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.     

Comparison of human protein-protein interaction maps

MOTIVATION: Large-scale mappings of protein-protein interactions have started to give us new views of the complex molecular mechanisms inside a cell. After initial projects to systematically map protein interactions in model organisms such as yeast, worm and fly, researchers have begun to focus on the mapping of the human interactome. To tackle this enormous challenge, different approaches have been proposed and pursued. While several large-scale human protein interaction maps have recently been published, their quality remains to be critically assessed. RESULTS: We present here a first comparative analysis of eight currently available large-scale maps with a total of over 10,000 unique proteins and 57,000 interactions included. They are based either on literature search, orthology or by yeast-two-hybrid assays. Comparison reveals only a small, but statistically significant overlap. More importantly, our analysis gives clear indications that all interaction maps imply considerable selection and detection biases. These results have to be taken into account for future assembly of the human interactome. AVAILABILITY: An integrated human interaction network called Unified Human Interactome (UniHI) is made publicly accessible at http://www.mdc-berlin.de/unihi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

From protein networks to biological systems

A system-level understanding of any biological process requires a map of the relationships among the various molecules involved. Technologies to detect and predict protein interactions have begun to produce very large maps of protein interactions, some including most of an organism's proteins. These maps can be used to study how proteins work together to form molecular machines and regulatory pathways. They also provide a framework for constructing predictive models of how information and energy flow through biological networks. In many respects, protein interaction maps are an entrée into systems biology.     

LinkinPath: from sequence to interconnected pathway

SUMMARY: LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps; (ii) compare functional compositions within and among the datasets using TreeMaps; (iii) analyze the statistically enriched pathways across the datasets; (iv) build the pathway-to-pathway networks for each dataset; (v) explore potential interaction/reaction paths between pathways; and (vi) identify common pathway-to-pathway networks across the datasets. AVAILABILITY: LinkinPath is freely available to all interested users at http://www.biotec.or.th/isl/linkinpath/.     

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer''s Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.     

The Cartographers toolbox: building bigger and better human protein interaction networks

One of the greatest challenges of the post-genomic era is theconstruction of a more comprehensive human protein interactionmap. While this process may take many years to complete, thedevelopment of stringent high throughput techniques and theemergence of complementary assays mean that the aim of buildinga detailed binary map of the human interactome is now a veryrealistic goal. In particular, methods which facilitate theanalysis of large numbers of membrane-protein interactions meanthat it will be possible to construct more extensive networks,which in turn provide new insights into the functional connectivitybetween intra- and extra-cellular processes. This is importantas many therapeutic strategies are designed to elicit effectsvia ‘tractable’ cell-surface proteins. Therefore,the construction of maps depicting the complexity of trans-cellularcommunication networks will not only improve our understandingof physiological processes, it will also aid the design of rationaltherapeutic strategies, with fewer potential side effects. Thisreview aims to provide a basic insight into the approaches currentlybeing used to construct binary human protein interaction networks,with particular reference to newer techniques, which have thepotential to extend network coverage and aid the conditionalannotation of interactome-scale protein interaction maps.      

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.     

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the heaviest k-subgraph problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the standard deviation and mean ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal     

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.     

Two-dimensional electrophoresis of plasma proteins without denaturing agents.

A technique of two-dimensional polyacrylamide gel electrophoresis for the separation of plasma proteins is described. Human plasma proteins were separated by isoelectric focusing followed by electrophoresis in a 4 to 21% linear gradient gel slab. No denaturing agent was used throughout the procedure, so that the analysis of native proteins is possible. Two-dimensional patterns obtained from normal human plasma samples were recorded as "staining density maps," which are similar to contour line maps, and more than 230 protein spots were counted reproducibly on each "staining density map." This technique permits the simultaneous estimation of pI's and approximate molecular weights of native proteins on the slab gel. Applications of this technique to an IgA myeloma plasma sample and a porcine serum sample are described.     

Molecular interaction networks for the analysis of human disease: Utility,limitations, and considerations

High‐throughput ‘‐omics’ data can be combined with large‐scale molecular interaction networks, for example, protein–protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative ‘‐omics’ methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and “network‐type.” The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high‐throughput studies versus a meta‐database of smaller literature‐curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large‐scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.     

Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

Background  

Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear.     

HPID: the Human Protein Interaction Database

The Human Protein Interaction Database (http://www.hpid.org) was designed (1) to provide human protein interaction information pre-computed from existing structural and experimental data, (2) to predict potential interactions between proteins submitted by users and (3) to provide a depository for new human protein interaction data from users. Two types of interaction are available from the pre-computed data: (1) interactions at the protein superfamily level and (2) those transferred from the interactions of yeast proteins. Interactions at the superfamily level were obtained by locating known structural interactions of the PDB in the SCOP domains and identifying homologs of the domains in the human proteins. Interactions transferred from yeast proteins were obtained by identifying homologs of the yeast proteins in the human proteins. For each human protein in the database and each query submitted by users, the protein superfamilies and yeast proteins assigned to the protein are shown, along with their interacting partners. We have also developed a set of web-based programs so that users can visualize and analyze protein interaction networks in order to explore the networks further. AVAILABILITY: http://www.hpid.org.     

