Reverse MAPPIT detects disruptors of protein-protein interactions in human cells

Reverse mammalian protein-protein interaction trap (MAPPIT) is a mammalian reverse two-hybrid technology. The method is adapted from the forward MAPPIT technique, a two-hybrid complementation system in which the interaction between a bait-fusion protein and a prey-fusion protein restores ligand-dependent cytokine receptor signaling. In the reverse mode described in detail here, a positive readout is generated on disruption of the designated protein-protein interactions. Reverse MAPPIT functions in intact human cells, facilitating simultaneous analysis of disruption, toxicity and permeability of the tested compounds, making it particularly suitable for screening for molecules that target therapeutically interesting protein-protein interactions or for mapping the interaction interface between proteins. The total handling time of a typical reverse MAPPIT experiment is approximately 9 h and is spread over 4-5 d.     

MAPPIT: a protein interaction toolbox built on insights in cytokine receptor signaling

MAPPIT (mammalian protein-protein interaction trap) is a two-hybrid interaction mapping technique based on functional complementation of a type I cytokine receptor signaling pathway. Over the last decade, the technology has been extended into a platform of complementary assays for the detection of interactions among proteins and between chemical compounds and proteins, and for the identification of small molecules that interfere with protein-protein interactions. Additionally, several screening approaches have been developed to broaden the utility of the platform. In this review we provide an overview of the different components of the MAPPIT toolbox and highlight a number of applications in interactomics, drug screening and compound target profiling.     

Protein evolution is faster outside the cell

Some proteins are highly conserved across all species, whereas others diverge significantly even between closely related species. Attempts have been made to correlate the rate of protein evolution to amino acid composition, protein dispensability, and the number of protein-protein interactions, but in all cases, conflicting studies have shown that the theories are hard to confirm experimentally. The only correlation that is undisputed so far is that highly/broadly expressed proteins seem to evolve at a lower rate. Consequently, it has been suggested that correlations between evolution rate and factors like protein dispensability or the number of protein-protein interactions could be just secondary effects due to differences in expression. The purpose of this study was to analyze mammalian proteins/genes with known subcellular location for variations in evolution rates. We show that proteins that are exported (extracellular proteins) evolve faster than proteins that reside inside the cell (intracellular proteins). We find weak, but significant, correlations between evolution rates and expression levels, percentage of tissues in which the proteins are expressed (expression broadness), and the number of protein interaction partners. More important, we show that the observed difference in evolution rate between extra- and intracellular proteins is largely independent of expression levels, expression broadness, and the number of protein-protein interactions. We also find that the difference is not caused by an overrepresentation of immunological proteins or disulfide bridge-containing proteins among the extracellular data set. We conclude that the subcellular location of a mammalian protein has a larger effect on its evolution rate than any of the other factors studied in this paper, including expression levels/patterns. We observe a difference in evolution rates between extracellular and intracellular proteins for a yeast data set as well and again show that it is completely independent of expression levels.     

Monitoring protein-protein interactions in mammalian cells by trans-SUMOylation

Protein-protein interactions are essential for almost all cellular processes, hence understanding these processes mainly depends on the identification and characterization of the relevant protein-protein interactions. In the present paper, we introduce the concept of TRS (trans-SUMOylation), a new method developed to identify and verify protein-protein interactions in mammalian cells in vivo. TRS utilizes Ubc9-fusion proteins that trans-SUMOylate co-expressed interacting proteins. Using TRS, we analysed interactions of 65 protein pairs co-expressed in HEK (human embryonic kidney)-293 cells. We identified seven new and confirmed 16 known protein interactions, which were determined via endogenous SUMOylation sites of the binding partners or by using SUMOylation-site tags respectively. Four of the new protein interactions were confirmed by GST (glutathione transferase) pull-down and the p38α-Edr2 interaction was verified by co-localization analysis. Functionally, this p38α-Edr2 interaction could possibly be involved in the recruitment of p38α to the polycomb chromatin-remodelling complex to phosphorylate Bmi1. We also used TRS to characterize protein-interaction domains of the protein kinase pairs p38α-MK2 [MK is MAPK (mitogen-activated protein kinase)-activated protein kinase] and ERK3 (extracellular-signal-regulated kinase 3)-MK5 and of the p38α-p53 complex. The ability of TRS to monitor protein interactions in mammalian cells in vivo at levels similar to endogenous expression makes it an excellent new tool that can help in defining the protein interactome of mammalian cells.     

A network-based analysis of polyanion-binding proteins utilizing human protein arrays

The existence of interactions between many cellular proteins and various polyanionic surfaces within a cell is now well established. The functional role of such interactions, however, remains to be clearly defined. The existence of protein arrays, with a large selection of different kinds of proteins, provides a way to better address a number of aspects of this question. We have therefore investigated the interaction between five cellular polyanions (actin, tubulin, heparin, heparan sulfate, and DNA) and approximately 5,000 human proteins using protein microarrays in an attempt to better understand the functional nature of such interaction(s). We demonstrate that a large number of polyanion-binding proteins exist that contain multiple positively charged regions, are often disordered, are involved in phosphorylation processes, and appear to play a role in protein-protein interaction networks. Considering the crowded nature of cellular interiors, we propose that polyanion-binding proteins interact with a wide variety of polyanionic surfaces in cells in a functionally significant manner.     

