GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds.

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known. Receptor-based methods predicting protein-ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein-ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.

PharmDB-K: Integrated Bio-Pharmacological Network Database for Traditional Korean Medicine.

Despite the growing attention given to Traditional Medicine (TM) worldwide, there is no well-known, publicly available, integrated bio-pharmacological Traditional Korean Medicine (TKM) database for researchers in drug discovery. In this study, we have constructed PharmDB-K, which offers comprehensive information relating to TKM-associated drugs (compound), disease indication, and protein relationships. To explore the underlying molecular interaction of TKM, we integrated fourteen different databases, six Pharmacopoeias, and literature, and established a massive bio-pharmacological network for TKM and experimentally validated some cases predicted from the PharmDB-K analyses. Currently, PharmDB-K contains information about 262 TKMs, 7,815 drugs, 3,721 diseases, 32,373 proteins, and 1,887 side effects. One of the unique sets of information in PharmDB-K includes 400 indicator compounds used for standardization of herbal medicine. Furthermore, we are operating PharmDB-K via phExplorer (a network visualization software) and BioMart (a data federation framework) for convenient search and analysis of the TKM network. Database URL: http://pharmdb-k.org, http://biomart.i-pharm.org.

Comprehensive data resources and analytical tools for pathological association of aminoacyl tRNA synthetases with cancer.

Mammalian cells have cytoplasmic and mitochondrial aminoacyl-tRNA synthetases (ARSs) that catalyze aminoacylation of tRNAs during protein synthesis. Despite their housekeeping functions in protein synthesis, recently, ARSs and ARS-interacting multifunctional proteins (AIMPs) have been shown to play important roles in disease pathogenesis through their interactions with disease-related molecules. However, there are lacks of data resources and analytical tools that can be used to examine disease associations of ARS/AIMPs. Here, we developed an Integrated Database for ARSs (IDA), a resource database including cancer genomic/proteomic and interaction data of ARS/AIMPs. IDA includes mRNA expression, somatic mutation, copy number variation and phosphorylation data of ARS/AIMPs and their interacting proteins in various cancers. IDA further includes an array of analytical tools for exploration of disease association of ARS/AIMPs, identification of disease-associated ARS/AIMP interactors and reconstruction of ARS-dependent disease-perturbed network models. Therefore, IDA provides both comprehensive data resources and analytical tools for understanding potential roles of ARS/AIMPs in cancers.

CDA: combinatorial drug discovery using transcriptional response modules.

BACKGROUND: Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years. METHODOLOGY: Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells. CONCLUSIONS: CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.