The role of water coordination in the pH-dependent gating of hAQP10

Human aquaporin 10 (hAQP10) is an aquaglyceroporin that assists in maintaining glycerol flux in adipocytes during lipolysis at low pH. Hence, a molecular understanding of the pH-sensitive glycerol conductance may open up for drug development in obesity and metabolically related disorders. Control of hAQP10-mediated glycerol flux has been linked to the cytoplasmic end of the channel, where a unique loop is regulated by the protonation status of histidine 80 (H80). Here, we performed unbiased molecular dynamics simulations of three protonation states of H80 to unravel channel gating. Strikingly, at neutral pH, we identified a water coordination pattern with an inverted orientation of the water molecules in vicinity of the loop. Protonation of H80 results in a more hydrophobic loop conformation, causing loss of water coordination and leaving the pore often dehydrated. Our results indicate that the loss of such water interaction network may be integral for the destabilization of the loop in the closed configuration at low pH. Additionally, a residue unique to hAQP10 (F85) reveals structural importance by flipping into the channel in correlation with loop movements, indicating a loop-stabilizing role in the closed configuration. Taken together, our simulations suggest a unique gating mechanism combining complex interaction networks between water molecules and protein residues at the loop interface. Considering the role of hAQP10 in adipocytes, the detailed molecular insights of pH-regulation presented here will help to understand glycerol pathways in these cells and may assist in drug discovery for better management of human adiposity and obesity.     

Mechanism of regulating human leukemia cell growth and differentiation by a lymphokine

Human myelogenous leukemia cells can be induced to differentiate in vitro along the monocyte-macrophage pathway by a T cell lymphokine maturation inducer. Maturation inducer has now been purified from a human T cell line and determined to be a single chain protein with an approximate molecular weight of 53,500. It induces the differentiation and proliferation of human leukemic HL-60 promyelocytes in a dosage-dependent fashion. Initial interaction with cells at G1 phase induced the cells to enter proliferating S phase. Subsequent differentiation from S phase was dependent on an optimal inducer quantity (18.7 pM - 18.7 nM) which mediated growth cessation and termination differentiation to monocytes-macrophages. When inducer quantity was more or less than this optimal range, the cells did not undergo differentiation but were continuously stimulated to proliferate. This may represent an important proliferation mechanism of leukemic cells.     

PAI1: a novel PP1‐interacting protein that mediates human plasma's anti‐apoptotic effect in endothelial cells

Activation of apoptotic signalling in endothelial cells contributes to the detrimental effects of a variety of pathological stimuli. In investigating the molecular events underlying the anti‐apoptotic effect of human plasma in cultured human endothelial cells, we unexpectedly uncovered a novel mechanism of apoptosis suppression by human plasma through an interaction between two previously unrelated proteins. Human plasma inhibited hypoxia–serum deprivation‐induced apoptosis and stimulated BAD^S136 and Akt^S473 phosphorylation. Akt1 silencing reversed part (~52%) of the anti‐apoptotic effect of human plasma, suggesting the existence of additional mechanisms mediating the anti‐apoptotic effect other than Akt signalling. Human plasma disrupted the interaction of BAD with protein phosphatase 1 (PP1). Mass spectrometry identified fourteen PP1‐interacting proteins induced by human plasma. Notably, a group of serine protease inhibitors including plasminogen activator inhibitor 1 (PAI1), a major inhibitor of fibrinolysis, were involved. Silencing of PAI1 attenuated the anti‐apoptotic effect of human plasma. Furthermore, combined Akt1 and PAI1 silencing attenuated the majority of the anti‐apoptotic effect of human plasma. We conclude that human plasma protects against endothelial cell apoptosis through sustained BAD phosphorylation, which is achieved by, at least in part, a novel interaction between PP1 with PAI1.     

A three-symbol code for organized proteomes based on cyclical imaging of protein locations.

BACKGROUND: A major challenge in the post genomic era is to map and decipher the functional molecular networks of proteins directly in a cell or a tissue. This task requires technologies for the colocalization of random numbers of different molecular components (e.g. proteins) in one sample in one experiment. METHODS: Multi-epitope-ligand-"kartographie" (MELK) was developed as a microscopic imaging technology running cycles of iterative fluorescence tagging, imaging, and bleaching, to colocalize a large number of proteins in one sample (morphologically intact routinely fixed cells or tissue). RESULTS: In the present study, 18 different cell surface proteins were colocalized by MELK in cells and tissue sections in different compartments of the human immune system. From the resulting sets of multidimensional binary vectors the most prominent groups of protein-epitope arrangements were extracted and imaged as protein "toponome" maps providing direct insight in the higher order topological organization of immune compartments uncovering new tissue domains. The data sets suggest that protein networks, topologically organized in proteomes in situ, obey a unique protein-colocation and -anticolocation code describable by three symbols. CONCLUSION: The technology has the potential to colocalize hundreds of proteins and other molecular components in one sample and may offer many applications in biology and medicine.