Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

SUMMARY Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions which were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably-associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes, and encompass both candidate disease genes and unnanotated proteins to inform on mechanism. Strikingly, whereas larger multi-protein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with 5 or fewer subunits are far more likely to be functionally un-annotated or restricted to vertebrates, suggesting more recent functional innovations.

In this paper we present SFCHECK, a stand-alone software package that features a uni®ed set of procedures for evaluating the structure-factor data obtained from X-ray diffraction experiments and for assessing the agreement of the atomic coordinates with these data. The evaluation is performed completely automatically, and produces a concise PostScript pictorial output similar to that of PROCHECK [Laskowski, MacArthur, Moss & Thornton (1993). J. Appl. Cryst. 26, 283±291], greatly facilitating visual inspection of the results. The required inputs are the structure-factor amplitudes and the atomic coordinates. Having those, the program summarizes relevant information on the deposited structure factors and evaluates their quality using criteria such as data completeness, structure-factor uncertainty and the optical resolution computed from the Patterson origin peak. The dependence of various parameters on the nominal resolution (d spacing) is also given. To evaluate the global agreement of the atomic model with the experimental data, the program recomputes the R factor, the correlation coef®cient between observed and calculated structure-factor amplitudes and R free (when appropriate). In addition, it gives several estimates of the average error in the atomic coordinates. The local agreement between the model and the electron-density map is evaluated on a per-residue basis, considering separately the macromolecule backbone and side-chain atoms, as well as solvent atoms and heterogroups. Among the criteria are the normalized average atomic displacement, the local density correlation coef®cient and the polymer chain connectivity. The possibility of computing these criteria using the omit-map procedure is also provided. The described software should be a valuable tool in monitoring the re®nement procedure and in assessing structures deposited in databases.

CAPRI is a communitywide experiment to assess the capacity of protein-docking methods to predict protein-protein interactions. Nineteen groups participated in rounds 1 and 2 of CAPRI and submitted blind structure predictions for seven protein-protein complexes based on the known structure of the component proteins. The predictions were compared to the unpublished X-ray structures of the complexes. We describe here the motivations for launching CAPRI, the rules that we applied to select targets and run the experiment, and some conclusions that can already be drawn. The results stress the need for new scoring functions and for methods handling the conformation changes that were observed in some of the target systems. CAPRI has already been a powerful drive for the community of computational biologists who development docking algorithms. We hope that this issue of Proteins will also be of interest to the community of structural biologists, which we call upon to provide new targets for future rounds of CAPRI, and to all molecular biologists who view protein-protein recognition as an essential process. Proteins 2003;52: 2-9.