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.     

In silico discovery and experimental validation of new protein-protein interactions

We introduce a framework for predicting novel protein-protein interactions (PPIs), based on Fisher's method for combining probabilities of predictions that are based on different data sources, such as the biomedical literature, protein domain and mRNA expression information. Our method compares favorably to our previous method based on text-mining alone and other methods such as STRING. We evaluated our algorithms through the prediction of experimentally found protein interactions underlying Muscular Dystrophy, Huntington's Disease and Polycystic Kidney Disease, which had not yet been recorded in protein-protein interaction databases. We found a 1.74-fold increase in the mean average prediction precision for dysferlin and a 3.09-fold for huntingtin when compared to STRING. The top 10 of predicted interaction partners of huntingtin were analysed in depth. Five were identified previously, and the other five were new potential interaction partners. The full matrix of human protein pairs and their prediction scores are available for download. Our framework can be extended to predict other types of relationships such as proteins in a complex, pathway or related disease mechanisms.     

Quantitative assessment of the structural bias in protein-protein interaction assays

With recent publications of several large-scale protein-protein interaction (PPI) studies, the realization of the full yeast interaction network is getting closer. Here, we have analysed several yeast protein interaction datasets to understand their strengths and weaknesses. In particular, we investigate the effect of experimental biases on some of the protein properties suggested to be enriched in highly connected proteins. Finally, we use support vector machines (SVM) to assess the contribution of these properties to protein interactivity. We find that protein abundance is the most important factor for detecting interactions in tandem affinity purifications (TAP), while it is of less importance for Yeast Two Hybrid (Y2H) screens. Consequently, sequence conservation and/or essentiality of hubs may be related to their high abundance. Further, proteins with disordered structure are over-represented in Y2H screens and in one, but not the other, large-scale TAP assay. Hence, disordered regions may be important both in transient interactions and interactions in complexes. Finally, a few domain families seem to be responsible for a large part of all interactions. Most importantly, we show that there are method-specific biases in PPI experiments. Thus, care should be taken before drawing strong conclusions based on a single dataset.     

History of protein-protein interactions: from egg-white to complex networks

Today, it is widely appreciated that protein-protein interactions play a fundamental role in biological processes. This was not always the case. The study of protein interactions started slowly and evolved considerably, together with conceptual and technological progress in different areas of research through the late 19th and the 20th centuries. In this review, we present some of the key experiments that have introduced major conceptual advances in biochemistry and molecular biology, and review technological breakthroughs that have paved the way for today's systems-wide approaches to protein-protein interaction analysis.     

An evaluation of in vitro protein-protein interaction techniques: assessing contaminating background proteins

Determination of protein-protein interactions is an important component in assigning function and discerning the biological relevance of proteins within a broader cellular context. In vitro protein-protein interaction methodologies, including affinity chromatography, coimmunoprecipitation, and newer approaches such as protein chip arrays, hold much promise in the detection of protein interactions, particularly in well-characterized organisms with sequenced genomes. However, each of these approaches attracts certain background proteins that can thwart detection and identification of true interactors. In addition, recombinant proteins expressed in Escherichia coli are also extensively used to assess protein-protein interactions, and background proteins in these isolates can thus contaminate interaction studies. Rigorous validation of a true interaction thus requires not only that an interaction be found by alternate techniques, but more importantly that researchers be aware of and control for matrix/support dependence. Here, we evaluate these methods for proteins interacting with DmsD (an E. coli redox enzyme maturation protein chaperone), in vitro, using E. coli subcellular fractions as prey sources. We compare and contrast the various in vitro interaction methods to identify some of the background proteins and protein profiles that are inherent to each of the methods in an E. coli system.     

