Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

ModuleSearch: finding functional modules in a protein-protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein-protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein-protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Social behavior of the yeast protein-protein interaction network

Protein-protein interaction networks are useful in contextual annotation of protein function and in general to achieve a system-level understanding of cellular behavior. This work reports on the social behavior of the yeast protein-protein interaction network and concludes that it is non-random. This work, while providing an analysis of organization of genes into functional societies, can potentially be useful in assessing the accuracy of contextual gene annotation based on such interaction networks.     

Detecting protein-protein interaction in live yeast by flow cytometry.

BACKGROUND: The yeast Saccharomyces cerevisiae is the most commonly used organism for studying protein- protein interactions. In this report we demonstrate the use of flow cytometry in observing fluorescence resonance energy transfer (FRET) between cyan and yellow fluorescent fusion proteins (CFP and YFP, respectively) as a marker for protein interaction in live yeast cells. Probability binning is also employed to provide a statistical confirmation of our observations. METHODS: We coexpressed CFP and YFP fusions containing the N-terminal transmembrane domain (NTM) of Tom70p in yeast and analyzed FRET in live cells with a multilaser flow cytometer. The Tom70p NTM was previously shown to be sufficient for mitochondrial localization and protein-protein interaction (Millar and Shore, 1994, J Biol Chem 269:12229-12232). RESULTS: FRET was observed only in cells that expressed CFP and YFP fusions that each contained the wild-type NTM. The introduction of mutations previously shown to disrupt NTM interaction eliminated FRET. Probability binning confirmed that differences between the FRET channels of experimental and control samples were statistically and physiologically significant. CONCLUSION: Flow cytometric analysis of FRET in yeast is a powerful technique for studying protein-protein interactions. The use of flow cytometry allows FRET data to be gathered from a large number of individual cells, thus providing important advantages unavailable to other techniques. Its application to yeast presents a new method to a popular system widely used in proteomic studies.     

Protein bipartivity and essentiality in the yeast protein-protein interaction network

Protein-protein interaction networks (PINs) are structured by means of a few highly connected proteins linked to a large number of less-connected ones. Essential proteins have been found to be more abundant among these highly connected proteins. Here we demonstrate that the likelihood that removal of a protein in a PIN will prove lethal to yeast correlates with the lack of bipartivity of the protein. A protein is bipartite if it can be partitioned in such a way that there are two groups of proteins with intergroup, but not intragroup, interactions. The abundance of essential proteins found among the least bipartite proteins clearly exceeds that found among the most connected ones. For instance, among the top 50 proteins ranked by their lack of bipartivity 62% are essential proteins. However, this percentage is only 38% for proteins ranked according to their number of interactions. Protein bipartivity also surpasses another 5 measures of protein centrality in yeast PIN in identifying essential proteins and doubles the number of essential proteins selected at random. We propose a possible mechanism for the evolution of essential proteins in yeast PIN based on the duplication-divergence scheme. We conclude that a replica protein evolving from a nonbipartite target will also be nonbipartite with high probability. Consequently, these new replicas evolving from nonbipartite (essential) targets will with high probability be essential.     

Topological structure analysis of the protein-protein interaction network in budding yeast

Interaction detection methods have led to the discovery of thousands of interactions between proteins, and discerning relevance within large-scale data sets is important to present-day biology. Here, a spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated protein-protein interaction networks. Our analyses suggest that these hidden topological structures consist of biologically relevant functional groups. This result motivates a new method to predict the function of uncharacterized proteins based on the classification of known proteins within topological structures. Using this spectral analysis method, 48 quasi-cliques and six quasi-bipartites were isolated from a network involving 11,855 interactions among 2617 proteins in budding yeast, and 76 uncharacterized proteins were assigned functions.     

Functional clustering of yeast proteins from the protein-protein interaction network

Background  

The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.     

