Motivation: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein--protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. Results: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs. Contact: dgough@bioeng.ucsd.edu

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Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between

Abstract. Nup116p is a member of a family of five yeast nuclear pore complex (NPC) proteins that share an amino terminal region of repetitive tetrapeptide "GLFG " motifs. Previous experiments characterized the unique morphological perturbations that occur in a nup116 null mutant: temperature-sensitive formation of nuclear envelope seals over the cytoplasmic face of the NPC (Wente, S. R., and G. Blobel. 1993. J. Cell Biol. 123:275-284). Three approaches have been taken to dissect the structural basis for Nupll6p's role in NPC function. First, deletion mutagenesis analysis of NUPll6 revealed that the GLFG region was required for NPC function. This was not true for the other four yeast GLFG family members (Nup49p, Nup57p, Nupl00p, and Nup145p). Moreover, deletion of either

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this paper, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-theart approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can a) produce improved biologically significant functional groupings; and b) facilitate soft clustering by discovering multiple functional associations for proteins. 1.

Many tools exist for visually exploring biological networks including well-known examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of these tools is to go beyond “static” representations of cellular state, towards a more dynamic model of cellular processes through the incorporation of gene expression data, subcellular localization information, and time-dependent behavior. We provide a comprehensive review of the relative advantages and disadvantages of existing systems with two goals in mind: to aid researchers in efficiently identifying the appropriate existing tools for data visualization; to describe the necessary and realistic goals for the next generation of visualization tools. In view of the first goal, we provide in the Supplementary Materials a systematic comparison of more than 35 existing tools in terms of over 25 different features.

Abstract. Import of newly synthesized PTS1 proteins into the peroxisome requires the PTS1 receptor (Pex5p), a predominantly cytoplasmic protein that cycles between the cytoplasm and peroxisome. We have identified Pex13p, a novel integral peroxisomal membrane from both yeast and humans that binds the PTS1 receptor via a cytoplasmically oriented SH3 domain. Although only a small amount of Pex5p is bound to peroxisomes at steady state (<5%), loss of Pex13p fur-ther reduces the amount of peroxisome-associated Pex5p by ~40-fold. Furthermore, loss of Pex13p eliminates import of peroxisomal matrix proteins that contain either the type-1 or type-2 peroxisomal targeting signal but does not affect targeting and insertion of integral peroxisomal membrane proteins. We conclude

Abstract. We identified a Saccharomyces cerevisiae peroxisomal membrane protein, Pexl3p, that is essential for protein import. A point mutation in the COOHterminal Src homology 3 (SH3) domain of Pexl3p inactivated the protein but did not affect its membrane targeting. A two-hybrid screen with the SH3 domain of Pexl3p identified Pex5p, a receptor for proteins with a type I peroxisomal targeting signal (PTS1), as its ligand. Pexl3p SH3 interacted specifically with Pex5p in vitro. We determined, furthermore, that Pex5p was mainly present in the cytosol and only a small fraction was associated with peroxisomes. We therefore propose that Pex13p is a component of the peroxisomal protein import machinery onto which the mobile Pex5p receptor docks for the delivery of the selected PTS1 protein.

Abstract. Coiled bodies are small nuclear organelles that are highly enriched in small nuclear RNAs, and that have long been thought to be associated with the nucleolus. Here we use mutational analysis, transient transfections, and the yeast two-hybrid system to show that the nucleolar phosphoprotein Nopp140 functions as a molecular link between the two prominent nuclear organelles. Exogenous Nopp140 accumulated in the nucleolus rapidly, but only after a lag phase in coiled bodies, suggesting a pathway between the two organelles. The expression of partial Nopp140 constructs exerted dominant negative effects on the endogenous Nopp140 by chasing it and other antigens that were common to both organelles out of the nucleolus. The alternating positively and negatively charged repeat domain of Nopp140 was required for targeting to both organelles. In addition, partial Nopp140 constructs caused formation of novel structures in the nucleoplasm and, in the case of the conserved carboxy terminus, led to the dispersal of coiled bodies. As a final link, we identified the coiled body–specific protein p80 coilin in a yeast two-hybrid screen with Nopp140. The interaction of the two proteins was confirmed by coimmunoprecipitation. Taken together, Nopp140 appeared to shuttle between the nucleolus and the coiled bodies, and to chaperone the transport of other molecules. Key words: nucleolus • coiled bodies • protein transport • protein interaction • transfection