The intracellular antibody capture technology: towards the high-throughput selection of functional intracellular antibodies for target validation.

Several approaches have been developed over the past decade to study the complex interactions that occur in biological system. The ability to carry out a comprehensive genetic analysis of an organism becomes more limited and difficult as the complexity of the organism increases because complex organisms are likely to have not only more genes than simple organisms but also more elaborate networks of interactions among those genes. The development of technologies to systematically disrupt protein networks at the genomic scale would greatly accelerate the comprehensive understanding of the cell as molecular machinery. Intracellular antibodies (intrabodies) can be targeted to different intracellular compartments to specifically interfere with function of selected intracellular gene products in mammalian cells. This technique should prove important for studies of mammalian cells, where genetic approaches are more difficult. In the context of large-scale protein interaction mapping projects, intracellular antibodies (ICAbs) promise to be an important tool to knocking out protein function inside the cell. In this context, however, the need for speed and high throughput requires the development of simple and robust methods to derive antibodies which function within cells, without the need for optimization of each individual ICAb. The successful inhibition of biological processes by intrabodies has been demonstrated in a number of different cells. The performance of antibodies that are intracellularly expressed is, however, somewhat unpredictable, because the reducing environment of the cell cytoplasm in which they are forced to work prevents some antibodies, but not others, to fold properly. For this reason, we have developed an in vivo selection procedure named Intracellular Antibody Capture Technology (IACT) that allows the isolation of functional intrabodies. The IAC technology has been used for the rapid identification of antigen-antibody pairs in intracellular compartments and for the in vivo identification of epitopes recognized by the selected intracellular antibodies. Several optimizations of the IAC technology for protein knock-out have been developed so far. This system offers a powerful and versatile proteomic tool to dissect diverse functional properties of cellular proteins in different cell lines.

MINT: a Molecular INTeraction database.

Protein interaction databases represent unique tools to store, in a computer readable form, the protein interaction information disseminated in the scientific literature. Well organized and easily accessible databases permit the easy retrieval and analysis of large interaction data sets. Here we present MINT, a database (http://cbm.bio.uniroma2.it/mint/index.html) designed to store data on functional interactions between proteins. Beyond cataloguing binary complexes, MINT was conceived to store other types of functional interactions, including enzymatic modifications of one of the partners. Release 1.0 of MINT focuses on experimentally verified protein-protein interactions. Both direct and indirect relationships are considered. Furthermore, MINT aims at being exhaustive in the description of the interaction and, whenever available, information about kinetic and binding constants and about the domains participating in the interaction is included in the entry. MINT consists of entries extracted from the scientific literature by expert curators assisted by 'MINT Assistant', a software that targets abstracts containing interaction information and presents them to the curator in a user-friendly format. The interaction data can be easily extracted and viewed graphically through 'MINT Viewer'. Presently MINT contains 4568 interactions, 782 of which are indirect or genetic interactions.

A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules.

Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation.