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Published in January 2015
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ValidatorDB: database of up-to-date validation results for ligands and non-standard residues from the Protein Data Bank.

Authors: Sehnal D, Svobodova Varekova R, Pravda L, Ionescu CM, Geidl S, Horsky V, Jaiswal D, Wimmerova M, Koca J

Abstract: Following the discovery of serious errors in the structure of biomacromolecules, structure validation has become a key topic of research, especially for ligands and non-standard residues. ValidatorDB (freely available at http://ncbr.muni.cz/ValidatorDB) offers a new step in this direction, in the form of a database of validation results for all ligands and non-standard residues from the Protein Data Bank (all molecules with seven or more heavy atoms). Model molecules from the wwPDB Chemical Component Dictionary are used as reference during validation. ValidatorDB covers the main aspects of validation of annotation, and additionally introduces several useful validation analyses. The most significant is the classification of chirality errors, allowing the user to distinguish between serious issues and minor inconsistencies. Other such analyses are able to report, for example, completely erroneous ligands, alternate conformations or complete identity with the model molecules. All results are systematically classified into categories, and statistical evaluations are performed. In addition to detailed validation reports for each molecule, ValidatorDB provides summaries of the validation results for the entire PDB, for sets of molecules sharing the same annotation (three-letter code) or the same PDB entry, and for user-defined selections of annotations or PDB entries.
Published in January 2015
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Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions.

Authors: Betts MJ, Lu Q, Jiang Y, Drusko A, Wichmann O, Utz M, Valtierra-Gutierrez IA, Schlesner M, Jaeger N, Jones DT, Pfister S, Lichter P, Eils R, Siebert R, Bork P, Apic G, Gavin AC, Russell RB

Abstract: Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein-protein, protein-nucleic acid and a subset of protein-chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.
Published in January 2015
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ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms.

Authors: Cai MC, Xu Q, Pan YJ, Pan W, Ji N, Li YB, Jin HJ, Liu K, Ji ZL

Abstract: Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico evaluation of drug (or candidate) safety in laboratory is thus so desired, and unfortunately has been largely hindered by misuse of ADR terms. The growing impact of bioinformatics and systems biology in toxicological research also requires a specialized ADR term system that works beyond a simple glossary. Adverse Drug Reaction Classification System (ADReCS; http://bioinf.xmu.edu.cn/ADReCS) is a comprehensive ADR ontology database that provides not only ADR standardization but also hierarchical classification of ADR terms. The ADR terms were pre-assigned with unique digital IDs and at the same time were well organized into a four-level ADR hierarchy tree for building an ADR-ADR relation. Currently, the database covers 6544 standard ADR terms and 34,796 synonyms. It also incorporates information of 1355 single active ingredient drugs and 134,022 drug-ADR pairs. In summary, ADReCS offers an opportunity for direct computation on ADR terms and also provides clues to mining common features underlying ADRs.
Published in January 2015
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The RCSB Protein Data Bank: views of structural biology for basic and applied research and education.

Authors: Rose PW, Prlic A, Bi C, Bluhm WF, Christie CH, Dutta S, Green RK, Goodsell DS, Westbrook JD, Woo J, Young J, Zardecki C, Berman HM, Bourne PE, Burley SK

Abstract: The RCSB Protein Data Bank (RCSB PDB, http://www.rcsb.org) provides access to 3D structures of biological macromolecules and is one of the leading resources in biology and biomedicine worldwide. Our efforts over the past 2 years focused on enabling a deeper understanding of structural biology and providing new structural views of biology that support both basic and applied research and education. Herein, we describe recently introduced data annotations including integration with external biological resources, such as gene and drug databases, new visualization tools and improved support for the mobile web. We also describe access to data files, web services and open access software components to enable software developers to more effectively mine the PDB archive and related annotations. Our efforts are aimed at expanding the role of 3D structure in understanding biology and medicine.
Published in January 2015
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GSK-3 modulates cellular responses to a broad spectrum of kinase inhibitors.

Authors: Thorne CA, Wichaidit C, Coster AD, Posner BA, Wu LF, Altschuler SJ

Abstract: A fundamental challenge in treating disease is identifying molecular states that affect cellular responses to drugs. Here, we focus on glycogen synthase kinase 3 (GSK-3), a key regulator for many of the hallmark behaviors of cancer cells. We alter GSK-3 activity in colon epithelial cells to test its role in modulating drug response. We find that GSK-3 activity broadly affects the cellular sensitivities to a panel of oncology drugs and kinase inhibitors. Specifically, inhibition of GSK-3 activity can strongly desensitize or sensitize cells to kinase inhibitors (for example, mTOR or PLK1 inhibitors, respectively). Additionally, colorectal cancer cell lines, in which GSK-3 function is commonly suppressed, are resistant to mTOR inhibitors and yet highly sensitive to PLK1 inhibitors, and this is further exacerbated by additional GSK-3 inhibition. Finally, by conducting a kinome-wide RNAi screen, we find that GSK-3 modulates the cell proliferative phenotype of a large fraction ( approximately 35%) of the kinome, which includes approximately 50% of current, clinically relevant kinase-targeted drugs. Our results highlight an underappreciated interplay of GSK-3 with therapeutically important kinases and suggest strategies for identifying disease-specific molecular profiles that can guide optimal selection of drug treatment.
Published in January 2015
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PubAngioGen: a database and knowledge for angiogenesis and related diseases.

Authors: Li P, Liu Y, Wang H, He Y, Wang X, He Y, Lv F, Chen H, Pang X, Liu M, Shi T, Yi Z

Abstract: Angiogenesis is the process of generating new blood vessels based on existing ones, which is involved in many diseases including cancers, cardiovascular diseases and diabetes mellitus. Recently, great efforts have been made to explore the mechanisms of angiogenesis in various diseases and many angiogenic factors have been discovered as therapeutic targets in anti- or pro-angiogenic drug development. However, the resulted information is sparsely distributed and no systematical summarization has been made. In order to integrate these related results and facilitate the researches for the community, we conducted manual text-mining from published literature and built a database named as PubAngioGen (http://www.megabionet.org/aspd/). Our online application displays a comprehensive network for exploring the connection between angiogenesis and diseases at multilevels including protein-protein interaction, drug-target, disease-gene and signaling pathways among various cells and animal models recorded through text-mining. To enlarge the scope of the PubAngioGen application, our database also links to other common resources including STRING, DrugBank and OMIM databases, which will facilitate understanding the underlying molecular mechanisms of angiogenesis and drug development in clinical therapy.
Published in January 2015
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In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni.

Authors: Neves BJ, Braga RC, Bezerra JC, Cravo PV, Andrade CH

Abstract: Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes.
Published in January 2015
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T3DB: the toxic exposome database.

Authors: Wishart D, Arndt D, Pon A, Sajed T, Guo AC, Djoumbou Y, Knox C, Wilson M, Liang Y, Grant J, Liu Y, Goldansaz SA, Rappaport SM

Abstract: The exposome is defined as the totality of all human environmental exposures from conception to death. It is often regarded as the complement to the genome, with the interaction between the exposome and the genome ultimately determining one's phenotype. The 'toxic exposome' is the complete collection of chronically or acutely toxic compounds to which humans can be exposed. Considerable interest in defining the toxic exposome has been spurred on by the realization that most human injuries, deaths and diseases are directly or indirectly caused by toxic substances found in the air, water, food, home or workplace. The Toxin-Toxin-Target Database (T3DB--www.t3db.ca) is a resource that was specifically designed to capture information about the toxic exposome. Originally released in 2010, the first version of T3DB contained data on nearly 2900 common toxic substances along with detailed information on their chemical properties, descriptions, targets, toxic effects, toxicity thresholds, sequences (for both targets and toxins), mechanisms and references. To more closely align itself with the needs of epidemiologists, toxicologists and exposome scientists, the latest release of T3DB has been substantially upgraded to include many more compounds (>3600), targets (>2000) and gene expression datasets (>15,000 genes). It now includes extensive data on 'normal' toxic compound concentrations in human biofluids as well as detailed chemical taxonomies, informative chemical ontologies and a large number of referential NMR, MS/MS and GC-MS spectra. This manuscript describes the most recent update to the T3DB, which was previously featured in the 2010 NAR Database Issue.
Published in January 2015
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Human cancer databases (review).

Authors: Pavlopoulou A, Spandidos DA, Michalopoulos I

Abstract: Cancer is one of the four major noncommunicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancerassociated data from diverse resources, such as scientific publications, genomewide association studies, gene expression experiments, genegene or proteinprotein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to wellannotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancerrelated information and bioinformatic tools have also been provided.
Published on January 29, 2015
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FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach.

Authors: Lu W, Cheng F, Jiang J, Zhang C, Deng X, Xu Z, Zou S, Shen X, Tang Y, Huang J

Abstract: Non-steroidal anti-inflammatory drugs (NSAIDs) are worldwide used drugs for analgesic, antipyretic, and anti-inflammatory therapeutics. However, NSAIDs often cause several serious liver injuries, such as drug-induced liver injury (DILI), and the molecular mechanisms of DILI have not been clearly elucidated. In this study, we developed a systems pharmacology approach to explore the mechanism-of-action of NSAIDs. We found that the Farnesoid X Receptor (FXR) antagonism of NSAIDs is a potential molecular mechanism of DILI through systematic network analysis and in vitro assays. Specially, the quantitative real-time PCR assay reveals that indomethacin and ibuprofen regulate FXR downstream target gene expression in HepG2 cells. Furthermore, the western blot shows that FXR antagonism by indomethacin induces the phosphorylation of STAT3 (signal transducer and activator of transcription 3), promotes the activation of caspase9, and finally causes DILI. In summary, our systems pharmacology approach provided novel insights into molecular mechanisms of DILI for NSAIDs, which may propel the ways toward the design of novel anti-inflammatory pharmacotherapeutics.