Prediction of host-pathogen protein interactions by extended network model

Knowledge of the pathogen-host interactions between the species is essentialin order to develop a solution strategy against infectious diseases. In vitro methods take extended periods of time to detect interactions and provide very few of the possible interaction pairs. Hence, modelling interactions between proteins has necessitated the development of computational methods. The main scope of this paper is integrating the known protein interactions between thehost and pathogen organisms to improve the prediction success rate of unknown pathogen-host interactions. Thus, the truepositive rate of the predictions was expected to increase.In order to perform this study extensively, encoding methods and learning algorithms of several proteins were tested. Along with human as the host organism, two different pathogen organisms were used in the experiments. For each combination of protein-encoding and prediction method, both the original prediction algorithms were tested using only pathogen-host interactions and the same methodwas testedagain after integrating the known protein interactions within each organism. The effect of merging the networks of pathogen-host interactions of different species on the prediction performance of state-of-the-art methods was also observed. Successwas measured in terms of Matthews correlation coefficient, precision, recall, F1 score, and accuracy metrics. Empirical results showed that integrating the host and pathogen interactions yields better performance consistently in almost all experiments.     

Identification of conserved protein complexes based on a model of protein network evolution

MOTIVATION: Data on protein-protein interactions (PPIs) are increasing exponentially. To date, large-scale protein interaction networks are available for human and most model species. The arising challenge is to organize these networks into models of cellular machinery. As in other biological domains, a comparative approach provides a powerful basis for addressing this challenge. RESULTS: We develop a probabilistic model for protein complexes that are conserved across two species. The model describes the evolution of conserved protein complexes from an ancestral species by protein interaction attachment and detachment and gene duplication events. We apply our model to search for conserved protein complexes within the PPI networks of yeast and fly, which are the largest networks in public databases. We detect 150 conserved complexes that match well-known complexes in yeast and are coherent in their functional annotations both in yeast and in fly. In comparison with two previous approaches, our model yields higher specificity and sensitivity levels in protein complex detection. AVAILABILITY: The program is available upon request.     

Identification of functional modules from conserved ancestral protein-protein interactions

MOTIVATION: The increasing availability of large-scale protein-protein interaction (PPI) data has fueled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison. RESULTS: We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity. AVAILABILITY: Information for obtaining software and supplementary results are available at http://bioputer.mimuw.edu.pl/papers/cappi.     

Linear Motif-Mediated Interactions Have Contributed to the Evolution of Modularity in Complex Protein Interaction Networks

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.     

Detecting overlapping protein complexes in PPI networks based on robustness

Background

Recently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biological processes, detecting protein complexes in PPI networks is one of important tasks in post-genomic era. However, PPI networks are often incomplete and noisy, which builds barriers to mining protein complexes.

Results

We propose a new and effective algorithm based on robustness to detect overlapping clusters as protein complexes in PPI networks. And in order to improve the accuracy of resulting clusters, our algorithm tries to reduce bad effects brought by noise in PPI networks. And in our algorithm, each new cluster begins from a seed and is expanded through adding qualified nodes from the cluster's neighbourhood nodes. Besides, in our algorithm, a new distance measurement method between a cluster K and a node in the neighbours of K is proposed as well. The performance of our algorithm is evaluated by applying it on two PPI networks which are Gavin network and Database of Interacting Proteins (DIP). The results show that our algorithm is better than Markov clustering algorithm (MCL), Clique Percolation method (CPM) and core-attachment based method (CoAch) in terms of F-measure, co-localization and Gene Ontology (GO) semantic similarity.

Conclusions

Our algorithm detects locally dense regions or clusters as protein complexes. The results show that protein complexes generated by our algorithm have better quality than those generated by some previous classic methods. Therefore, our algorithm is effective and useful.

     

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.     

Quantitative model for binary measurements of protein-protein interactions.

