Context-specific protein network miner--an online system for exploring context-specific protein interaction networks from the literature

Background

Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis.

Results

We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality.

Conclusions

CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.     

An automated method for finding molecular complexes in large protein interaction networks

Background  

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery.     

Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks

Background

Epidemiological studies in the recent years have investigated the relationship between dietary habits and disease risk demonstrating that diet has a direct effect on public health. Especially plant-based diets -fruits, vegetables and herbs- are known as a source of molecules with pharmacological properties for treatment of several malignancies. Unquestionably, for developing specific intervention strategies to reduce cancer risk there is a need for a more extensive and holistic examination of the dietary components for exploring the mechanisms of action and understanding the nutrient-nutrient interactions. Here, we used colon cancer as a proof-of-concept for understanding key regulatory sites of diet on the disease pathway.

Results

We started from a unique vantage point by having a database of 158 plants positively associated to colon cancer reduction and their molecular composition (~3,500 unique compounds). We generated a comprehensive picture of the interaction profile of these edible and non-edible plants with a predefined candidate colon cancer target space consisting of ~1,900 proteins. This knowledge allowed us to study systematically the key components in colon cancer that are targeted synergistically by phytochemicals and identify statistically significant and highly correlated protein networks that could be perturbed by dietary habits.

Conclusion

We propose here a framework for interrogating the critical targets in colon cancer processes and identifying plant-based dietary interventions as important modifiers using a systems chemical biology approach. Our methodology for better delineating prevention of colon cancer by nutritional interventions relies heavily on the availability of information about the small molecule constituents of our diet and it can be expanded to any other disease class that previous evidence has linked to lifestyle.     

Capturing time-resolved changes in molecular structure by negative staining

Imaging structural intermediates of biological processes is a key step in understanding biological function. Because intermediates are commonly short-lived, lasting only milliseconds, the main methods used to capture them have been conventional imaging of analog or inhibited states, having extended lifetimes, or rapid (millisecond timescale) freezing of intermediates with subsequent observation by cryo-EM. We have developed a simpler method that fixes structure on the millisecond timescale. The procedure consists of briefly (milliseconds) exposing the macromolecular structure of interest on an EM grid to conditions that initiate the structural change, then immediately fixing with uranyl acetate or tannic acid. Specimens are then observed by negative staining. The key finding that validates this approach is our demonstration that uranyl acetate, and in some cases tannic acid, fixes protein molecular structure on the millisecond timescale. This is demonstrated by our observation that exposure of actin and myosin filaments to these fixatives for as little as 10 ms is sufficient to fully preserve them against changes that normally induce rapid and major alteration in their molecular structure. Fixation appears to stabilize both ionic and hydrophobic bonds. This approach should be of general utility for studying transient molecular changes in many systems.     

Strategies for dealing with incomplete information in the modeling of molecular interaction networks

Modelers of molecular interaction networks encounter the paradoxical situation that while large amounts of data are available, these are often insufficient for the formulation and analysis of mathematical models describing the network dynamics. In particular, information on the reaction mechanisms and numerical values of kinetic parameters are usually not available for all but a few well-studied model systems. In this article we review two strategies that have been proposed for dealing with incomplete information in the study of molecular interaction networks: parameter sensitivity analysis and model simplification. These strategies are based on the biologically justified intuition that essential properties of the system dynamics are robust against moderate changes in the value of kinetic parameters or even in the rate laws describing the interactions. Although advanced measurement techniques can be expected to relieve the problem of incomplete information to some extent, the strategies discussed in this article will retain their interest as tools providing an initial characterization of essential properties of the network dynamics.     

Discovering functional interaction patterns in protein-protein interaction networks

Background  

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

Protein interaction networks in bacteria

The complement of expressed cellular proteins - the proteome - is organized into functional, structured networks of protein interactions that mediate assembly of molecular machines and dynamic cellular pathways. Recent studies reveal the biological roles of protein interactions in bacteriophage T7 and Helicobacter pylori, and new methods allow to compare and to predict interaction networks in other species. Smaller scale networks provide biological insights into DNA replication and chromosome dynamics in Bacillus subtilis and Archeoglobus fulgidus, and into the assembly of multiprotein complexes such as the type IV secretion system of Agrobacterium tumefaciens, and the cell division machinery of Escherichia coli. Genome-wide interaction networks in several species are needed to obtain a biologically meaningful view of the higher order organization of the proteome in bacteria.     

