The value of high quality protein-protein interaction networks for systems biology

Protein-protein interaction (PPI) networks contain a large amount of useful information for the functional characterization of proteins and promote the understanding of the complex molecular relationships that determine the phenotype of a cell. Recently, large human interaction maps have been generated with high throughput technologies such as the yeast two-hybrid system. However, they are static and incomplete and do not provide immediate clues about the cellular processes that convert genetic information into complex phenotypes. Refined multiple-aspect PPI screening and confirmation strategies will have to be put in place to increase the validity of interaction maps. Integration of interaction data with other qualitative and quantitative information (e.g. protein expression or localization data), will be required to construct networks of protein function that reflect dynamic processes in the cell. In this way, combined PPI networks can become valuable resources for a systems-level understanding of cellular processes and complex phenotypes.     

Dominating biological networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC)" genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Protein and Genome Evolution in Mammalian Cells for Biotechnology Applications

Mutation and selection are the essential steps of evolution. Researchers have long used in vitro mutagenesis, expression, and selection techniques in laboratory bacteria and yeast cultures to evolve proteins with new properties, termed directed evolution. Unfortunately, the nature of mammalian cells makes applying these mutagenesis and whole-organism evolution techniques to mammalian protein expression systems laborious and time consuming. Mammalian evolution systems would be useful to test unique mammalian cell proteins and protein characteristics, such as complex glycosylation. Protein evolution in mammalian cells would allow for generation of novel diagnostic tools and designer polypeptides that can only be tested in a mammalian expression system. Recent advances have shown that mammalian cells of the immune system can be utilized to evolve transgenes during their natural mutagenesis processes, thus creating proteins with unique properties, such as fluorescence. On a more global level, researchers have shown that mutation systems that affect the entire genome of a mammalian cell can give rise to cells with unique phenotypes suitable for commercial processes. This review examines the advances in mammalian cell and protein evolution and the application of this work toward advances in commercial mammalian cell biotechnology.     

Functional topology in a network of protein interactions

MOTIVATION: The building blocks of biological networks are individual protein-protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells. RESULTS: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database. CONCLUSIONS: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure-function relationships observable in a large PPI network.     

In vivo protein trapping in Drosophila.

The deciphering of complete genome sequences has opened a post-genomic proteomics era. While the sequence of many proteins is now known, attention will increasingly focus on the complex interaction networks that regulate their activity, and the analysis of protein distribution in the cell will be crucial to elucidating their function. A new generation of gene trapping devices, protein trap transposons, offers a way of analysing In vivo the dynamics of sub-cellular distribution of a large number of proteins. Many transgenic lines have already been established and are available. Screens focusing on particular cell compartments can be devised.     

Probing the extent of randomness in protein interaction networks

Protein-protein interaction (PPI) networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered features. We recently demonstrated that PPI networks show degree-weighted behavior, whereby the probability of interaction between two proteins is generally proportional to the product of their numbers of interacting partners or degrees. It was surmised that degree-weighted behavior is a characteristic of randomness. We expand upon these findings by developing a random, degree-weighted, network model and show that eight PPI networks determined from single high-throughput (HT) experiments have global and local properties that are consistent with this model. The apparent random connectivity in HT PPI networks is counter-intuitive with respect to their observed degree distributions; however, we resolve this discrepancy by introducing a non-network-based model for the evolution of protein degrees or "binding affinities." This mechanism is based on duplication and random mutation, for which the degree distribution converges to a steady state that is identical to one obtained by averaging over the eight HT PPI networks. The results imply that the degrees and connectivities incorporated in HT PPI networks are characteristic of unbiased interactions between proteins that have varying individual binding affinities. These findings corroborate the observation that curated and high-confidence PPI networks are distinct from HT PPI networks and not consistent with a random connectivity. These results provide an avenue to discern indiscriminate organizations in biological networks and suggest caution in the analysis of curated and high-confidence networks.     

