Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.comparative analysis ͉ multiple alignment ͉ protein network ͉ yeast two-hybrid A major challenge of postgenomic biology is to understand the complex networks of interacting genes, proteins, and small molecules that give rise to biological form and function. Advances in whole-genome approaches are now enabling us to characterize these networks systematically, by using procedures such as the two-hybrid assay (1) and protein coimmunoprecipitation (2) to screen for protein-protein interactions. To date, these technologies have generated large interaction networks for bacteria (3), yeast (4-7), and, recently, fruit fly (8) and nematode worm (9).The large amount of protein interaction data now available presents opportunities and challenges in understanding evolution and function. Such challenges involve assigning functional roles to interactions (10), separating true protein-protein interactions from false positives (11), and, ultimately, organizing large-scale interaction data into models of cellular signaling and regulatory machinery. As is often the case in biology, an approach based on evolutionary cross-species comparisons provides a valuable framework for addressing these challenges. However, although methods for comparing DNA and protein sequences have been a mainstay of bioinformatics over the past 30 years, development of similar tools at other levels of biological information, including protein interactions (12-14), metabolic networks (15-17), or gene expression data (18)(19)(20), is just beginning.Recently, we devised a method called PATHBLAST (13) for comparing the protein interaction networks of two species. Just as BLAST performs rapid pairwise alignment of protein sequences (21), PATHBLAST is based on efficient alignment of two protein networks to identify conserved network regions. Here, we extend this approach to present a computational framework for alignment and comparison of more than two protein networks. We apply this multiple network alignment strategy to compare the recently available protein networks for worm, fly, and yeast, and show that although any single net...

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications.

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.