Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

Roles for the two-hybrid system in exploration of the yeast protein interactome

Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein complexes. While these interaction data have led to a number of novel findings and the emergence of a single huge network containing thousands of proteins, they suffer many false signals and fall short of grasping the entire interactome. Thus, continuous efforts are necessary in both bioinformatics and experimentation to fully exploit these data and to proceed another step forward to the goal. Computational tools to integrate existing biological knowledge buried in literature and various functional genomic data with the interactome data are required for biological interpretation of the huge protein interaction network. Novel experimental methods have to be developed to detect weak, transient interactions involving low abundance proteins as well as to obtain clues to the biological role for each interaction. Since the yeast two-hybrid system can be used for the mapping of the interaction domains and the isolation of interaction-defective mutants, it would serve as a technical basis for the latter purpose, thereby playing another important role in the next phase of protein interactome research.     

The protein–protein interaction network of the human Sirtuin family

Protein–protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein–protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.     

Interactome Analysis of KIN (Kin17) Shows New Functions of This Protein

KIN (Kin17) protein is overexpressed in a number of cancerous cell lines, and is therefore considered a possible cancer biomarker. It is a well-conserved protein across eukaryotes and is ubiquitously expressed in all cell types studied, suggesting an important role in the maintenance of basic cellular function which is yet to be well determined. Early studies on KIN suggested that this nuclear protein plays a role in cellular mechanisms such as DNA replication and/or repair; however, its association with chromatin depends on its methylation state. In order to provide a better understanding of the cellular role of this protein, we investigated its interactome by proximity-dependent biotin identification coupled to mass spectrometry (BioID-MS), used for identification of protein–protein interactions. Our analyses detected interaction with a novel set of proteins and reinforced previous observations linking KIN to factors involved in RNA processing, notably pre-mRNA splicing and ribosome biogenesis. However, little evidence supports that this protein is directly coupled to DNA replication and/or repair processes, as previously suggested. Furthermore, a novel interaction was observed with PRMT7 (protein arginine methyltransferase 7) and we demonstrated that KIN is modified by this enzyme. This interactome analysis indicates that KIN is associated with several cell metabolism functions, and shows for the first time an association with ribosome biogenesis, suggesting that KIN is likely a moonlight protein.     

Viruses and interactomes in translation

A decade of high-throughput screenings for intraviral and virus-host protein-protein interactions led to the accumulation of data and to the development of theories on laws governing interactome organization for many viruses. We present here a computational analysis of intraviral protein networks (EBV, FLUAV, HCV, HSV-1, KSHV, SARS-CoV, VACV, and VZV) and virus-host protein networks (DENV, EBV, FLUAV, HCV, and VACV) from up-to-date interaction data, using various mathematical approaches. If intraviral networks seem to behave similarly, they are clearly different from the human interactome. Viral proteins target highly central human proteins, which are precisely the Achilles' heel of the human interactome. The intrinsic structural disorder is a distinctive feature of viral hubs in virus-host interactomes. Overlaps between virus-host data sets identify a core of human proteins involved in the cellular response to viral infection and in the viral capacity to hijack the cell machinery for viral replication. Host proteins that are strongly targeted by a virus seem to be particularly attractive for other viruses. Such protein-protein interaction networks and their analysis represent a powerful resource from a therapeutic perspective.     

Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets

ABSTRACT: Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false positive and false negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer-related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu/     

Identifying protein complexes from heterogeneous biological data

With the increasing availability of diverse biological information for proteins, integration of heterogeneous data becomes more useful for many problems in proteomics, such as annotating protein functions, predicting novel protein–protein interactions and so on. In this paper, we present an integrative approach called InteHC (Inte grative H ierarchical C lustering) to identify protein complexes from multiple data sources. Although integrating multiple sources could effectively improve the coverage of current insufficient protein interactome (the false negative issue), it could also introduce potential false‐positive interactions that could hurt the performance of protein complex prediction. Our proposed InteHC method can effectively address these issues to facilitate accurate protein complex prediction and it is summarized into the following three steps. First, for each individual source/feature, InteHC computes the matrices to store the affinity scores between a protein pair that indicate their propensity to interact or co‐complex relationship. Second, InteHC computes a final score matrix, which is the weighted sum of affinity scores from individual sources. In particular, the weights indicating the reliability of individual sources are learned from a supervised model (i.e., a linear ranking SVM). Finally, a hierarchical clustering algorithm is performed on the final score matrix to generate clusters as predicted protein complexes. In our experiments, we compared the results collected by our hierarchical clustering on each individual feature with those predicted by InteHC on the combined matrix. We observed that integration of heterogeneous data significantly benefits the identification of protein complexes. Moreover, a comprehensive comparison demonstrates that InteHC performs much better than 14 state‐of‐the‐art approaches. All the experimental data and results can be downloaded from http://www.ntu.edu.sg/home/zhengjie/data/InteHC . Proteins 2013; 81:2023–2033. © 2013 Wiley Periodicals, Inc.     

New insights into protein-protein interaction data lead to increased estimates of the <Emphasis Type="Italic">S. cerevisiae</Emphasis> interactome size

Background  

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.     

Large-scale de novo prediction of physical protein-protein association

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org.     