Simulated evolution of protein-protein interaction networks with realistic topology

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.     

LEVELNET to visualize,explore, and compare protein–protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein–protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.     

Chemical genetic screening approaches to neurobiology

Chemical genetics is an emerging technology for revealing the signaling networks that regulate biological phenotypes using exogenous reagents such as small organic molecules. To study neurobiology using chemical genetics, high-throughput cell and organismal assays can be created to identify compounds and proteins that regulate diverse neuronal phenotypes, such as cell viability, gene expression level, protein association, protein aggregation, glutamate uptake, membrane polarization, mitochondrial function, neurite outgrowth, and growth cone composition. This powerful set of tools will enable the molecular dissection of complex processes that occur within the nervous system.     

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.     

Strategies towards high-quality binary protein interactome maps

Many processes in a cell depend on protein–protein interactions (PPIs) and perturbations of these interactions can lead to diseases. Comprehensive knowledge of PPI networks will not only give us information on how the cell is organized, but will also provide new drug targets. Current binary PPI networks are mainly generated by high-throughput yeast two-hybrid. Due to the small overlap of these maps, it has long been assumed that these maps are of low quality containing many false positives. However, by using an orthogonal two-hybrid method, MAPPIT (mammalian protein–protein interaction trap), these maps were shown to be of high quality suggesting that the limited overlap is likely due to low sensitivity and not to low specificity.     

Exploring the protein–protein interaction landscape in plants

Protein–protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.     

Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources

Background

Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.

Results

We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.

Conclusions

We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.     

Linking structural features of protein complexes and biological function

Protein–protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure–function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.     

Salinity-induced changes in protein expression in the halophytic plant Nitraria sphaerocarpa

Salinity is a major abiotic stress that inhibits plant growth and development. Plants have evolved complex adaptive mechanisms that respond to salinity stress. However, an understanding of how plants respond to salinity stress is far from being complete. In particular, how plants survive salinity stress via alterations to their intercellular metabolic networks and defense systems is largely unknown. To delineate the responses of Nitraria sphaerocarpa cell suspensions to salinity, changes in their protein expression patterns were characterized by a comparative proteomic approach. Cells that had been treated with 150 mM NaCl for 1, 3, 5, 7, or 9 days developed several stress-related phenotypes, including those affecting morphology and biochemical activities. Of ~1100 proteins detected in 2-DE gel patterns, 130 proteins showed differences in abundance with more than 1.5-fold when cells were stressed by salinity. All but one of these proteins was identified by MS and database searching. The 129 spots contained 111 different proteins, including those involved in signal transduction, cell rescue/defense, cytoskeleton and cell cycle, protein folding and assembly, which were the most significantly affected. Taken together, our results provide a foundation to understand the mechanism of salinity response.     

Age-Dependent Evolution of the Yeast Protein Interaction Network Suggests a Limited Role of Gene Duplication and Divergence

Proteins interact in complex protein–protein interaction (PPI) networks whose topological properties—such as scale-free topology, hierarchical modularity, and dissortativity—have suggested models of network evolution. Currently preferred models invoke preferential attachment or gene duplication and divergence to produce networks whose topology matches that observed for real PPIs, thus supporting these as likely models for network evolution. Here, we show that the interaction density and homodimeric frequency are highly protein age–dependent in real PPI networks in a manner which does not agree with these canonical models. In light of these results, we propose an alternative stochastic model, which adds each protein sequentially to a growing network in a manner analogous to protein crystal growth (CG) in solution. The key ideas are (1) interaction probability increases with availability of unoccupied interaction surface, thus following an anti-preferential attachment rule, (2) as a network grows, highly connected sub-networks emerge into protein modules or complexes, and (3) once a new protein is committed to a module, further connections tend to be localized within that module. The CG model produces PPI networks consistent in both topology and age distributions with real PPI networks and is well supported by the spatial arrangement of protein complexes of known 3-D structure, suggesting a plausible physical mechanism for network evolution.