Hubs with network motifs organize modularity dynamically in the protein-protein interaction network of yeast

Background

It has been recognized that modular organization pervades biological complexity. Based on network analysis, ‘party hubs’ and ‘date hubs’ were proposed to understand the basic principle of module organization of biomolecular networks. However, recent study on hubs has suggested that there is no clear evidence for coexistence of ‘party hubs’ and ‘date hubs’. Thus, an open question has been raised as to whether or not ‘party hubs’ and ‘date hubs’ truly exist in yeast interactome.

Methodology

In contrast to previous studies focusing on the partners of a hub or the individual proteins around the hub, our work aims to study the network motifs of a hub or interactions among individual proteins including the hub and its neighbors. Depending on the relationship between a hub''s network motifs and protein complexes, we define two new types of hubs, ‘motif party hubs’ and ‘motif date hubs’, which have the same characteristics as the original ‘party hubs’ and ‘date hubs’ respectively. The network motifs of these two types of hubs display significantly different features in spatial distribution (or cellular localizations), co-expression in microarray data, controlling topological structure of network, and organizing modularity.

Conclusion

By virtue of network motifs, we basically solved the open question about ‘party hubs’ and ‘date hubs’ which was raised by previous studies. Specifically, at the level of network motifs instead of individual proteins, we found two types of hubs, motif party hubs (mPHs) and motif date hubs (mDHs), whose network motifs display distinct characteristics on biological functions. In addition, in this paper we studied network motifs from a different viewpoint. That is, we show that a network motif should not be merely considered as an interaction pattern but be considered as an essential function unit in organizing modules of networks.     

Promiscuous domains: facilitating stability of the yeast protein-protein interaction network

Domain-domain interactions are a critical type of the mechanisms mediating protein-protein interactions (PPIs). For a given protein domain, its ability to combine with distinct domains is usually referred to as promiscuity or versatility. Interestingly, a previous study has reported that a domain's promiscuity may reflect its ability to interact with other domains in human proteins. In this work, promiscuous domains were first identified from the yeast genome. Then, we sought to determine what roles promiscuous domains might play in the PPI network. Mapping the promiscuous domains onto the proteins in this network revealed that, consistent with the previous knowledge, the hub proteins were significantly enriched with promiscuous domains. We also found that the set of hub proteins were not the same set as those proteins with promiscuous domains, although there was some overlap. Analysis of the topological properties of this yeast PPI network showed that the characteristic path length of the network increased significantly after deleting proteins with promiscuous domains. This indicated that communication between two proteins was longer and the network stability decreased. These observations suggested that, as the hub proteins, proteins with promiscuous domains might play a role in maintaining network stability. In addition, functional analysis revealed that proteins with promiscuous domains mainly participated in the "Folding, Sorting, and Degradation" and "Replication and Repair" biological pathways, and that they significantly execute key molecular functions, such as "nucleoside-triphosphatase activity (GO:0017111)."     

Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation

Network analysis became a powerful tool giving new insights to the understanding of cellular behavior. Heat shock, the archetype of stress responses, is a well-characterized and simple model of cellular dynamics. S. cerevisiae is an appropriate model organism, since both its protein-protein interaction network (interactome) and stress response at the gene expression level have been well characterized. However, the analysis of the reorganization of the yeast interactome during stress has not been investigated yet. We calculated the changes of the interaction-weights of the yeast interactome from the changes of mRNA expression levels upon heat shock. The major finding of our study is that heat shock induced a significant decrease in both the overlaps and connections of yeast interactome modules. In agreement with this the weighted diameter of the yeast interactome had a 4.9-fold increase in heat shock. Several key proteins of the heat shock response became centers of heat shock-induced local communities, as well as bridges providing a residual connection of modules after heat shock. The observed changes resemble to a 'stratus-cumulus' type transition of the interactome structure, since the unstressed yeast interactome had a globally connected organization, similar to that of stratus clouds, whereas the heat shocked interactome had a multifocal organization, similar to that of cumulus clouds. Our results showed that heat shock induces a partial disintegration of the global organization of the yeast interactome. This change may be rather general occurring in many types of stresses. Moreover, other complex systems, such as single proteins, social networks and ecosystems may also decrease their inter-modular links, thus develop more compact modules, and display a partial disintegration of their global structure in the initial phase of crisis. Thus, our work may provide a model of a general, system-level adaptation mechanism to environmental changes.     