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.     

Protein interaction data set highlighted with human Ras-MAPK/PI3K signaling pathways

The Ras-MAPK and PI3K-AKT pathways are conserved in metazoan organisms, which involve a series of signaling cascades and form the basis for numerous physiological and pathological processes. Here we report on yeast two hybrid screening results of a protein interaction network around the known components of human Ras-MAPK/PI3K pathways. A total of 42 independent cDNA library screenings resulted in 200 protein-protein interaction (PPI) pairs among 180 molecules. Most of the proteins formed a large cluster that contains 193 PPIs between 169 proteins. Seventy-four interactions indicate high-confidence according to bioinformatics analysis. The prey list contains high enrichment genes with specific Gene Ontology (GO) terms such as response to stress and response to external stimulus. Most interactions link the Ras signaling pathway with various cellular processes. Five interactions were validated by coimmunoprecipitation and colocalization assays in mammalian cells to confirm their in vivo interactions. This protein interaction network provides further insights into the molecular mechanism of Ras-MAPK/PI3K signaling pathways.     

Quantitative analysis of dynamic protein-protein interactions in planta by a floated-leaf luciferase complementation imaging (FLuCI) assay using binary Gateway vectors

Dynamic protein-protein interactions are essential in all cellular and developmental processes. Protein-fragment complementation assays allow such protein-protein interactions to be investigated in vivo. In contrast to other protein-fragment complementation assays, the split-luciferase (split-LUC) complementation approach facilitates dynamic and quantitative in vivo analysis of protein interactions, as the restoration of luciferase activity upon protein-protein interaction of investigated proteins is reversible. Here, we describe the development of a floated-leaf luciferase complementation imaging (FLuCI) assay that enables rapid and quantitative in vivo analyses of protein interactions in leaf discs floating on a luciferin infiltration solution after transient expression of split-LUC-labelled interacting proteins in Nicotiana benthamiana. We generated a set of eight Gateway-compatible split-LUC destination vectors, enabling fast, and almost fail-safe cloning of candidate proteins to the LUC termini in all possible constellations. We demonstrate their functionality by visualizing the well-established homodimerization of the 14-3-3 regulator proteins. Quantitative interaction analyses of the molybdenum co-factor biosynthesis proteins CNX6 and CNX7 show that the luciferase-based protein-fragment complementation assay allows direct real-time monitoring of absolute values of protein complex assembly. Furthermore, the split-LUC assay is established as valuable tool to investigate the dynamics of protein interactions by monitoring the disassembly of actin filaments in planta. The new Gateway-compatible split-LUC destination vector system, in combination with the FLuCI assay, provides a useful means to facilitate quantitative analyses of interactions between large numbers of proteins constituting interaction networks in plant cells.     

RoXaN, a novel cellular protein containing TPR, LD, and zinc finger motifs, forms a ternary complex with eukaryotic initiation factor 4G and rotavirus NSP3

Rotavirus mRNAs are capped but not polyadenylated, and viral proteins are translated by the cellular translation machinery. This is accomplished through the action of the viral nonstructural protein NSP3, which specifically binds the 3' consensus sequence of viral mRNAs and interacts with the eukaryotic translation initiation factor eIF4G I. To further our understanding of the role of NSP3 in rotavirus replication, we looked for other cellular proteins capable of interacting with this viral protein. Using the yeast two-hybrid assay, we identified a novel cellular protein-binding partner for rotavirus NSP3. This 110-kDa cellular protein, named RoXaN (rotavirus X protein associated with NSP3), contains a minimum of three regions predicted to be involved in protein-protein or nucleic acid-protein interactions. A tetratricopeptide repeat region, a protein-protein interaction domain most often found in multiprotein complexes, is present in the amino-terminal region. In the carboxy terminus, at least five zinc finger motifs are observed, further suggesting the capacity of RoXaN to bind other proteins or nucleic acids. Between these two regions exists a paxillin leucine-aspartate repeat (LD) motif which is involved in protein-protein interactions. RoXaN is capable of interacting with NSP3 in vivo and during rotavirus infection. Domains of interaction were mapped and correspond to the dimerization domain of NSP3 (amino acids 163 to 237) and the LD domain of RoXaN (amino acids 244 to 341). The interaction between NSP3 and RoXaN does not impair the interaction between NSP3 and eIF4G I, and a ternary complex made of NSP3, RoXaN, and eIF4G I can be detected in rotavirus-infected cells, implicating RoXaN in translation regulation.     

