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.     

Analyzing protein–protein interactions in the post-interactomic era. Are we ready for the endgame?

Mapping protein–protein interactions in genome-wide scales revealed thousands of novel binding partners in each of the explored model organisms. Organizing these hits in comprehensive ways is becoming increasingly important for systems biology approaches to understand complex cellular processes and diseases. However, proteome wide interaction techniques and their resulting global networks are not revealing the topologies of networks that are truly operating in the cell. In this short review I will discuss which prerequisites have to be fulfilled and which experimental methods might be practicable to translate primary protein interaction data into network presentations that help in understanding cellular processes.     

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.     

Methods for mapping of interaction networks involving membrane proteins

Nearly one-third of all genes in various organisms encode membrane-associated proteins that participate in numerous protein-protein interactions important to the processes of life. However, membrane protein interactions pose significant challenges due to the need to solubilize membranes without disrupting protein-protein interactions. Traditionally, analysis of isolated protein complexes by high-resolution 2D gel electrophoresis has been the main method used to obtain an overall picture of proteome constituents and interactions. However, this method is time consuming, labor intensive, detects only abundant proteins and is limited with respect to the coverage required to elucidate large interaction networks. In this review, we discuss the application of various methods to elucidate interactions involving membrane proteins. These techniques include methods for the direct isolation of single complexes or interactors as well as methods for characterization of entire subcellular and cellular interactomes.     

A map of WW domain family interactions

WW domains are protein modules that bind proline-rich ligands. WW domain-ligand complexes are of importance as they have been implicated in several human diseases such as muscular dystrophy, cancer, hypertension, Alzheimer's, and Huntington's diseases. We report the results of a protein array aimed at mapping all the human WW domain protein-protein interactions. Our biochemical approach integrates parallel synthesis of peptides, protein expression, and high-throughput screening methodology combined with tools of bioinformatics. The results suggest that the majority of the bioinformatically predicted WW peptide ligands and most WW domains are functional, and that only about 10% of the measured domain-ligand interactions are positive. The analysis of the WW domain protein arrays also underscores the importance of the amino acid residues surrounding the WW ligand core motifs for specific binding to WW domains. In addition, the methodology presented here allows for the rapid elucidation of WW domain-ligand interactions with multiple applications including prediction of exact WW ligand binding sites, which can be applied to the mapping of other protein signaling domain families. Such information can be applied to the generation of protein interaction networks and identification of potential drug targets. To our knowledge, this report describes the first protein-protein interaction map of a domain in the human proteome.     

Interaction networks for systems biology

Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.     

Hsp33 Controls Elongation Factor-Tu Stability and Allows Escherichia coli Growth in the Absence of the Major DnaK and Trigger Factor Chaperones

Intracellular de novo protein folding is assisted by cellular networks of molecular chaperones. In Escherichia coli, cooperation between the chaperones trigger factor (TF) and DnaK is central to this process. Accordingly, the simultaneous deletion of both chaperone-encoding genes leads to severe growth and protein folding defects. Herein, we took advantage of such defective phenotypes to further elucidate the interactions of chaperone networks in vivo. We show that disruption of the TF/DnaK chaperone pathway is efficiently rescued by overexpression of the redox-regulated chaperone Hsp33. Consistent with this observation, the deletion of hslO, the Hsp33 structural gene, is no longer tolerated in the absence of the TF/DnaK pathway. However, in contrast with other chaperones like GroEL or SecB, suppression by Hsp33 was not attributed to its potential overlapping general chaperone function(s). Instead, we show that overexpressed Hsp33 specifically binds to elongation factor-Tu (EF-Tu) and targets it for degradation by the protease Lon. This synergistic action of Hsp33 and Lon was responsible for the rescue of bacterial growth in the absence of TF and DnaK, by presumably restoring the coupling between translation and the downstream folding capacity of the cell. In support of this hypothesis, we show that overexpression of the stress-responsive toxin HipA, which inhibits EF-Tu, also rescues bacterial growth and protein folding in the absence of TF and DnaK. The relevance for such a convergence of networks of chaperones and proteases acting directly on EF-Tu to modulate the intracellular rate of protein synthesis in response to protein aggregation is discussed.     

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.     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.     

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.     

Bimolecular fluorescence complementation (BiFC) analysis of protein interactions in Caenorhabditis elegans

Protein interactions are essential components of signal transduction in cells. With the progress in genome-wide yeast two hybrid screens and proteomics analyses, many protein interaction networks have been generated. These analyses have identified hundreds and thousands of interactions in cells and organisms, creating a challenge for further validation under physiological conditions. The bimolecular fluorescence complementation (BiFC) assay is such an assay that meets this need. The BiFC assay is based on the principle of protein fragment complementation, in which two non-fluorescent fragments derived from a fluorescent protein are fused to a pair of interacting partners. When the two partners interact, the two non-fluorescent fragments are brought into proximity and an intact fluorescent protein is reconstituted. Hence, the reconstituted fluorescent signals reflect the interaction of two proteins under study. Over the past six years, the BiFC assay has been used for visualization of protein interactions in living cells and organisms, including our application of the BiFC assay to the transparent nematode Caenorhabditis elegans. We have demonstrated that BiFC analysis in C. elegans provides a direct means to identify and validate protein interactions in living worms and allows visualization of temporal and spatial interactions. Here, we provide a guideline for the implementation of BiFC analysis in living worms and discuss the factors that are critical for BiFC analysis.     

The nature of protein domain evolution: shaping the interaction network

The proteomes that make up the collection of proteins in contemporary organisms evolved through recombination and duplication of a limited set of domains. These protein domains are essentially the main components of globular proteins and are the most principal level at which protein function and protein interactions can be understood. An important aspect of domain evolution is their atomic structure and biochemical function, which are both specified by the information in the amino acid sequence. Changes in this information may bring about new folds, functions and protein architectures. With the present and still increasing wealth of sequences and annotation data brought about by genomics, new evolutionary relationships are constantly being revealed, unknown structures modeled and phylogenies inferred. Such investigations not only help predict the function of newly discovered proteins, but also assist in mapping unforeseen pathways of evolution and reveal crucial, co-evolving inter- and intra-molecular interactions. In turn this will help us describe how protein domains shaped cellular interaction networks and the dynamics with which they are regulated in the cell. Additionally, these studies can be used for the design of new and optimized protein domains for therapy. In this review, we aim to describe the basic concepts of protein domain evolution and illustrate recent developments in molecular evolution that have provided valuable new insights in the field of comparative genomics and protein interaction networks.     

Four-dimensional visualisation and analysis of protein-protein interaction networks

Protein-protein interaction networks are typically built with interactions collated from many experiments. These networks are thus composite and show all interactions that are currently known to occur in a cell. However, these representations are static and ignore the constant changes in protein-protein interactions. Here we present software for the generation and analysis of dynamic, four-dimensional (4-D) protein interaction networks. In this, time-course-derived abundance data are mapped onto three-dimensional networks to generate network movies. These networks can be navigated, manipulated and queried in real time. Two types of dynamic networks can be generated: a 4-D network that maps expression data onto protein nodes and one that employs 'real-time rendering' by which protein nodes and their interactions appear and disappear in association with temporal changes in expression data. We illustrate the utility of this software by the analysis of singlish interface date hub interactions during the yeast cell cycle. In this, we show that proteins MLC1 and YPT52 show strict temporal control of when their interaction partners are expressed. Since these proteins have one and two interaction interfaces, respectively, it suggests that temporal control of gene expression may be used to limit competition at the interaction interfaces of some hub proteins. The software and movies of the 4-D networks are available at http://www.systemsbiology.org.au/downloads_geomi.html.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Self-assembly of repeat proteins: Concepts and design of new interfaces

In nature, assembled protein structures offer the most complex functional structures. The understanding of the mechanisms ruling protein–protein interactions opens the door to manipulate protein assemblies in a rational way. Proteins are versatile scaffolds with great potential as tools in nanotechnology and biomedicine because of their chemical, structural, and functional versatility. Currently, bottom-up self-assembly based on biomolecular interactions of small and well-defined components, is an attractive approach to biomolecular engineering and biomaterial design. Specifically, repeat proteins are simplified systems for this purpose.In this work, we provide an overview of fundamental concepts of the design of new protein interfaces. We describe an experimental approach to form higher order architectures by a bottom-up assembly of repeated building blocks. For this purpose, we use designed consensus tetratricopeptide repeat proteins (CTPRs). CTPR arrays contain multiple identical repeats that interact through a single inter-repeat interface to form elongated superhelices. Introducing a novel interface along the CTPR superhelix allows two CTPR molecules to assemble into protein nanotubes. We apply three approaches to form protein nanotubes: electrostatic interactions, hydrophobic interactions, and π-π interactions. We isolate and characterize the stability and shape of the formed dimers and analyze the nanotube formation considering the energy of the interaction and the structure in the three different models. These studies provide insights into the design of novel protein interfaces for the control of the assembly into more complex structures, which will open the door to the rational design of nanostructures and ordered materials for many potential applications in nanotechnology.     

Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins

Water molecules and molecular chaperones efficiently help the protein folding process. Here we describe their action in the context of the energy and topological networks of proteins. In energy terms water and chaperones were suggested to decrease the activation energy between various local energy minima smoothing the energy landscape, rescuing misfolded proteins from conformational traps and stabilizing their native structure. In kinetic terms water and chaperones may make the punctuated equilibrium of conformational changes less punctuated and help protein relaxation. Finally, water and chaperones may help the convergence of multiple energy landscapes during protein-macromolecule interactions. We also discuss the possibility of the introduction of protein games to narrow the multitude of the energy landscapes when a protein binds to another macromolecule. Both water and chaperones provide a diffuse set of rapidly fluctuating weak links (low affinity and low probability interactions), which allow the generalization of all these statements to a multitude of networks.     

蛋白质芯片技术

以前对蛋白质的研究集中在一次研究一种蛋白质 ,通常费时费力 ;而蛋白质芯片技术是研究蛋白质组的新技术 ,是高通量、微型化和自动化的蛋白质分析技术。它可以用来研究蛋白质的亚细胞定位和蛋白质与蛋白质之间的相互作用 ,以及对蛋白质的功能进行生物化学分析 ,将对蛋白质组研究及医学生物学的发展有很大的推动作用。较系统地介绍了蛋白质芯片的概念、制作及检测方法 ;同时也讨论了蛋白质芯片的两种功能形式、存在问题和应用前景。     

A common neighbor based technique to detect protein complexes in PPI networks

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.