Protein interaction networks in plants

Protein–protein interactions are fundamental to virtually every aspect of cellular functions. With the development of high-throughput technologies of both the yeast two-hybrid system and tandem mass spectrometry, genome-wide protein-linkage mapping has become a major objective in post-genomic research. While at least partial “interactome” networks of several model organisms are already available, in the plant field, progress in this respect is slow. However, even with comprehensive protein interaction data still missing, substantial recent advance in the graph-theoretical functional interpretation of complex network architectures might pave the way for novel approaches in plant research. This article reviews current progress and discussions in network biology. Emphasis is put on the question of what can be learned about protein functions and cellular processes by studying the topology of complex protein interaction networks and the evolutionary mechanisms underlying their development. Particularly the intermediate and local levels of network organization—the modules, motifs and cliques—are increasingly recognized as the operational units of biological functions. As demonstrated by some recent results from systematic analyses of plant protein families, protein interaction networks promise to be a valuable tool for a molecular understanding of functional specificities and for identifying novel regulatory components and pathways.     

Extending pathways and processes using molecular interaction networks to analyse cancer genome data

Background  

Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.     

Capturing VCP: another molecular piece in the ALS jigsaw puzzle

TDP-43 mislocalization and aggregation are implicated in the pathogenesis of ALS and FTLD-U. Valosin containing protein (VCP) mutations also lead to TDP-43 deposition, resulting in Inclusion Body Myopathy, Paget disease, and Frontotemporal Dementia (IBMPFD). In this issue of Neuron, Johnson et?al. used whole-exome capture to identify VCP mutations in familial ALS. This extends the VCP phenotype to include motor neuron degeneration and provides another molecular tool to explore neurodegeneration disease mechanisms underlying the TDP-43 proteinopathies.     

Intersectin (ITSN) family of scaffolds function as molecular hubs in protein interaction networks

Members of the intersectin (ITSN) family of scaffold proteins consist of multiple modular domains, each with distinct ligand preferences. Although ITSNs were initially implicated in the regulation of endocytosis, subsequent studies have revealed a more complex role for these scaffold proteins in regulation of additional biochemical pathways. In this study, we performed a high throughput yeast two-hybrid screen to identify additional pathways regulated by these scaffolds. Although several known ITSN binding partners were identified, we isolated more than 100 new targets for the two mammalian ITSN proteins, ITSN1 and ITSN2. We present the characterization of several of these new targets which implicate ITSNs in the regulation of the Rab and Arf GTPase pathways as well as regulation of the disrupted in schizophrenia 1 (DISC1) interactome. In addition, we demonstrate that ITSN proteins form homomeric and heteromeric complexes with each other revealing an added level of complexity in the function of these evolutionarily conserved scaffolds.     

mTOR complexes -- molecular spiders in molecular networks

PI3K route is one of the most outstanding signal transduction pathways, which has a key role in the decision-making processes and functions of a cell. In this network mTOR (mammalian target of rapamycin) is a well-known integrator. mTOR forms two complexes, and their increased activity is present in many human tumors. Therefore, mTOR inhibitors became more and more important in the targeted therapy, first of all in the treatment of renal cancer, neuroendocrine pancreatic cancer and certain astrocytomas, and many trials are going on in other tumor types. The therapeutic results are obvious, but problems also occur, which lead to new strategies and to the development of new drugs in order to create more individualised cancer therapy.     

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.     

Abundance and nestedness in interaction networks

Nestedness is a useful metric that characterizes the generalist–specialist balance in ecological communities. Although several nestedness indices have been proposed, few have explored how species abundance per se affects their performance and the ability to detect true interaction networks. We here develop a mathematical framework that takes into account abundance in estimates of nestedness. We use an analytical approach to relate abundance and nestedness. In our null model the probability of interaction among species is determined solely as function of their abundances. Assuming a power-law abundance model we analytically find the nestedness index and its coefficient of variability. We find that the sloping abundance distribution of our null model generates more nested structures. On the other hand steeper abundances lead to higher coefficients of variability. Both results suggest that nestedness analysis should be evaluated and explanations sought carefully.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.