We develop a stochastic model for quantifying the binary measurements of protein-protein interactions. A key concept in the model is the binary response function (BRF) which represents the conditional probability of successfully detecting a protein-protein interaction with a given number of the protein complexes. A popular form of the BRF is introduced and the effect of the sharpness (Hill's coefficient) of this function is studied. Our model is motivated by the recently developed yeast two-hybrid method for measuring protein-protein interaction networks. We suggest that the same phenomenological BRF can also be applied to the mass spectroscopic measurement of protein-protein interactions. Based on the model, we investigate the contributions to the network topology of protein-protein interactions from (i) the distribution of protein binary association free energy, and from (ii) the cellular protein abundance. It is concluded that the association constants among different protein pairs cannot be totally independent. It is also shown that not only the association constants but also the protein abundance could be a factor in producing the power-law degree distribution of protein-protein interaction networks.     

Dynamics of lysozyme structure network: probing the process of unfolding

Recently we showed that the three-dimensional structure of proteins can be investigated from a network perspective, where the amino acid residues represent the nodes in the network and the noncovalent interactions between them are considered for the edge formation. In this study, the dynamical behavior of such networks is examined by considering the example of T4 lysozyme. The equilibrium dynamics and the process of unfolding are followed by simulating the protein at 300 K and at higher temperatures (400 K and 500 K), respectively. The snapshots of the protein structure from the simulations are represented as protein structure networks in which the strength of the noncovalent interactions is considered an important criterion in the construction of edges. The profiles of the network parameters, such as the degree distribution and the size of the largest cluster (giant component), were examined as a function of interaction strength at different temperatures. Similar profiles are seen at all the temperatures. However, the critical strength of interaction (Icritical) and the size of the largest cluster at all interaction strengths shift to lower values at 500 K. Further, the folding/unfolding transition is correlated with contacts evaluated at Icritical and with the composition of the top large clusters obtained at interaction strengths greater than Icritical. Finally, the results are compared with experiments, and predictions are made about the residues, which are important for stability and folding. To summarize, the network analysis presented in this work provides insights into the details of the changes occurring in the protein tertiary structure at the level of amino acid side-chain interactions, in both the equilibrium and the unfolding simulations. The method can also be employed as a valuable tool in the analysis of molecular dynamics simulation data, since it captures the details at a global level, which may elude conventional pairwise interaction analysis.     

Conserved abundance and topological features in chromatin‐remodeling protein interaction networks

The study of conserved protein interaction networks seeks to better understand the evolution and regulation of protein interactions. Here, we present a quantitative proteomic analysis of 18 orthologous baits from three distinct chromatin‐remodeling complexes in Saccharomyces cerevisiae and Homo sapiens. We demonstrate that abundance levels of orthologous proteins correlate strongly between the two organisms and both networks have highly similar topologies. We therefore used the protein abundances in one species to cross‐predict missing protein abundance levels in the other species. Lastly, we identified a novel conserved low‐abundance subnetwork further demonstrating the value of quantitative analysis of networks.     

Link Clustering Reveals Structural Characteristics and Biological Contexts in Signed Molecular Networks

Many biological networks are signed molecular networks which consist of positive and negative links. To reveal the distinct features between links with different signs, we proposed signed link-clustering coefficients that assess the similarity of inter-action profiles between linked molecules. We found that positive links tended to cluster together, while negative links usually behaved like bridges between positive clusters. Positive links with higher adhesiveness tended to share protein domains, be associated with protein-protein interactions and make intra-connections within protein complexes. Negative links that were more bridge-like tended to make interconnections between protein complexes. Utilizing the proposed measures to group positive links, we observed hierarchical modules that could be well characterized by functional annotations or known protein complexes. Our results imply that the proposed sign-specific measures can help reveal the network structural characteristics and the embedded biological contexts of signed links, as well as the functional organization of signed molecular networks.     

