ReverseScreen3D: a structure-based ligand matching method to identify protein targets

Ligand promiscuity, which is now recognized as an extremely common phenomenon, is a major underlying cause of drug toxicity. We have developed a new reverse virtual screening (VS) method called ReverseScreen3D, which can be used to predict the potential protein targets of a query compound of interest. The method uses a 2D fingerprint-based method to select a ligand template from each unique binding site of each protein within a target database. The target database contains only the structurally determined bioactive conformations of known ligands. The 2D comparison is followed by a 3D structural comparison to the selected query ligand using a geometric matching method, in order to prioritize each target binding site in the database. We have evaluated the performance of the ReverseScreen2D and 3D methods using a diverse set of small molecule protein inhibitors known to have multiple targets, and have shown that they are able to provide a highly significant enrichment of true targets in the database. Furthermore, we have shown that the 3D structural comparison improves early enrichment when compared with the 2D method alone, and that the 3D method performs well even in the absence of 2D similarity to the template ligands. By carrying out further experimental screening on the prioritized list of targets, it may be possible to determine the potential targets of a new compound or determine the off-targets of an existing drug. The ReverseScreen3D method has been incorporated into a Web server, which is freely available at http://www.modelling.leeds.ac.uk/ReverseScreen3D .     

A machine learning-based method to improve docking scoring functions and its application to drug repurposing

Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score; however, these weights should be gene family dependent. In addition, they incorrectly assume that individual interactions contribute toward the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper, we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS. We construct two prediction models: a regression model trained using IC(50) values from BindingDB, and a classification model trained using active and decoy compounds from the Directory of Useful Decoys (DUD). Moreover, to address the issue of overrepresentation of negative data in high-throughput screening data sets, we have designed a multiple-planar SVM training procedure for the classification model. The increased performance that both SVMs give when compared with the original eHiTS scoring function highlights the potential for using nonlinear methods when deriving overall energy scores from their individual components. We apply the above methodology to train a new scoring function for direct inhibitors of Mycobacterium tuberculosis (M.tb) InhA. By combining ligand binding site comparison with the new scoring function, we propose that phosphodiesterase inhibitors can potentially be repurposed to target M.tb InhA. Our methodology may be applied to other gene families for which target structures and activity data are available, as demonstrated in the work presented here.     

Imaging cytoplasmic cAMP in mouse brainstem neurons

Background  

cAMP is an ubiquitous second messenger mediating various neuronal functions, often as a consequence of increased intracellular Ca²⁺ levels. While imaging of calcium is commonly used in neuroscience applications, probing for cAMP levels has not yet been performed in living vertebrate neuronal tissue before.     

Stable isotopic studies of earthworm feeding ecology in tropical ecosystems of Puerto Rico

Feeding strategies of earthworms and their influence on soil processes are often inferred from morphological, behavioral and physiological traits. We used (13)C and (15)N natural abundance in earthworms, soils and plants to explore patterns of resource utilization by different species of earthworms in three tropical ecosystems in Puerto Rico. In a high altitude dwarf forest, native earthworms Trigaster longissimus and Estherella sp. showed less (15)N enrichment ((15)N = 3-6 per thousand) than exotic Pontoscolex corethrurus ((15)N =7-9 per thousand) indicating different food sources or stronger isotopic discrimination by the latter. Conversely, in a lower altitude tabonuco forest, Estherella sp. and P. corethrurus overlapped completely in (15)N enrichment ((15)N = 6-9 per thousand), suggesting the potential for interspecific competition for N resources. A tabonuco forest converted to pasture contained only P. corethrurus which were less enriched in (15)N than those in the forest sites, but more highly enriched in (13)C suggesting assimilation of C from the predominant C(4) grass. These results support the utility of stable isotopes to delineate resource partitioning and potential competitive interactions among earthworm species. Copyright 1999 John Wiley & Sons, Ltd.