Understanding the cellular mechanisms of platelet activation and their pharmacologic modulation is of major interest for basic and clinical research. Here we introduce a comprehensive human platelet repository (PlateletWeb) for systems biologic analysis of platelets in the functional context of integrated networks. Functional, drug, and pathway associations provide a first systemic insight into various aspects of platelet functionality and pharmacologic regulation. Detailed manual curation of recent platelet proteome and transcriptome studies yielded more than 5000 platelet proteins. Integration of protein-protein interactions with kinase-substrate relationships unraveled the platelet signaling network involving more than 70% of all platelet proteins. Analysis of the platelet kinome in the context of the kinase phylogenetic background revealed an over-representation of tyrosine kinase substrates. The extraction and graphical visualization of specific subnetworks allow identification of all major signaling modules involved in activation and inhibition. An in-depth analysis of DOK1 signaling identifies putative signal modulators of the integrin network. Through integration of various information sources and high curation standards, the PlateletWeb knowledge base offers the systems biologic background for the investigation of signal transduction in human platelets (http://plateletweb.bioapps.biozentrum.uni-wuerzburg.de).

The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure–activity relationships.Database URL: http://drumpid.bioapps.biozentrum.uni-wuerzburg.de

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of proteinprotein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stressrelated kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. Molecular & Cellular Proteomics