Contrasting structures of plant–mite networks compounded by phytophagous and predatory mite species

Differences in the feeding habits between phytophagous and predatory species can determine distinct ecological interactions between mites and their host plants. Herein, plant–mite networks were constructed using available literature on plant-dwelling mites from Brazilian natural vegetation in order to contrast phytophagous and predatory mite networks. The structural patterns of plant–mite networks were described through network specialization (connectance) and modularity. A total of 187 mite species, 65 host plant species and 646 interactions were recorded in 14 plant–mite networks. Phytophagous networks included 96 mite species, 61 host plants and 277 interactions, whereas predatory networks contained 91 mite species, 54 host plants and 369 interactions. No differences in the species richness of mites and host plants were observed between phytophagous and predatory networks. However, plant–mite networks composed of phytophagous mites showed lower connectance and higher modularity when compared to the predatory mite networks. The present results corroborate the hypothesis that trophic networks are more specialized than commensalistic networks, given that the phytophagous species must deal with plant defenses, in contrast to predatory mites which only inhabit and forage for resources on plants.     

Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks

A major focus of systems biology is to characterize interactions between cellular components, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flexible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to reconstruct biological networks including protein-DNA interactions, posttranslational protein modifications (PTMs), lectin-glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological systems. We will also discuss emerging applications and future directions of protein microarray technology in the global frontier.     

Elevated Serum Levels of Macrophage Migration Inhibitory Factor Are Associated with Progressive Chronic Cardiomyopathy in Patients with Chagas Disease

Clinical symptoms of chronic Chagas disease occur in around 30% of the individuals infected with Trypanosoma cruzi and are characterized by heart inflammation and dysfunction. The pathogenesis of chronic chagasic cardiomyopathy (CCC) is not completely understood yet, partially because disease evolution depends on complex host-parasite interactions. Macrophage migration inhibitory factor (MIF) is a pleiotropic proinflammatory cytokine that promotes numerous pathophysiological processes. In the current study, we investigated the link between MIF and CCC progression.Immunohistochemical analysis demonstrated MIF overexpression in the hearts from chronically T. cruzi-infected mice, particularly those showing intense inflammatory infiltration. We also found that MIF exogenously added to parasite-infected murine macrophage cultures is capable of enhancing the production of TNF-α and reactive oxygen species, both with pathogenic roles in CCC. Thus, the integrated action of MIF and other cytokines and chemokines may account for leukocyte influx to the infected myocardium, accompanied by enhanced local production of multiple inflammatory mediators. We further examined by ELISA the level of MIF in the sera from chronic indeterminate and cardiomyopathic chagasic patients, and healthy subjects. CCC patients displayed significantly higher MIF concentrations than those recorded in asymptomatic T. cruzi-infected and uninfected individuals. Interestingly, increased MIF levels were associated with severe progressive Chagas heart disease, in correlation with elevated serum concentration of high sensitivity C-reactive protein and also with several echocardiographic indicators of left ventricular dysfunction, one of the hallmarks of CCC. Our present findings represent the first evidence that enhanced MIF production is associated with progressive cardiac impairment in chronic human infection with T. cruzi, strengthening the relationship between inflammatory response and parasite-driven pathology. These observations contribute to unravel the elements involved in the pathogenesis of CCC and may also be helpful for the design of novel therapies aimed to control long-term morbidity in chagasic patients.     

Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network

In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C alpha-H...O and C alpha-H...pi interactions in proteins using artificial neural network. Both standard feed-forward neural network (FNN) and recurrent neural networks (RNN) have been trained and tested using five-fold cross-validation on a non-homologous dataset of 2298 protein chains where no pair of sequences has more than 25% sequence identity. It has been found that the prediction accuracy varies with the separation distance between donor and acceptor residues. The maximum sensitivity achieved with RNN for C alpha-H...O is 51.2% when donor and acceptor residues are four residues apart (i.e. at delta D-A = 4) and for C alpha-H...pi is 82.1% at delta D-A = 3. The performance of RNN is increased by 1-3% for both types of interactions when PSIPRED predicted protein secondary structure is used. Overall, RNN performs better than feed-forward networks at all separation distances between donor-acceptor pair for both types of interactions. Based on the observations, a web server CHpredict (available at http://www.imtech.res.in/raghava/chpredict/) has been developed for predicting donor and acceptor residues in C alpha-H...O and C alpha-H...pi interactions in proteins.     