Interactive three-dimensional visualization and contextual analysis of protein interaction networks

To understand the biology of the interactome, the covisualization of protein interactions and other protein-related data is required. In this study, we have adapted a 3-D network visualization platform, GEOMI, to allow the coanalysis of protein-protein interaction networks with proteomic parameters such as protein localization, abundance, physicochemical parameters, post-translational modifications, and gene ontology classification. Working with Saccharomyces cerevisiae data, we show that rich and interactive visualizations, constructed from multidimensional orthogonal data, provide insights on the complexity of the interactome and its role in biological processes and the architecture of the cell. We present the first organelle-specific interaction networks, that provide subinteractomes of high biological interest. We further present some of the first views of the interactome built from a new combination of yeast two-hybrid data and stable protein complexes, which are likely to approximate the true workings of stable and transient aspects of the interactome. The GEOMI tool and all interactome data are freely available by contacting the authors.     

Global protein interactome exploration through mining genome-scale data in Arabidopsis thaliana

Background

Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant.

Results

We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage.

Conclusions

Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions.

     

Yeast two-hybrid contributions to interactome mapping

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.     

Chaperones as integrators of cellular networks: changes of cellular integrity in stress and diseases

The complex integrity of the cells and its sudden, but often predictable changes can be described and understood by the topology and dynamism of cellular networks. All these networks undergo both local and global rearrangements during stress and development of diseases. Here, we illustrate this by showing the stress-induced structural rearrangement of the yeast protein-protein interaction network (interactome). In an unstressed state, the yeast interactome is highly compact, and the centrally organized modules have a large overlap. During stress, several original modules became more separated, and a number of novel modules also appear. A few basic functions such as theproteasome preserve their central position; however, several functions with high energy demand, such the cell-cycle regulation loose their original centrality during stress. A number of key stress-dependent protein complexes, such as the disaggregation-specific chaperone, Hsp104 gain centrality in the stressed yeast interactome. Molecular chaperones, heat shock, or stress proteins became established as key elements in our molecular understanding of the cellular stress response. Chaperones form complex interaction networks (the chaperome) with each other and their partners. Here, we show that the human chaperome recovers the segregation of protein synthesis-coupled and stress-related chaperones observed in yeast recently. Examination of yeast and human interactomes shows that chaperones 1) are intermodular integrators of protein-protein interaction networks, which 2) often bridge hubs and 3) are favorite candidates for extensive phosphorylation. Moreover, chaperones 4) become more central in the organization of the isolated modules of the stressed yeast protein-protein interaction network, which highlights their importance in the decoupling and recoupling of network modules during and after stress. Chaperone-mediated evolvability of cellular networks may play a key role in cellular adaptation during stress and various polygenic and chronic diseases, such as cancer, diabetes or neurodegeneration.     

Choice of biological source material supersedes oxidative stress in its influence on DJ-1 in vivo interactions with Hsp90

DJ-1 is a small but relatively abundant protein of unknown function that may undergo stress-dependent cellular translocation and has been implicated in both neurodegenerative diseases and cancer. As such, DJ-1 may be an excellent study object to elucidate the relative influence of the cellular context on its interactome and for exploring whether acute exposure to oxidative stressors alters its molecular environment. Using quantitative mass spectrometry, we conducted comparative DJ-1 interactome analyses from in vivo cross-linked brains or livers and from hydrogen peroxide-treated or na?ve embryonic stem cells. The analysis identified a subset of glycolytic enzymes, heat shock proteins 70 and 90, and peroxiredoxins as interactors of DJ-1. Consistent with a role of DJ-1 in Hsp90 chaperone biology, we document destabilization of Hsp90 clients in DJ-1 knockout cells. We further demonstrate the existence of a C106 sulfinic acid modification within DJ-1 and thereby establish that this previously inferred modification also exists in vivo. Our data suggest that caution has to be exerted in interpreting interactome data obtained from a single biological source material and identify a role of DJ-1 as an oxidative stress sensor and partner of a molecular machinery notorious for its involvement in cell fate decisions.     

Structure homology and interaction redundancy for discovering virus–host protein interactions

Virus–host interactomes are instrumental to understand global perturbations of cellular functions induced by infection and discover new therapies. The construction of such interactomes is, however, technically challenging and time consuming. Here we describe an original method for the prediction of high‐confidence interactions between viral and human proteins through a combination of structure and high‐quality interactome data. Validation was performed for the NS1 protein of the influenza virus, which led to the identification of new host factors that control viral replication.     

Visualization and analysis of the complexome network of Saccharomyces cerevisiae

Most processes in the cell are delivered by protein complexes, rather than individual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes; these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.     

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific "bait" protein and its associated "prey" proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.     

Systematic prediction of human membrane receptor interactions

Membrane receptor‐activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high‐throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system‐wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.     

A predicted protein-protein interaction network of the filamentous fungus Neurospora crassa

The filamentous fungus Neurospora crassa is a leading model organism for circadian clock studies. Computational identification of a protein-protein interaction (PPI) network (also known as an interactome) in N. crassa can provide new insights into the cellular functions of proteins. Using two well-established bioinformatics methods (the interolog method and the domain interaction-based method), we predicted 27,588 PPIs among 3006 N. crassa proteins. To the best of our knowledge, this is the first identified interactome for N. crassa, although it remains problematic because of incomplete interactions and false positives. In particular, the established PPI network has provided clues to further decipher the molecular mechanism of circadian rhythmicity. For instance, we found that clock-controlled genes (ccgs) are more likely to act as bottlenecks in the established PPI network. We also identified an important module related to circadian oscillators, and some functional unknown proteins in this module may serve as potential candidates for new oscillators. Finally, all predicted PPIs were compiled into a user-friendly database server (NCPI), which is freely available at .