Geometric De-noising of Protein-Protein Interaction Networks

Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.     

Chromosomal Translocations Generated by High-Frequency Meiotic Recombination between Repeated Yeast Genes

We have examined meiotic and mitotic recombination between repeated genes on nonhomologous chromosomes in the yeast Saccharomyces cerevisiae. The results of these experiments can be summarized in three statements. First, gene conversion events between repeats on nonhomologous chromosomes occur frequently in meiosis. The frequency of such conversion events is only 17-fold less than the analogous frequency of conversion between genes at allelic positions on homologous chromosomes. Second, meiotic and mitotic conversion events between repeated genes on nonhomologous chromosomes are associated with reciprocal recombination to the same extent as conversion between allelic sequences. The reciprocal exchanges between the repeated genes result in chromosomal translocations. Finally, recombination between repeated genes on nonhomologous chromosomes occurs much more frequently in meiosis than in mitosis.     

Identification of Chromosomal HP0892-HP0893 Toxin-Antitoxin Proteins in Helicobacter pylori and Structural Elucidation of Their Protein-Protein Interaction

Bacterial chromosomal toxin-antitoxin (TA) systems have been proposed not only to play an important role in the stress response, but also to be associated with antibiotic resistance. Here, we identified the chromosomal HP0892-HP0893 TA proteins in the gastric pathogen, Helicobacter pylori, and structurally characterized their protein-protein interaction. Previously, HP0892 protein was suggested to be a putative TA toxin based on its structural similarity to other RelE family TA toxins. In this study, we demonstrated that HP0892 binds to HP0893 strongly with a stoichiometry of 1:1, and HP0892-HP0893 interaction occurs mainly between the N-terminal secondary structure elements of HP0892 and the C-terminal region of HP0893. HP0892 cleaved mRNA in vitro, preferentially at the 5′ end of A or G, and the RNase activity of HP0892 was inhibited by HP0893. In addition, heterologous expression of HP0892 in Escherichia coli cells led to cell growth arrest, and the cell toxicity of HP0892 was neutralized by co-expression with HP0893. From these results and a structural comparison with other TA toxins, it is concluded that HP0892 is a toxin with intrinsic RNase activity and HP0893 is an antitoxin against HP0892 from a TA system of H. pylori. It has been known that hp0893 gene and another TA antitoxin gene, hp0895, of H. pylori, are both genomic open reading frames that correspond to genes that are potentially expressed in response to interactions with the human gastric mucosa. Therefore, it is highly probable that TA systems of H. pylori are involved in virulence of H. pylori.     

Evolutionary Capacitance and Control of Protein Stability in Protein-Protein Interaction Networks

In addition to their biological function, protein complexes reduce the exposure of the constituent proteins to the risk of undesired oligomerization by reducing the concentration of the free monomeric state. We interpret this reduced risk as a stabilization of the functional state of the protein. We estimate that protein-protein interactions can account for of additional stabilization; a substantial contribution to intrinsic stability. We hypothesize that proteins in the interaction network act as evolutionary capacitors which allows their binding partners to explore regions of the sequence space which correspond to less stable proteins. In the interaction network of baker''s yeast, we find that statistically proteins that receive higher energetic benefits from the interaction network are more likely to misfold. A simplified fitness landscape wherein the fitness of an organism is inversely proportional to the total concentration of unfolded proteins provides an evolutionary justification for the proposed trends. We conclude by outlining clear biophysical experiments to test our predictions.     

Identifying Hubs in Protein Interaction Networks

Background

In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines “hub” proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust.

Methodology

In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction.

Conclusions

We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.     

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein–protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker’s yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.     

Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

Background  

Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.     

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today''s PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.     

Identification of Essential Proteins Based on Ranking Edge-Weights in Protein-Protein Interaction Networks

Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.     

FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

The characterization of the interacting behaviors of complex biological systems is a primary objective in protein–protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein–protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.     