A single-chain antibody/epitope system for functional analysis of protein-protein interactions

Protein-protein interactions play a critical role in cellular processes such as signal transduction. Although many methods for identifying the binding partners of a protein of interest are available, it is currently difficult or impossible to assess the functional consequences of a specific interaction in vivo. To address this issue, we propose to modify proteins by addition of an artificial protein binding interface, thereby forcing them to interact in the cell in a pairwise fashion and allowing the functional consequences to be determined. For this purpose, we have developed an artificial binding interface consisting of a anti-Myc single-chain antibody (ScFv) and its peptide epitope. We found that the binding of an ScFv derived from anti-Myc monoclonal antibody 9E10 was relatively weak in vivo, so we selected an improved clone, 3DX, by in vitro mutagenesis and phage display. 3DX bound well to its epitope in a yeast two-hybrid system, and GST-fused 3DX also bound to several Myc-tagged proteins in mammalian cells. In vivo binding was relatively insensitive to the position of the ScFv in a fusion protein, but was improved by including multiple tandem copies of the Myc epitope in the binding partner. To test the system, we successfully replaced the SH3 domain-mediated interaction between the Abl tyrosine kinase and adaptor proteins Crk and Nck with an engineered interaction between 3DX and multiple Myc tags. We expect that this approach, which we term a functional interaction trap, will be a powerful proteomic tool for investigating protein-protein interactions.     

Protein-protein interactions in the synaptonemal complex.

In mammalian systems, an approximately M(r) 30,000 Cor1 protein has been identified as a major component of the meiotic prophase chromosome cores, and a M(r) 125,000 Syn1 protein is present between homologue cores where they are synapsed and form the synaptonemal complex (SC). Immunolocalization of these proteins during meiosis suggests possible homo- and heterotypic interactions between the two as well as possible interactions with yet unrecognized proteins. We used the two-hybrid system in the yeast Saccharomyces cerevisiae to detect possible protein-protein associations. Segments of hamsters Cor1 and Syn1 proteins were tested in various combinations for homo- and heterotypic interactions. In the cause of Cor1, homotypic interactions involve regions capable of coiled-coil formation, observation confirmed by in vitro affinity coprecipitation experiments. The two-hybrid assay detects no interaction of Cor1 protein with central and C-terminal fragments of Syn1 protein and no homotypic interactions involving these fragments of Syn1. Hamster Cor1 and Syn1 proteins both associate with the human ubiquitin-conjugation enzyme Hsubc9 as well as with the hamster Ubc9 homologue. The interactions between SC proteins and the Ubc9 protein may be significant for SC disassembly, which coincides with the repulsion of homologs by late prophase I, and also for the termination of sister centromere cohesiveness at anaphase II.     

Stalking metal-linked dimers

Protein dimerization is essential for cellular processes including regulation and biosignalling. While protein-protein interactions can occur through many modes, this review will focus on those interactions mediated through the binding of metal ions to the proteins. Selected techniques used to study protein-protein interactions, including size exclusion chromatography, mass spectrometry, affinity chromatography, and frontal zone chromatography, are described as applied to the characterization of the Enterococcus hirae protein CopY. CopY forms a homodimer to control the expression of proteins involved in the homeostasis of cellular copper levels. At the center of the CopY dimerization interaction lies a metal binding motif, -CxCxxxxCxC-, capable of binding Zn(II) or Cu(I). The binding of metal to this cysteine hook motif, one within each monomer, is critical to the dimerization interaction. The CopY dimer is also stabilized by hydrophobic interactions between the two monomers. The cysteine hook metal binding motif has been identified in numerous other uncharacterized proteins across the biological spectrum. The prevalence of the motif gives evidence to the biological relevance of this motif, both as a metal binding domain and as a dimerization motif.     

Predicting protein-protein interactions from protein domains using a set cover approach

One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, maximum specificity set cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the maximum likelihood estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi-cse.nd.edu     

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 single PDZ domain protein interacts with the Menkes copper ATPase, ATP7A. A new protein implicated in copper homeostasis

The homeostatic regulation of essential elements such as copper requires many proteins whose activities are often mediated and tightly coordinated through protein-protein interactions. This regulation ensures that cells receive enough copper without intracellular concentrations reaching toxic levels. To date, only a small number of proteins implicated in copper homeostasis have been identified, and little is known of the protein-protein interactions required for this process. To identify other proteins important for copper homeostasis, while also elucidating the protein-protein interactions that are integral to the process, we have utilized a known copper protein, the copper ATPase ATP7A, as a bait in a yeast two-hybrid screen of a human cDNA library to search for interacting partners. One of the ATP7A-interacting proteins identified is a novel protein with a single PDZ domain. This protein was recently identified to interact with the plasma membrane calcium ATPase b-splice variants. We propose a change in name for this protein from PISP (plasma membrane calcium ATPase-interacting single-PDZ protein) to AIPP1 (ATPase-interacting PDZ protein) and suggest that it represents the protein that interacts with the class I PDZ binding motif identified at the ATP7A C terminus. The interaction in mammalian cells was confirmed and an additional splice variant of AIPP1 was identified. This study represents an essential step forward in identifying the proteins and elucidating the network of protein-protein interactions involved in maintaining copper homeostasis and validates the use of the yeast two-hybrid approach for this purpose.     

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.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

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.