, existing public ontologies that describe the knowledge space of biomolecular interactions do not cover all aspects relevant to pharmaceutical modelling and simulation. RESULTS: The protein--ligand interaction ontology (PLIO) was developed around three main concepts, namely target, ligand and interaction, and was enriched by adding synonyms, useful annotations and references. The quality of the ontology was assessed based on structural, functional and usability features. Validation of the lexicalized ontology by means of natural language processing (NLP)-based methods showed a satisfactory performance (F-score = 81%). Through integration into our information retrieval environment we can demonstrate that PLIO supports lexical search in PubMed abstracts. The usefulness of PLIO is demonstrated by two use-case scenarios and it is shown that PLIO is able to capture both confirmatory and new knowledge from simulation and empirical studies. AVAILABILITY: The PLIO ontology is made freely available to the public at http://www.scai.fraunhofer.de/bioinformatics/downloads.html.

In silico drug discovery approaches on grid computing infrastructures.

The first step in finding a "drug" is screening chemical compound databases against a protein target. In silico approaches like virtual screening by molecular docking are well established in modern drug discovery. As molecular databases of compounds and target structures are becoming larger and more and more computational screening approaches are available, there is an increased need in compute power and more complex workflows. In this regard, computational Grids are predestined and offer seamless compute and storage capacity. In recent projects related to pharmaceutical research, the high computational and data storage demands of large-scale in silico drug discovery approaches have been addressed by using Grid computing infrastructures, in both; pharmaceutical industry as well as academic research. Grid infrastructures are part of the so-called eScience paradigm, where a digital infrastructure supports collaborative processes by providing relevant resources and tools for data- and compute-intensive applications. Substantial computing resources, large data collections and services for data analysis are shared on the Grid infrastructure and can be mobilized on demand. This review gives an overview on the use of Grid computing for in silico drug discovery and tries to provide a vision of future development of more complex and integrated workflows on Grids, spanning from target identification and target validation via protein-structure and ligand dependent screenings to advanced mining of large scale in silico experiments.

DockFlow - a prototypic PharmaGrid for virtual screening integrating four different docking tools.

In this paper we present DockFlow, a prototypic version of a PharmaGrid. DockFlow is supporting pharmaceutical research through enabling virtual screening on the Grid. The system was developed in the course of the BRIDGE project funded by the European Commission. Grids have been used before to run compute- and data-intensive virtual screening experiments, like in the WISDOM project. With DockFlow, however, we addressed a variety of problems yet unsolved, like the diversity of results produced by different docking tools. We also addressed the problem of analysing the data produced in a distributed virtual screening system applying a combinatorial docking approach. In DockFlow we worked on a grid-based problem solving environment for virtual screening with the following major features: execution of four different docking services (FlexX, AutoDock, DOCK and GAsDock) at locations in Europe and China remotely from a common workflow, storage of the results in a common Docking Database providing a shared analysis platform for the collaboration partners and combination of the results. The DockFlow prototype is evaluated on two scientific case studies: malaria and avian flu.

Design of new plasmepsin inhibitors: a virtual high throughput screening approach on the EGEE grid.

Though different species of the genus Plasmodium may be responsible for malaria, the variant caused by P. falciparum is often very dangerous and even fatal if untreated. Hemoglobin degradation is one of the key metabolic processes for the survival of the Plasmodium parasite in its host. Plasmepsins, a family of aspartic proteases encoded by the Plasmodium genome, play a prominent role in host hemoglobin cleavage. In this paper we demonstrate the use of virtual screening, in particular molecular docking, employed at a very large scale to identify novel inhibitors for plasmepsins II and IV. A large grid infrastructure, the EGEE grid, was used to address the problem of large computation resources required for docking hundreds of thousands of chemical compounds on different plasmepsin targets of P. falciparum. A large compound library of about 1 million chemical compounds was docked on 5 different targets of plasmepsins using two different docking software, namely FlexX and AutoDock. Several strategies were employed to analyze the results of this virtual screening approach including docking scores, ideal binding modes, and interactions to key residues of the protein. Three different classes of structures with thiourea, diphenylurea, and guanidino scaffolds were identified to be promising hits. While the identification of diphenylurea compounds is in accordance with the literature and thus provides a sort of "positive control", the identification of novel compounds with a guanidino scaffold proves that high throughput docking can be effectively used to identify novel potential inhibitors of P. falciparum plasmepsins. Thus, with the work presented here, we do not only demonstrate the relevance of computational grids in drug discovery but also identify several promising small molecules which have the potential to serve as candidate inhibitors for P. falciparum plasmepsins. With the use of the EGEE grid infrastructure for the virtual screening campaign against the malaria causing parasite P. falciparum we have demonstrated that resource sharing on an eScience infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor.

Alternative to consensus scoring--a new approach toward the qualitative combination of docking algorithms.

Since the development of the first docking algorithm in the early 1980s a variety of different docking approaches and tools has been created in order to solve the docking problem. Subsequent studies have shown that the docking performance of most tools strongly depends on the considered target. Thus it is hard to choose the best algorithm in the situation at hand. The docking tools FlexX and AutoDock are among the most popular programs for docking flexible ligands into target proteins. Their analysis, comparison, and combination are the topics of this study. In contrast to standard consensus scoring techniques which integrate different scoring algorithms usually only by their rank, we focus on a more general approach. Our new combined docking workflow-AutoxX-unifies the interaction models of AutoDock and FlexX rather than combining the scores afterward which allows interpretability of the results. The performance of FlexX, AutoDock, and the combined algorithm AutoxX was evaluated on the basis of a test set of 204 structures from the Protein Data Bank (PDB). AutoDock and FlexX show a highly diverse redocking accuracy at the different complexes which assures again the usefulness of taking several docking algorithms into account. With the combined docking the number of complexes reproduced below an rmsd of 2.5 A could be raised by 10. AutoxX had a strong positive effect on several targets. The highest performance increase could be found when redocking 20 protein-ligand complexes of alpha-thrombin, plasmepsin, neuraminidase, and d-xylose isomerase. A decrease was found for gamma-chymotrypsin. The results show that--applied to the right target-AutoxX can improve the docking performance compared to AutoDock and FlexX alone.

Glutamate release by neurons evokes a purinergic inhibitory mechanism of osmotic glial cell swelling in the rat retina: activation by neuropeptide Y.

Glial cell swelling is a central cause of ischemic edema in the brain and retina; however, the regulation of glial cell volume by endogenous factors in situ is largely unknown. In slices of the postischemic retina of the rat, the somata of glial (Müller) cells swell upon hypotonic stress that is not observed in slices of control retinas. We describe an endogenous signaling pathway that leads to inhibition of the osmotic glial cell swelling, and that is evoked by the release of glutamate from retinal neurons upon application of neuropeptide Y. Glutamate activates metabotropic glutamate receptors on swollen glial cells, which evokes a Ca2+ -independent purinergic signaling cascade that involves release of ATP, P2Y1 receptor activation, and transporter-mediated release of adenosine. Activation of A1 receptors causes the inhibition of osmotic glial cell swelling, by a protein kinase A-dependent activation of K+ and Cl- channels. It is proposed that the glutamate-evoked purinergic receptor signaling of glial cells is crucially involved in the cell volume homeostasis of the retina, and that this mechanism may contribute to the protective effect of adenosine in the ischemic tissue.

The glucocorticoid triamcinolone acetonide inhibits osmotic swelling of retinal glial cells via stimulation of endogenous adenosine signaling.

The glucocorticoid triamcinolone acetonide is clinically used for the treatment of macular edema. However, the edema-resolving mechanisms of triamcinolone are incompletely understood. Since cell swelling is a central cause of cytotoxic edema in the brain and retina, we determined the effects of triamcinolone acetonide on the swelling of retinal ganglion and Müller glial cells in acutely isolated retinas from rats and guinea pigs in situ. Triamcinolone acetonide (100 microM) had no effect on the swelling of ganglion cells that was evoked in isolated whole mounts of the guinea pig retina by acute application of glutamate (1 mM) or high K+ (50 mM). However, triamcinolone reversed the osmotic swelling of Müller glial cells in retinas of the rat that was observed under various experimental conditions: in retinas isolated at 3 days after transient retinal ischemia, in retinas of eyes with lipopolysaccharide-induced ocular inflammation, and in control retinas in the presence of Ba2+ (1 mM), H2O2 (200 microM), arachidonic acid (10 microM), or prostaglandin E2 (30 nM). The inhibiting effect of triamcinolone on osmotic glial cell swelling was mediated by stimulation of transporter-mediated release of endogenous adenosine and subsequent A1 receptor activation, resulting in an elevation of the intracellular cAMP level and activation of the protein kinase A, and, finally, in an opening of extrusion pathways for K+ and Cl- ions. The inhibitory effect on the cytotoxic swelling of glial cells may contribute to the fast edema-resolving effect of vitreal triamcinolone observed in human patients.