Observing frugivores or collecting scats: a method comparison to construct quantitative seed dispersal networks

Mutualistic interactions form the basis for many ecological processes and are often analyzed within the framework of ecological networks. These interactions can be sampled with a range of methods and first analyses of pollination networks sampled with different methods showed differences in common network metrics. However, it is yet unknown if metrics of seed dispersal networks are similarly affected by the sampling method and if different methods detect a complementary set of frugivores. This is necessary to better understand the (dis-)advantages of each method and to identify the role of each frugivore for the seed dispersal process. Here, we compare seed removal networks based on the observation of 2189 frugivore visits on ten focal plant species with seed deposition networks constructed by DNA barcoding of plant seeds in 3094 frugivore scats. We were interested in whether both methods identify the same disperser species and if species-level network metrics of plant species were correlated between network types. Both methods identified the same avian super-generalist frugivores, which accounted for the highest number of dispersed seeds. However, only with DNA barcoding, we detected elusive but frequent mammalian seed dispersers. The overall networks created by both methods were congruent but the plant species' degree, their interaction frequency and their specialization index (d′) differed. Our study suggests that DNA barcoding of defecated and regurgitated seeds can be used to construct quantitative seed deposition networks similar to those constructed by focal observations. To improve the overall completeness of seed dispersal networks it might be useful to combine both methods to detect interactions by both birds and mammals. Most importantly, the DNA barcoding method provides information on the post-dispersal stage and thus on the qualitative contribution of each frugivore for the plant community thereby linking species interactions to regeneration dynamics of fleshy-fruited plant species.     

Spatio-temporal variation in the structure of pollination networks

Pollination networks are representations of all interactions between co-existing plants and their flower visiting animals at a given site. Although the study of networks has become a distinct sub-discipline in pollination biology, few studies have attempted to quantify spatio-temporal variation in species composition and structure of networks. We here investigate patterns of year-to-year change in pollination networks from six different sites spanning a large latitudinal gradient. We quantified level of species persistence and interactions among years, and examined year-to-year variation of network structural parameters in relation to latitude and sampling effort. In addition, we tested for correlations between annual variation in network parameters and short and long-term climate change variables. Numbers of plant and animal species and interactions were roughly constant from one year to another at all sites. However, composition of species and interactions changed from one year to another. Turnover was particularly high for flower visitors and interactions. On the other hand, network structural parameters (connectance, nestedness, modularity and centralization) remained remarkably constant between years, regardless of network size and latitude. Inter-annual variation of network parameters was not related to short or long term variation in climate variables (mean annual temperature and annual precipitation). We thus conclude that pollination networks are highly dynamic and variable in composition of species and interactions among years. However, general patterns of network structure remain constant, indicating that species may be replaced by topologically similar species. These results suggest that pollination networks are to some extent robust against factors affecting species occurrences.     

Highly covarying residues have a functional role in antibody constant domains

The ability to generate and design antibodies recognizing specific targets has revolutionized the pharmaceutical industry and medical imaging. Engineering antibody therapeutics in some cases requires modifying their constant domains to enable new and altered interactions. Engineering novel specificities into antibody constant domains has proved challenging due to the complexity of inter‐domain interactions. Covarying networks of residues that tend to cluster on the protein surface and near binding sites have been identified in some proteins. However, the underlying role these networks play in the protein resulting in their conservation remains unclear in most cases. Resolving their role is crucial, because residues in these networks are not viable design targets if their role is to maintain the fold of the protein. Conversely, these networks of residues are ideal candidates for manipulating specificity if they are primarily involved in binding, such as the myriad interdomain interactions maintained within antibodies. Here, we identify networks of evolutionarily‐related residues in C‐class antibody domains by evaluating covariation, a measure of propensity with which residue pairs vary dependently during evolution. We computationally test whether mutation of residues in these networks affects stability of the folded antibody domain, determining their viability as design candidates. We find that members of covarying networks cluster at domain‐domain interfaces, and that mutations to these residues are diverse and frequent during evolution, precluding their importance to domain stability. These results indicate that networks of covarying residues exist in antibody domains for functional reasons unrelated to thermodynamic stability, making them ideal targets for antibody design. Proteins 2013. © 2012 Wiley Periodicals, Inc.