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...

We present a new version of the Protein-Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound-unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the complexes in this version. The new benchmark consists of 72 unbound-unbound cases, with 52 rigid-body cases, 13 medium-difficulty cases, and 7 high-difficulty cases with substantial conformational change. In addition, we retained 12 antibody-antigen test cases with the antibody structure in the bound form. The new benchmark provides a platform for evaluating the progress of docking methods on a wide variety of targets. The new version of the benchmark is available to the public at http://zlab.bu.edu/benchmark2.

The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.

Protein phosphorylation is a key regulatory event in most cellular processes and development. Mass spectrometry-based proteomics provides a framework for the large-scale identification and characterization of phosphorylation sites. Here, we used a well-established phosphopeptide enrichment and identification strategy including the combination of strong cation exchange chromatography, immobilized metal affinity chromatography, and high-accuracy mass spectrometry instrumentation to study phosphorylation in developing Drosophila embryos. In total, 13 720 different phosphorylation sites were discovered from 2702 proteins with an estimated false-discovery rate (FDR) of 0.63% at the peptide level. Because of the large size of the data set, both novel and known phosphorylation motifs were extracted using the Motif-X algorithm, including those representative of potential ordered phosphorylation events.

We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, SCOP1 (Structural Classification of Proteins) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark.