BACKGROUND: Since proteins perform their functions by interacting with one another and with other biomolecules, reconstructing a map of the protein-protein interactions of a cell, experimentally or computationally, is an important first step toward understanding cellular function and machinery of a proteome. Solely derived from the Gene Ontology (GO), we have defined an effective method of reconstructing a yeast protein interaction network by measuring relative specificity similarity (RSS) between two GO terms.
DESCRIPTION: Based on the RSS method, here, we introduce a predicted Saccharomyces protein-protein interaction database called SPIDer. It houses a gold standard positive dataset (GSP) with high confidence level that covered 79.2% of the high-quality interaction dataset. Our predicted protein-protein interaction network reconstructed from the GSPs consists of 92,257 interactions among 3600 proteins, and forms 23 connected components. It also provides general links to connect predicted protein-protein interactions with three other databases, DIP, BIND and MIPS. An Internet-based interface provides users with fast and convenient access to protein-protein interactions based on various search features (searching by protein information, GO term information or sequence similarity). In addition, the RSS value of two GO terms in the same ontology, and the inter-member interactions in a list of proteins of interest or in a protein complex could be retrieved. Furthermore, the database presents a user-friendly graphical interface which is created dynamically for visualizing an interaction sub-network. The database is accessible at http://cmb.bnu.edu.cn/SPIDer/index.html.
CONCLUSION: SPIDer is a public database server for protein-protein interactions based on the yeast genome. It provides a variety of search options and graphical visualization of an interaction network. In particular, it will be very useful for the study of inter-member interactions among a list of proteins, especially the protein complex. In addition, based on the predicted interaction dataset, researchers could analyze the whole interaction network and associate the network topology with gene/protein properties based on a global or local topology view.
