Abstract: The analysis reported here describes detailed structural studies of endothiapepsin (the aspartic proteinase from Endothia parasitica), with and without bound inhibitors, and human pepsin 3b. Comparison of multiple crystal structures of members of the aspartic proteinase family has revealed small but significant differences in domain orientation in different crystal forms. In this paper, it is shown that these differences in domain orientation do not necessarily correlate with the presence or absence of bound inhibitors, but appear to stem at least partly from crystal contacts mediated by sulfate ions. However, since the same inherent flexibility of the structure is observed for other enzymes in this family such as human pepsin, the native structure of which is also reported here, the observed domain movements may well have implications for the mechanism of catalysis.
Abstract: A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F(1) score of 0.70 indicating a potential useful application of the corpus.
Abstract: Foreign substances can have a dramatic and unpredictable adverse effect on human health. In the development of new therapeutic agents, it is essential that the potential adverse effects of all candidates be identified as early as possible. The field of predictive toxicology strives to profile the potential for adverse effects of novel chemical substances before they occur, both with traditional in vivo experimental approaches and increasingly through the development of in vitro and computational methods which can supplement and reduce the need for animal testing. To be maximally effective, the field needs access to the largest possible knowledge base of previous toxicology findings, and such results need to be made available in such a fashion so as to be interoperable, comparable, and compatible with standard toolkits. This necessitates the development of open, public, computable, and standardized toxicology vocabularies and ontologies so as to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. Such ontology development will support data management, model building, integrated analysis, validation and reporting, including regulatory reporting and alternative testing submission requirements as required by guidelines such as the REACH legislation, leading to new scientific advances in a mechanistically-based predictive toxicology. Numerous existing ontology and standards initiatives can contribute to the creation of a toxicology ontology supporting the needs of predictive toxicology and risk assessment. Additionally, new ontologies are needed to satisfy practical use cases and scenarios where gaps currently exist. Developing and integrating these resources will require a well-coordinated and sustained effort across numerous stakeholders engaged in a public-private partnership. In this communication, we set out a roadmap for the development of an integrated toxicology ontology, harnessing existing resources where applicable. We describe the stakeholders' requirements analysis from the academic and industry perspectives, timelines, and expected benefits of this initiative, with a view to engagement with the wider community.
Abstract: The field of predictive toxicology requires the development of open, public, computable, standardized toxicology vocabularies and ontologies to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. In this article we review ontology developments based on a set of perspectives showing how ontologies are being used in predictive toxicology initiatives and applications. Perspectives on resources and initiatives reviewed include OpenTox, eTOX, Pistoia Alliance, ToxWiz, Virtual Liver, EU-ADR, BEL, ToxML, and Bioclipse. We also review existing ontology developments in neighboring fields that can contribute to establishing an ontological framework for predictive toxicology. A significant set of resources is already available to provide a foundation for an ontological framework for 21st century mechanistic-based toxicology research. Ontologies such as ToxWiz provide a basis for application to toxicology investigations, whereas other ontologies under development in the biological, chemical, and biomedical communities could be incorporated in an extended future framework. OpenTox has provided a semantic web framework for the implementation of such ontologies into software applications and linked data resources. Bioclipse developers have shown the benefit of interoperability obtained through ontology by being able to link their workbench application with remote OpenTox web services. Although these developments are promising, an increased international coordination of efforts is greatly needed to develop a more unified, standardized, and open toxicology ontology framework.
Abstract: The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested.
Abstract: The rapid development of molecular toxicology is providing innovative approaches to an improved investigation and recognition of toxic substances. Proteome analysis offers, with 2DE/MS (two-dimensional gel electrophoresis and mass spectrometry) and SELDI (surface enhanced laser desorption/ionisation), a promising discipline to classify molecular changes caused by toxic exposure. The Rat Liver Foci Bioassay (RLFB) is a detailed, well-described model for the investigation of liver carcinogenesis induced by chemical substances. Based on this model, we examined whether proteomic methods of molecular toxicology can be used for the early recognition of toxic and/or carcinogenic characteristics of toxic substances. In addition, identification and subsequent prevalidation of new hepatocellular biomarkers was performed, enabling better prediction of toxic and/or carcinogenic effects. This could lead to a more meaningful RLFB and thus to an improved risk assessment of chemicals. 2DE analysis in this study showed that deregulated proteins are assigned to mainly anabolic and catabolic metabolism pathways in the cell. Beyond this, individual proteins were identified which play a key role in the carcinogenic process. A comparison of the differentially expressed proteins in tissue from tumour-bearing animals and tissue derived from the start of the study revealed that protein expression changes (biomarkers) were already detectable shortly after exposure. In addition, analysis by SELDI clearly showed several differentially expressed proteins and/or derived masses. The spectra represented specific differences in tissues, which could be assigned to the same histopathological endpoints. With bioinformatics analysis it was possible to identify individual discriminating mass peaks, which were indicative of tumour formation. Group specific changes can be illustrated and/or represented in more detail with further cluster analysis methods. These results give hope for an improved prediction of hepatotoxicity and carcinogenicity by means of protein markers, which could in the future lead to a shortening of carcinogenicity studies and to a reduction in the use of experimental animals.