Chromosomal Integration and Expression of Two Bacterial alpha-Acetolactate Decarboxylase Genes in Brewer's Yeast

A bacterial gene encoding alpha-acetolactate decarboxylase, isolated from Klebsiella terrigena or Enterobacter aerogenes, was expressed in brewer's yeast. The genes were expressed under either the yeast phosphoglycerokinase (PGK1) or the alcohol dehydrogenase (ADH1) promoter and were integrated by gene replacement by using cotransformation into the PGK1 or ADH1 locus, respectively, of a brewer's yeast. The expression level of the alpha-acetolactate decarboxylase gene of the PGK1 integrant strains was higher than that of the ADH1 integrants. Under pilot-scale brewing conditions, the alpha-acetolactate decarboxylase activity of the PGK1 integrant strains was sufficient to reduce the formation of diacetyl below the taste threshold value, and no lagering was needed. The brewing properties of the recombinant yeast strains were otherwise unaltered, and the quality (most importantly, the flavor) of the trial beers produced was as good as that of the control beer.     

利用RNA-seq数据分析肝细胞癌差异表达基因及蛋白相互作用

RNA-Seq已成为当前转录组学研究的强有力工具,尤其在肿瘤差异表达基因的筛选方面有重要的应用价值。为进一步阐明肝细胞癌(HCC)的分子机制,本研究对GEO中1个包括12对HCC组织标本的RNA-Seq数据集(GSE63863)进行了生物信息学分析。采用edgeR、DESeq2、voom等3种不同算法的软件进行统计分析,共获得976个差异表达基因(adj. p-value<0.01或FDR<0.01,|logFC|≥2),其中上调表达422个(43.2%),下调554个(56.8%)。GO富集分析显示这些差异表达基因主要涉及离子结合、氧化还原酶活性等分子功能以及氧化还原、细胞分裂等生物学过程;KEGG通路分析显示,这些差异表达基因主要涉及细胞周期、视黄醇等代谢通路。STRING分析显示,共有654个基因编码的蛋白质存在相互作用,进一步利用MCODE分析显示,169个基因编码蛋白构成4个子网络,相应的中心节点基因分别为UBE2C、GNG4、TTR、FOS,这些基因的异常表达可能在HCC的发生发展过程中具有重要作用。上述研究结果将为进一步阐明HCC分子发病机制、寻找新型生物标志物提供初步的依据。     

Interaction between Intrinsically Disordered Proteins Frequently Occurs in a Human Protein-Protein Interaction Network

Intrinsic protein disorder is a widespread phenomenon characterised by a lack of stable three-dimensional structures and is considered to play an important role in protein-protein interactions (PPIs). This study examined the genome-wide preference of disorder in PPIs by using exhaustive disorder prediction in human PPIs. We categorised the PPIs into three types (interaction between disordered proteins, interaction between structured proteins, and interaction between a disordered protein and a structured protein) with regard to the flexibility of molecular recognition and compared these three interaction types in an existing human PPI network with those in a randomised network. Although the structured regions were expected to become the identifiers for binding recognition, this comparative analysis revealed unexpected results. The occurrence of interactions between disordered proteins was significantly frequent, and that between a disordered protein and a structured protein was significantly infrequent. We found that this propensity was much stronger in interactions between nonhub proteins. We also analysed the interaction types from a functional standpoint by using GO, which revealed that the interaction between disordered proteins frequently occurred in cellular processes, regulation, and metabolic processes. The number of interactions, especially in metabolic processes between disordered proteins, was 1.8 times as large as that in the randomised network. Another analysis conducted by using KEGG pathways provided results where several signaling pathways and disease-related pathways included many interactions between disordered proteins. All of these analyses suggest that human PPIs preferably occur between disordered proteins and that the flexibility of the interacting protein pairs may play an important role in human PPI networks.     

脂肪细胞分化差异表达基因与其染色体所在遗传图位置的相关性分析

在脂肪细胞分化过程中,有约1／3表达的基因被诱导或抑制。通过分析3T3－L1脂肪细胞分化差异表达基因在染色体遗传图上的位置,对共同表达诱导或抑制的基因群体的调控与它们在染色体遗传图上的位置分布的关系进行分析。结果显示这些共同调控的基因除拥有共同的转录调控因子外,未发现在染色体的位置上和它们的共同调控有相关性。     

ModuleRole: A Tool for Modulization,Role Determination and Visualization in Protein-Protein Interaction Networks

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by “User Guide” in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user’s own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.

Availability

ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.     

Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.