Abstract: BACKGROUND: Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases. DESCRIPTION: A comprehensive pharmacokinetics ontology was constructed. It can annotate all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. It covers all drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK-corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK-corpus was demonstrated by a drug interaction extraction text mining analysis. CONCLUSIONS: The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK-corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions.

Abstract: BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.

Abstract: We present 'dcGO' (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, 'dcGO', provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.

Abstract: The actin-bundling protein fascin is a key mediator of tumor invasion and metastasis and its activity drives filopodia formation, cell-shape changes and cell migration. Small-molecule inhibitors of fascin block tumor metastasis in animal models. Conversely, fascin deficiency might underlie the pathogenesis of some developmental brain disorders. To identify fascin-pathway modulators we devised a cell-based assay for fascin function and used it in a bidirectional drug screen. The screen utilized cultured fascin-deficient mutant Drosophila neurons, whose neurite arbors manifest the 'filagree' phenotype. Taking a repurposing approach, we screened a library of 1040 known compounds, many of them FDA-approved drugs, for filagree modifiers. Based on scaffold distribution, molecular-fingerprint similarities, and chemical-space distribution, this library has high structural diversity, supporting its utility as a screening tool. We identified 34 fascin-pathway blockers (with potential anti-metastasis activity) and 48 fascin-pathway enhancers (with potential cognitive-enhancer activity). The structural diversity of the active compounds suggests multiple molecular targets. Comparisons of active and inactive compounds provided preliminary structure-activity relationship information. The screen also revealed diverse neurotoxic effects of other drugs, notably the 'beads-on-a-string' defect, which is induced solely by statins. Statin-induced neurotoxicity is enhanced by fascin deficiency. In summary, we provide evidence that primary neuron culture using a genetic model organism can be valuable for early-stage drug discovery and developmental neurotoxicity testing. Furthermore, we propose that, given an appropriate assay for target-pathway function, bidirectional screening for brain-development disorders and invasive cancers represents an efficient, multipurpose strategy for drug discovery.

Abstract: Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

Abstract: Bacteria from the genus Streptomyces are very important for the production of natural bioactive compounds such as antibiotic, antitumour or immunosuppressant drugs. Around two-thirds of all known natural antibiotics are produced by these bacteria. An enormous quantity of crucial data related to this genus has been generated and published, but so far no freely available and comprehensive database exists. Here, we present StreptomeDB (http://www.pharmaceutical-bioinformatics.de/streptomedb/). To the best of our knowledge, this is the largest database of natural products isolated from Streptomyces. It contains >2400 unique and diverse compounds from >1900 different Streptomyces strains and substrains. In addition to names and molecular structures of the compounds, information about source organisms, references, biological role, activities and synthesis routes (e.g. polyketide synthase derived and non-ribosomal peptides derived) is included. Data can be accessed through queries on compound names, chemical structures or organisms. Extraction from the literature was performed through automatic text mining of thousands of articles from PubMed, followed by manual curation. All annotated compound structures can be downloaded from the website and applied for in silico screenings for identifying new active molecules with undiscovered properties.

Abstract: Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

Abstract: Given the increasing notion of target promiscuity of bioactive compounds and polypharmacological drug behavior, a detailed analysis of publicly available compound activity data from medicinal chemistry sources was carried out to determine and quantify the degree of promiscuity of active compounds across all known human target families. The results are surprising. Approximately 62% of currently available compounds with high-confidence activity data are only annotated with a single biological target, whereas 36% are known to act against multiple targets within the same family (i.e., closely related targets). However, only ~2% of bioactive compounds are promiscuous across different target families. Thus, despite general data sparseness, these findings indicate that highly promiscuous bioactive compounds only rarely occur. Because pharmaceutically relevant active compounds represent the pool from which drug candidates emerge, one might extrapolate from these results and conclude that there is a low statistical probability to obtain drugs that act against multiple targets belonging to distinct families.

Abstract: The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to approximately 1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry.

Abstract: ChemProt-2.0 (http://www.cbs.dtu.dk/services/ChemProt-2.0) is a public available compilation of multiple chemical-protein annotation resources integrated with diseases and clinical outcomes information. The database has been updated to >1.15 million compounds with 5.32 millions bioactivity measurements for 15 290 proteins. Each protein is linked to quality-scored human protein-protein interactions data based on more than half a million interactions, for studying diseases and biological outcomes (diseases, pathways and GO terms) through protein complexes. In ChemProt-2.0, therapeutic effects as well as adverse drug reactions have been integrated allowing for suggesting proteins associated to clinical outcomes. New chemical structure fingerprints were computed based on the similarity ensemble approach. Protein sequence similarity search was also integrated to evaluate the promiscuity of proteins, which can help in the prediction of off-target effects. Finally, the database was integrated into a visual interface that enables navigation of the pharmacological space for small molecules. Filtering options were included in order to facilitate and to guide dynamic search of specific queries.