Abstract: The novel genistein (G) derivative, 6-carboxymethyl genistein (CG) was evaluated for its biological properties in comparison with G. Both compounds showed oestrogenic activity in vitro and in vivo. On the other hand G and CG differed in the following parameters: (i) only CG displayed mixed agonist-antagonist activity for oestrogen receptor (ER) alpha in transactivation assays and (ii) only CG was capable of attenuating oestrogen (E(2))-induced proliferation in vascular smooth muscle cells and of inhibiting oestrogen-induced creatine kinase (CK) specific activity in rat tissues. On the other hand only G enhanced the stimulatory effect on CK specific activity in the uterus. In comparison to the selective oestrogen receptor modulator (SERM) raloxifene (RAL), CG showed the same selectivity profile as RAL in blocking the CK response to E(2) in tissues derived from both immature and ovariectomized female rats. Molecular modelling of CG bound to the ligand binding domain (LBD) of ERbeta predicts that the 6-carboxymethyl group of CG almost fits the binding cavity. On the other hand, molecular modelling of CG bound to the LBD of ERalpha suggests that the carboxyl group of CG may perturb the end of Helix 11, eliciting a severe backbone change for Leu 525, and consequently induces a conformational change which could position Helix 12 in an antagonist conformation. This model supports the experimental findings that CG can act as a mixed agonist-antagonist when E(2) is bound to its receptors. Collectively, our findings suggest that CG can be considered a novel SERM with unique effects on the vasculature, bone and uterus.
Abstract: Ontologies are specifications of the concepts in a given field, and of the relationships among those concepts. The development of ontologies for molecular-biology information and the sharing of those ontologies within the bioinformatics community are central problems in bioinformatics. If the bioinformatics community is to share ontologies effectively, ontologies must be exchanged in a form that uses standardized syntax and semantics. This paper reports on an effort among the authors to evaluate alternative ontology-exchange languages, and to recommend one or more languages for use within the larger bioinformatics community. The study selected a set of candidate languages, and defined a set of capabilities that the ideal ontology-exchange language should satisfy. The study scored the languages according to the degree to which they satisfied each capability. In addition, the authors performed several ontology-exchange experiments with the two languages that received the highest scores: OML and Ontolingua. The result of those experiments, and the main conclusion of this study, was that the frame-based semantic model of Ontolingua is preferable to the conceptual graph model of OML, but that the XML-based syntax of OML is preferable to the Lisp-based syntax of Ontolingua.
Abstract: To date, many of the current criteria for diagnosis of somatotropin (growth hormone, GH) deficiency have been based upon measurement of this hormone by competitive radioimmunoassay (RIA) with use of polyclonal antibodies. In recent years, however, the development of hybridoma technology has led to the generation of various monoclonal antibodies (Mabs) to GH with different affinities and epitope specificities. Subsequently, these reagents have been used in the development of noncompetitive two-site immunometric assays (e.g., immunoradiometric assay; IRMA). In general, the values obtained for serum GH by IRMA have been lower than those obtained by RIA, because of the epitope-specificity profile of the Mabs in the IRMA. Attempting to obtain GH values numerically similar to those by RIA, we used a combination of Mabs to GH in developing and evaluating a two-site time-resolved immunofluorometric assay (IFMA) based on the streptavidin-biotin interaction. Fluorescence is proportional to concentration of analyte and is linearly related to concentration over the range 0.3 to 40 micrograms/L. The assay was satisfactory with respect to sensitivity, accuracy, and precision (CV less than 10% over the entire working range). In addition, the concentration of GH was determined by the IFMA and a competitive RIA in serum obtained from GH deficient and acromegalic patients. The pairing of antibodies in the IFMA gave numerical values that agreed well with those by RIA (r = 0.97; n = 100).