A scale of functional divergence for yeast duplicated genes revealed from analysis of the protein-protein interaction network

Background  

Studying the evolution of the function of duplicated genes usually implies an estimation of the extent of functional conservation/divergence between duplicates from comparison of actual sequences. This only reveals the possible molecular function of genes without taking into account their cellular function(s). We took into consideration this latter dimension of gene function to approach the functional evolution of duplicated genes by analyzing the protein-protein interaction network in which their products are involved. For this, we derived a functional classification of the proteins using PRODISTIN, a bioinformatics method allowing comparison of protein function. Our work focused on the duplicated yeast genes, remnants of an ancient whole-genome duplication.     

The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast

The refinement and high-throughput of protein interaction detection methods offer us a protein–protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.     

Non-uniform survival rate of heterodimerization links in the evolution of the yeast protein-protein interaction network

Protein-protein interaction networks (PINs) are scale-free networks with a small-world property. In a small-world network, the average cluster coefficient () is much higher than in a random network, but the average shortest path length () is similar between the two networks. To understand the evolutionary mechanisms shaping the structure of PINs, simulation studies using various network growth models have been performed. It has been reported that the heterodimerization (HD) model, in which a new link is added between duplicated nodes with a uniform probability, could reproduce scale-freeness and a high . In this paper, however, we show that the HD model is unsatisfactory, because (i) to reproduce the high in the yeast PIN, a much larger number (n(HI)) of HD links (links between duplicated nodes) are required than the estimated number of n(HI) in the yeast PIN and (ii) the spatial distribution of triangles in the yeast PIN is highly skewed but the HD model cannot reproduce the skewed distribution. To resolve these discrepancies, we here propose a new model named the non-uniform heterodimerization (NHD) model. In this model, an HD link is preferentially attached between duplicated nodes when they share many common neighbors. Simulation studies demonstrated that the NHD model can successfully reproduce the high , the low n(HI), and the skewed distribution of triangles in the yeast PIN. These results suggest that the survival rate of HD links is not uniform in the evolution of PINs, and that an HD link between high-degree nodes tends to be evolutionarily conservative. The non-uniform survival rate of HD links can be explained by assuming a low mutation rate for a high-degree node, and thus this model appears to be biologically plausible.     

Evolvability of yeast protein-protein interaction interfaces

The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.     

Discovering functional interaction patterns in protein-protein interaction networks

Background  

In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI) network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks.     

Detecting mutually exclusive interactions in protein-protein interaction maps

Comprehensive protein interaction maps can complement genetic and biochemical experiments and allow the formulation of new hypotheses to be tested in the system of interest. The computational analysis of the maps may help to focus on interesting cases and thereby to appropriately prioritize the validation experiments. We show here that, by automatically comparing and analyzing structurally similar regions of proteins of known structure interacting with a common partner, it is possible to identify mutually exclusive interactions present in the maps with a sensitivity of 70% and a specificity higher than 85% and that, in about three fourth of the correctly identified complexes, we also correctly recognize at least one residue (five on average) belonging to the interaction interface. Given the present and continuously increasing number of proteins of known structure, the requirement of the knowledge of the structure of the interacting proteins does not substantially impact on the coverage of our strategy that can be estimated to be around 25%. We also introduce here the Estrella server that embodies this strategy, is designed for users interested in validating specific hypotheses about the functional role of a protein-protein interaction and it also allows access to pre-computed data for seven organisms.     

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Background  

Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.     

A network of protein-protein interactions in yeast

A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.     

Detecting overlapping protein complexes in protein-protein interaction networks

We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.