Jessica Ahmed

SuperPred: drug classification and target prediction

The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Availability: the system is freely accessible at http://bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License.

CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge

During the development of methods for cancer diagnosis and treatment, a vast amount of information is generated. Novel cancer target proteins have been identified and many compounds that activate or inhibit cancer-relevant target genes have been developed. This knowledge is based on an immense number of experimentally validated compound–target interactions in the literature, and excerpts from literature text mining are spread over numerous data sources. Our own analysis shows that the overlap between important existing repositories such as Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), Pharmacogenomics Knowledge Base (PharmGKB) and DrugBank as well as between our own literature mining for cancer-annotated entries is surprisingly small. In order to provide an easy overview of interaction data, it is essential to integrate this information into a single, comprehensive data repository. Here, we present CancerResource, a database that integrates cancer-relevant relationships of compounds and targets from (i) our own literature mining and (ii) external resources complemented with (iii) essential experimental and supporting information on genes and cellular effects. In order to facilitate an overview of existing and supporting information, a series of novel information connections have been established. CancerResource addresses the spectrum of research on compound–target interactions in natural sciences as well as in individualized medicine; CancerResource is available at: http://bioinformatics.charite.de/cancerresource/.

SuperSweet—a resource on natural and artificial sweetening agents

A vast number of sweet tasting molecules are known, encompassing small compounds, carbohydrates, d-amino acids and large proteins. Carbohydrates play a particularly big role in human diet. The replacement of sugars in food with artificial sweeteners is common and is a general approach to prevent cavities, obesity and associated diseases such as diabetes and hyperlipidemia. Knowledge about the molecular basis of taste may reveal new strategies to overcome diet-induced diseases. In this context, the design of safe, low-calorie sweeteners is particularly important. Here, we provide a comprehensive collection of carbohydrates, artificial sweeteners and other sweet tasting agents like proteins and peptides. Additionally, structural information and properties such as number of calories, therapeutic annotations and a sweetness-index are stored in SuperSweet. Currently, the database consists of more than 8000 sweet molecules. Moreover, the database provides a modeled 3D structure of the sweet taste receptor and binding poses of the small sweet molecules. These binding poses provide hints for the design of new sweeteners. A user-friendly graphical interface allows similarity searching, visualization of docked sweeteners into the receptor etc. A sweetener classification tree and browsing features allow quick requests to be made to the database. The database is freely available at: http://bioinformatics.charite.de/sweet/.

SOAP/WSDL-Based Web Services for Biomedicine: Demonstrating the Technique with the CancerResource

Web services provide programmatic access to data or tools using internet technology. Several standards have been developed, one sophisticated application is the combina- tion of the Web Service Description Language (WSDL) with the SOAP (originally defined as Simple Object Access Proto- col) messaging protocol. We describe the fundamental technol- ogy of SOAP/WSDL-based web services and provide concepts to integrate data from independent, distant resources. Several informatics layers are described such as server-client messag- ing connections, programming languages, libraries, and modu- larity of web services. We illustrate principles underlying this technology and the types of web services they can be applied to in several functional stages, from simple data retrievals over combined data accesses (workflows) up to dynamically ren- dered images. As an example that is relevant for biomedicine we highlight the CancerResource as a use case for such diversi- fied applications. The CancerResource is conceptualized to present cancer- relevant drug-target connections to the life sciences, particularly to the field of medical science. In-depth literature data-mining resolved thousands of drug-target connections. CancerResource connects manifold information from different knowledge catego- ries. Targets are genes or proteins, which are well described in public databases like UniProt, Ensembl, and the PDB. Drugs are chemical compounds that specifically act on target genes in can- cer tissues; they are collected in databases like DrugBank, Su- perDrug, or PubChem. Connectivity Map (C-Map) expression data is an essential source of functional information for Cancer- Resource. A general visualization feature is the organization of genes in pathways; the KEGG database provides cancer-specific pathway maps. For this purpose CancerResource utilizes web service technologies to dynamically combine C-Map expression data with pathways. The CancerResource web interface allows the user to specify problems and helps to understand noticeable behaviors of genes that are implicated in cancer. The ultimate aim of CancerResource is to provide suggestions towards devel- oping specific drug therapies for cancer patients.

FragmentStore—a comprehensive database of fragments linking metabolites, toxic molecules and drugs

Consideration of biomolecules in terms of their molecular building blocks provides valuable new information regarding their synthesis, degradation and similarity. Here, we present the FragmentStore, a resource for the comparison of fragments found in metabolites, drugs or toxic compounds. Starting from 13 000 metabolites, 16 000 drugs and 2200 toxic compounds we generated 35 000 different building blocks (fragments), which are not only relevant to their biosynthesis and degradation but also provide important information regarding side-effects and toxicity. The FragmentStore provides a variety of search options such as 2D structure, molecular weight, rotatable bonds, etc. Various analysis tools have been implemented including the calculation of amino acid preferences of fragments’ binding sites, classification of fragments based on the enzyme classification class of the enzyme(s) they bind to and small molecule library generation via a fragment-assembler tool. Using the FragmentStore, it is now possible to identify the common fragments of different classes of molecules and generate hypotheses about the effects of such intersections. For instance, the co-occurrence of fragments in different drugs may indicate similar targets and possible off-target interactions whereas the co-occurrence of fragments in a drug and a toxic compound/metabolite could be indicative of side-effects. The database is publicly available at: http://bioinformatics.charite.de/fragment_store.

Cellular Fingerprints: A Novel Concept for the Integration of Experimental Data and Compound-Target-Pathway Relations (Extended Abstract)

The pharmaceutical industry is hunting for high-affinity inhibitors of medical targets, but most of them fail in clinical trials because of severe side effects. On the other hand, there is a growing knowledge about multiple targets and their role in various signalling pathways. Therefore, the integration of experimental data, literature knowledge about drugs, targets, their metabolism, ontology, and related pathways is an important task to achieve better understanding of drug mechanisms on a systems biological level.