SUMMARY Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. Finally, BioPlex, in combination with other approaches can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial Amyotrophic Lateral Sclerosis perturb a defined community of interactors.

SUMMARY Determining the composition of protein complexes is an essential step towards understanding the cell as an integrated system. Using co-affinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly five thousand individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a novel statistical framework to define individual protein-protein interactions, led to the generation of a Drosophila Protein interaction Map (DPiM) encompassing 556 protein complexes. The high quality of DPiM and its usefulness as a paradigm for metazoan proteomes is apparent from the recovery of many known complexes, significant enrichment for shared functional attributes and validation in human cells. DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

The protein modifier ubiquitin is a signal for proteasome-mediated degradation in eukaryotes.

Recent analyses of high-throughput protein interaction data coupled with large-scale investigations of evolutionary properties of interaction networks have left some unanswered questions. To what extent do protein interactions act as constraints during evolution of the protein sequence? How does the type of interaction, specifically transient or obligate, play into these constraints? Are the mutations in the binding site of an interacting protein correlated with mutations in the binding site of its partner? We address these and other questions by relying on a carefully curated dataset of protein complex structures. Results point to the importance of distinguishing between transient and obligate interactions. We conclude that residues in the interfaces of obligate complexes tend to evolve at a relatively slower rate, allowing them to coevolve with their interacting partners. In contrast, the plasticity inherent in transient interactions leads to an increased rate of substitution for the interface residues and leaves little or no evidence of correlated mutations across the interface.interaction networks ͉ obligate interactions ͉ protein interactions ͉ protein recognition ͉ transient interactions T he recent debate on the degree of constraint that proteinprotein interactions confer on protein evolution (1-4) has highlighted the problems of reliability in high-throughput interaction data and the processing and interpretation of those data. With increasing amounts of data on protein-protein interactions for several species as well as the emphasis on representing and understanding basic biological processes in terms of networks of interactions, it is important to focus on the precise definition and classification of these underlying interactions. Some computational analyses tend to group together disparate datasets originating from different experimental methods to get more robust answers (5), which sometimes tends to blur the definitions of the nodes and edges of the merged networks. Although the simplest approach to networks as sets of binary interactions provides some rudimentary understanding of the data, the more realistic and nuanced view in terms of modular complexes and subcomplex structures is needed, and such characterizations have recently started appearing in the literature (6).An important distinction between transient and obligate protein-protein interactions, overlooked in many studies, has important implications for the construction of protein interaction networks. Constructing a network with each node representing a single protein sequence is hardly realistic from a biological perspective. It is well known that many proteins exist as parts of permanent obligate complexes such as multisubunit enzymes, which may often fold and bind simultaneously (7,8). Other interactions are fleeting encounters between single proteins or the aforementioned larger complexes (9). These often include complexes involved in enzyme-inhibitor, enzymesubstrate, hormone-receptor, and signaling-effector types of interactions. Th...