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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.

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.

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. Contact: martin.hofmann-apitius@scai.fraunhofer.de Supplementary Information: Supplementary data are available at Bioinformatics online.

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.

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 angstrom 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.

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. Electronic supplementary material The online version of this article (doi:10.1007/s00894-012-1563-4) contains supplementary material, which is available to authorized users.