Detection of transient protein-protein interactions by bimolecular fluorescence complementation: the Abl-SH3 case

Protein-protein interactions are essential in most biological processes. Many proteomic approaches have succeeded in the identification of strong and obligatory interactions but the study of weak and transient protein-protein interactions is still a challenge. The aim of the present study was to test the ability of bimolecular fluorescence complementation to detect and discriminate in vivo weak intracellular protein interactions. As a test case, the interaction of the SH3 domain from the c-Abl tyrosine kinase with both natural and designed targets has been chosen. The reassociation of functional yellow fluorescent protein (YFP) from its fragments requires previous binding between the SH3 domain and its partners; but once this occurs, the complex is trapped, turning transient SH3 interactions into stable, easily detectable ones. The method is very sensitive and can be implemented for proteomic analysis of weak protein interactions using flow cytometry. The fluorescence emission is dependent on the strength of the interaction, in such a way that it can be used, at least qualitatively, to screen for best binding candidates among similar proline-rich peptides. In addition, it is illustrated how this method can be used to gain structural insights into particular c-Abl SH3 interactions.     

Measurable notions of complexity and their relationship to biological complexity

Complexity is often invoked as a motivation for a systems approach to biology. We review three measurable notions of complexity from the areas of computation and data analysis. These measures have each led to mathematical theory and to further insight on the complexity of objects, demonstrating the benefits of having a well-defined measure of complexity. Each measure is applicable in the study of particular biological systems; however, none is satisfactory to serve as a universal measure of biological complexity. The study of biological systems will likely require numerous measures of complexity, each appropriate for analysis in specific settings.     

Landscape of the hnRNP K protein-protein interactome

The heterogeneous nuclear ribonucleoprotein K is an ancient RNA/DNA-binding protein that is involved in multiple processes that compose gene expression. The pleiotropic action of K protein reflects its ability to interact with different classes of factors, interactions that are regulated by extracellular signals. We used affinity purification and MS to better define the repertoire of K protein partners. We identified a large number of new K protein partners, some typically found in subcellular compartments, such as plasma membrane, where K protein has not previously been seen. Electron microscopy showed K protein in the nucleus, cytoplasm, mitochondria, and in vicinity of plasma membrane. These observations greatly expanded the view of the landscape of K protein-protein interaction and provide new opportunities to explore signal transduction and gene expression in several subcellular compartments.     

Identifying transient protein-protein interactions in EphB2 signaling by blue native PAGE and mass spectrometry

Receptor tyrosine kinases (RTKs) are proteins that upon ligand stimulation undergo dimerization and autophosphorylation. Eph receptors (EphRs) are RTKs that are found in different cell types, from both tissues that are developing and from mature tissues, and play important roles in the development of the central nervous system and peripheral nervous system. EphRs also play roles in synapse formation, neural crest formation, angiogenesis and in remodeling the vascular system. Interaction of EphRs with their ephrin ligands lead to activation of signal transduction pathways and formation of many transient protein-protein interactions that ultimately leads to cytoskeletal remodeling. However, the sequence of events at the molecular level is not well understood. We used blue native PAGE and MS to analyze the transient protein-protein interactions that resulted from the stimulation of EphB2 receptors by their ephrinB1-Fc ligands. We analyzed the phosphotyrosine-containing protein complexes immunoprecipitated from the cell lysates of both unstimulated (-) and ephrinB1-Fc-stimulated (+) NG108 cells. Our experiments allowed us to identify many signaling proteins, either known to be part of EphB2 signaling or new for this pathway, which are involved in transient protein-protein interactions upon ephrinB1-Fc stimulation. These data led us to investigate the roles of proteins such as FAK, WAVEs and Nischarin in EphB2 signaling.     

Predicting interacting and interfacial residues using continuous sequence segments

Development of sequence-based methods for predicting putative interfacial residues is an extremely important task in modeling 3D structures of protein–protein complexes. In the present paper we used non-gapped sequence segments to predict both interacting and interfacial residues. We demonstrated that continuous sequence segments do occur at the protein–protein interfaces and showed that continuous interacting interfacial segments (CIIS) of length nine are presented on average, in 37% of the complexes in our dataset. Our results indicate that CIIS consist mostly of interacting strands and/or loops, while the CIIS involving the helixes are scarce. We performed scoring of CIIS using four different scoring mechanisms and found that scores of CIIS differ significantly from the scores calculated for random stretches of residues. We argue that such statistical difference inferred thought the corresponding Z-scores could be used for detecting putative interfacial residue segments without using any structural information. This hypothesis was tested on our dataset and benchmarking resulted to 10–60% prediction accuracy depending on type of benchmarking and scoring scheme used in calculations. Such predictions that do not depend on the availability of the 3D structures of monomers can be quite valuable in modeling 3D structures of obligatory complexes, for which structures of separated monomers do not exist.     

