The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.

Background: Interaction data for a given protein may be spread across multiple databases. We set out to create a unifying index that would facilitate searching for these data and that would group together redundant interaction data while recording the methods used to perform this grouping.

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machinereadable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. INTRODUCTIONIn light of the vast scientific resources made available through genomics, the science of deciphering molecular mechanisms is expanding rapidly. Scientists who once hunted for disease genes or sought to distinguish key concepts in evolution are now turning their attention to the details of molecular assembly and mechanism to further understand medicine and the key concepts underlying biology. The Biomolecular Interaction Network Database (BIND) was designed to store complete information about molecular assembly through a database structure in order to archive interactions and reactions arising from biopolymers (protein, RNA and DNA), as well as small molecules, lipids and carbohydrates. Detailed information about molecular mechanism, such as the chemical product(s) of an enzymatic reaction, can be encoded in BIND. The underlying ontology of the BIND database is chemistry, and as such, BIND is capable of storing information about molecular interactions to atomic resolution. The taxonomic scope of BIND is

The immune system can both promote and suppress cancer. Chronic inflammation and proinflammatory cytokines such as interleukin (IL)-1 and IL-6 are considered to be tumour promoting. In contrast, the exact nature of protective antitumour immunity remains obscure. Here, we quantify locally secreted cytokines during primary immune responses against myeloma and B-cell lymphoma in mice. Strikingly, successful cancer immunosurveillance mediated by tumour-specific CD4+ T cells is consistently associated with elevated local levels of both proinflammatory (IL-1α, IL-1β and IL-6) and T helper 1 (Th1)-associated cytokines (interferon-γ (IFN-γ), IL-2 and IL-12). Cancer eradication is achieved by a collaboration between tumour-specific Th1 cells and tumour-infiltrating, antigen-presenting macrophages. Th1 cells induce secretion of IL-1β and IL-6 by macrophages. Th1-derived IFN-γ is shown to render macrophages directly cytotoxic to cancer cells, and to induce macrophages to secrete the angiostatic chemokines CXCL9/MIG and CXCL10/IP-10. Thus, inflammation, when driven by tumour-specific Th1 cells, may prevent rather than promote cancer.

To study proteins in the context of a cellular system, it is essential that the molecules with which a protein interacts are identified and the functional consequence of each interaction is understood. A plethora of resources now exist to capture molecular interaction data from the many laboratories generating