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest‐ranked proteins, beta‐arrestin 1, and beta‐arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost‐effective means to identify additional proteins that remain elusive for current 2D gel‐based proteomic profiling techniques.     

Visualization of the interactome: What are we looking at?

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein-protein interactions, it also serves as a biological context for gaining insights from high-throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic trade-off between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D and dynamic networks.     

Strategies for membrane interaction proteomics: no mass spectrometry required

Membrane-bound proteins are one of the most important protein types in the cell, and are involved in many major cell processes and signaling pathways. Most proteins, including those at membranes, must interact with other proteins to form complexes, which are essential for their function(s). In this review, we describe some of the major non-mass spectrometry-based methods and technologies used for the investigation of intracellular membrane protein complexes including Tango, fluorescence/bioluminescence resonance energy transfer (F/BRET), luminescence-based mammalian interactome mapping (LUMIER), protein-fragment complementation assay (PCA), and membrane yeast two-hybrid assay (MYTH). We highlight the advantages and drawbacks of these methods, describe recent studies utilizing these methods, and discuss some of the major findings in the study of membrane protein-based cell pathways.     

Membrane-SPINE: an improved method to identify protein-protein interaction partners of membrane proteins in vivo

Membrane proteins are crucial for many essential cellular processes. As membrane proteins function in complexes, methods to detect and to characterize membrane protein-protein interactions are undoubtedly required. Therefore, we developed the "Membrane-Strep-tagged protein interaction experiment" (Membrane-SPINE) that combines the specific purification of a Strep-tagged membrane protein with the reversible fixation of protein complexes by formaldehyde cross-linking. In combination with MS analysis, we suggest Membrane-SPINE as a powerful tool to identify unknown interaction partners of membrane proteins in vivo.     

SPINE: a method for the rapid detection and analysis of protein-protein interactions in vivo

The detection and analysis of protein-protein interactions is one of the central tasks of proteomics in the postgenomic era. For this purpose, we present a procedure, the Strep-protein interaction experiment (SPINE) that combines the advantages of the Strep-tag protein purification system with those of reversible in vivo protein crosslinking by formaldehyde. Using two Bacillus subtilis regulator proteins, we demonstrate that this method is well suited to isolate protein complexes with high purity and virtually no background. Plasmids allowing the high-level expression of proteins carrying an N- or C-terminal Strep-tag in B. subtilis were constructed.     

Delineating protein-protein interactions via biomolecular interaction analysis-mass spectrometry

The utility of biomolecular interaction analysis-mass spectrometry (BIA/MS) in screening for protein-protein interactions was explored in this work. Experiments were performed in which proteins served as ligands for screening of possible interactions with other proteins from human plasma and urine. The proteins utilized were beta-2-microglobulin, cystatin C (cysC), retinol binding protein (RBP), transthyretin (TTR), alpha-1-microglobulin, C-reactive protein, transferrin and papain. The immobilization of functionally active proteins was confirmed via interactions with antibodies to the corresponding proteins. Various dilutions of human urine and plasma were injected over the protein-derivatized surfaces. It was observed that the urine injections generally yielded smaller SPR responses than those observed after the plasma injections. The BIA/MS experiments did not reveal novel protein-protein interactions, although several established interactions (such as those between RBP and TTR, and cysC and papain) were validated. Few protein ligand deficiencies (such as truncations) leading to false negative and false positive BIA/MS results were also discovered.     

Shadows of complexity: what biological networks reveal about epistasis and pleiotropy

Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene–gene interaction, has also been treated as an exception to the Mendelian one gene–one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena.