Relative quantification of protein-protein interactions using a dual luciferase reporter pull-down assay system

The identification and quantitative analysis of protein-protein interactions are essential to the functional characterization of proteins in the post-proteomics era. The methods currently available are generally time-consuming, technically complicated, insensitive and/or semi-quantitative. The lack of simple, sensitive approaches to precisely quantify protein-protein interactions still prevents our understanding of the functions of many proteins. Here, we develop a novel dual luciferase reporter pull-down assay by combining a biotinylated Firefly luciferase pull-down assay with a dual luciferase reporter assay. The biotinylated Firefly luciferase-tagged protein enables rapid and efficient isolation of a putative Renilla luciferase-tagged binding protein from a relatively small amount of sample. Both of these proteins can be quantitatively detected using the dual luciferase reporter assay system. Protein-protein interactions, including Fos-Jun located in the nucleus; MAVS-TRAF3 in cytoplasm; inducible IRF3 dimerization; viral protein-regulated interactions, such as MAVS-MAVS and MAVS-TRAF3; IRF3 dimerization; and protein interaction domain mapping, are studied using this novel assay system. Herein, we demonstrate that this dual luciferase reporter pull-down assay enables the quantification of the relative amounts of interacting proteins that bind to streptavidin-coupled beads for protein purification. This study provides a simple, rapid, sensitive, and efficient approach to identify and quantify relative protein-protein interactions. Importantly, the dual luciferase reporter pull-down method will facilitate the functional determination of proteins.     

Identification of protein-protein interactions and topologies in living cells with chemical cross-linking and mass spectrometry

We present results from a novel strategy that enables concurrent identification of protein-protein interactions and topologies in living cells without specific antibodies or genetic manipulations for immuno-/affinity purifications. The strategy consists of (i) a chemical cross-linking reaction: intact cell labeling with a novel class of chemical cross-linkers, protein interaction reporters (PIRs); (ii) two-stage mass spectrometric analysis: stage 1 identification of PIR-labeled proteins and construction of a restricted database by two-dimensional LC/MSMS and stage 2 analysis of PIR-labeled peptides by multiplexed LC/FTICR-MS; and (iii) data analysis: identification of cross-linked peptides and proteins of origin using accurate mass and other constraints. The primary advantage of the PIR approach and distinction from current technology is that protein interactions together with topologies are detected in native biological systems by stabilizing protein complexes with new covalent bonds while the proteins are present in the original cellular environment. Thus, weak or transient interactions or interactions that require properly folded, localized, or membrane-bound proteins can be labeled and identified through the PIR approach. This strategy was applied to Shewanella oneidensis bacterial cells, and initial studies resulted in identification of a set of protein-protein interactions and their contact/binding regions. Furthermore most identified interactions involved membrane proteins, suggesting that the PIR approach is particularly suited for studies of membrane protein-protein interactions, an area under-represented with current widely used approaches.     

Rapid identification of protein-protein interfaces for the construction of a complex model based on multiple unassigned signals by using time-sharing NMR measurements

Protein-protein interactions are necessary for various cellular processes, and therefore, information related to protein-protein interactions and structural information of complexes is invaluable. To identify protein-protein interfaces using NMR, resonance assignments are generally necessary to analyze the data; however, they are time consuming to collect, especially for large proteins. In this paper, we present a rapid, effective, and unbiased approach for the identification of a protein-protein interface without resonance assignments. This approach requires only a single set of 2D titration experiments of a single protein sample, labeled with a unique combination of an (15)N-labeled amino acid and several amino acids (13)C-labeled on specific atoms. To rapidly obtain high resolution data, we applied a new pulse sequence for time-shared NMR measurements that allowed simultaneous detection of a ω(1)-TROSY-type backbone (1)H-(15)N and aromatic (1)H-(13)C shift correlations together with single quantum methyl (1)H-(13)C shift correlations. We developed a structure-based computational approach, that uses our experimental data to search the protein surfaces in an unbiased manner to identify the residues involved in the protein-protein interface. Finally, we demonstrated that the obtained information of the molecular interface could be directly leveraged to support protein-protein docking studies. Such rapid construction of a complex model provides valuable information and enables more efficient biochemical characterization of a protein-protein complex, for instance, as the first step in structure-guided drug development.     

MULTIPROSPECTOR: an algorithm for the prediction of protein-protein interactions by multimeric threading

In this postgenomic era, the ability to identify protein-protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein-protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z-score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI-BLAST using a (less) permissive E-value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein-protein interactions are made; 1138 of these 2865 interactions have counterparts in the Database of Interacting Proteins. In contrast, PSI-BLAST made 1781 predictions, and 1215 have counterparts in DIP. An estimation of the false-negative rate for yeast-predicted interactions has also been provided. Thus, a promising approach to help assist in the assignment of protein-protein interactions on a genomic scale has been developed.     

Yeast surface display for protein engineering and characterization

Yeast surface display is being employed to engineer desirable properties into proteins for a broad variety of applications. Labeling with soluble ligands enables rapid and quantitative analysis of yeast-displayed libraries by flow cytometry, while cell-surface selections allow screening of libraries with insoluble or even as-yet-uncharacterized binding targets. In parallel, the utilization of yeast surface display for protein characterization, including in particular the mapping of functional epitopes mediating protein-protein interactions, represents a significant recent advance.     

Alkynyl-farnesol reporters for detection of protein S-prenylation in cells

Protein S-prenylation is a lipid modification that regulates membrane-protein and protein-protein interactions in cell signaling. Though sites of protein S-prenylation can be predicted based upon conserved C-terminal CaaX or CC/CXC motifs, biochemical detection of protein S-prenylation in cells is still challenging. Herein, we report an alkynyl-isoprenol chemical reporter (alk-FOH) as an efficient substrate for prenyltransferases in mammalian cells that enables sensitive detection of S-farnesylated and S-geranylgeranylated proteins using bioorthogonal ligation methods. Fluorescent detection alleviates the need to deplete cellular isoprenoids for biochemical analysis of S-prenylated proteins and enables robust characterization of S-prenylated proteins, such as effectors that are injected into host cells by bacterial pathogens. This alkynyl-prenylation reporter provides a sensitive tool for biochemical analysis and rapid profiling of prenylated proteins in cells.     

Bioluminescence resonance energy transfer (BRET) for the real-time detection of protein-protein interactions

A substantial range of protein-protein interactions can be readily monitored in real time using bioluminescence resonance energy transfer (BRET). The procedure involves heterologous coexpression of fusion proteins, which link proteins of interest to a bioluminescent donor enzyme or acceptor fluorophore. Energy transfer between these proteins is then detected. This protocol encompasses BRET1, BRET2 and the recently described eBRET, including selection of the donor, acceptor and substrate combination, fusion construct generation and validation, cell culture, fluorescence and luminescence detection, BRET detection and data analysis. The protocol is particularly suited to studying protein-protein interactions in live cells (adherent or in suspension), but cell extracts and purified proteins can also be used. Furthermore, although the procedure is illustrated with references to mammalian cell culture conditions, this protocol can be readily used for bacterial or plant studies. Once fusion proteins are generated and validated, the procedure typically takes 48-72 h depending on cell culture requirements.     

Extracting gene function from protein-protein interactions using Quantitative BAC InteraCtomics (QUBIC)

Large-scale proteomic screens are increasingly employed for placing genes into specific pathways. Therefore generic methods providing a physiological context for protein-protein interaction studies are of great interest. In recent years many protein-protein interactions have been determined by affinity purification followed by mass spectrometry (AP-MS). Among many different AP-MS approaches, the recently developed Quantitative BAC InteraCtomics (QUBIC) approach is particularly attractive as it uses tagged, full-length baits that are expressed under endogenous control. For QUBIC large cell line collections expressing tagged proteins from BAC transgenes or gene trap loci have been developed and are freely available. Here we describe detailed workflows on how to obtain specific protein binding partners with high confidence under physiological conditions. The methods are based on fast, streamlined and generic purification procedures followed by single run liquid chromatography-mass spectrometric analysis. Quantification is achieved either by the stable isotope labeling of amino acids in cell culture (SILAC) method or by a 'label-free' procedure. In either case data analysis is performed by using the freely available MaxQuant environment. The QUBIC approach enables biologists with access to high resolution mass spectrometry to perform small and large-scale protein interactome mappings.     

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.     

Determining nucleolar association from sequence by leveraging protein-protein interactions.

Controlled intra-nuclear organization of proteins is critical for sustaining correct function of the cell. Proteins and RNA are transported by passive diffusion and associate with compartments by virtue of diverse molecular interactions--presenting a challenging problem for data-driven model building. An increasing inventory of proteins with known intra-nuclear destination and proliferation of molecular interaction data motivate an integrative method, leveraging the existing evidence to build accurate models of intranuclear trafficking. Kernel canonical correlation analysis (KCCA) enables the construction of predictors based on genomic sequence data, but leverages other knowledge sources during training. The approach specifically involves the induction of protein sequence features and relations most pertinent to the recovery of nucleolar associated protein-protein interactions. With success rates of about 78%, the classification of nucleolar association from KCCA-induced features surpasses that of baseline approaches. We observe that the coalescence of protein-protein interaction data with sequence data enhances the prediction of highly interconnected, key ribosomal and RNA-related nucleolar proteins. For supplementary material, see www.itee.uq.edu.au/~ pprowler/nucleoli.     

Robust co-regulation of tyrosine phosphorylation sites on proteins reveals novel protein interactions

Cell signaling networks propagate information from extracellular cues via dynamic modulation of protein-protein interactions in a context-dependent manner. Networks based on receptor tyrosine kinases (RTKs), for example, phosphorylate intracellular proteins in response to extracellular ligands, resulting in dynamic protein-protein interactions that drive phenotypic changes. Most commonly used methods for discovering these protein-protein interactions, however, are optimized for detecting stable, longer-lived complexes, rather than the type of transient interactions that are essential components of dynamic signaling networks such as those mediated by RTKs. Substrate phosphorylation downstream of RTK activation modifies substrate activity and induces phospho-specific binding interactions, resulting in the formation of large transient macromolecular signaling complexes. Since protein complex formation should follow the trajectory of events that drive it, we reasoned that mining phosphoproteomic datasets for highly similar dynamic behavior of measured phosphorylation sites on different proteins could be used to predict novel, transient protein-protein interactions that had not been previously identified. We applied this method to explore signaling events downstream of EGFR stimulation. Our computational analysis of robustly co-regulated phosphorylation sites, based on multiple clustering analysis of quantitative time-resolved mass-spectrometry phosphoproteomic data, not only identified known sitewise-specific recruitment of proteins to EGFR, but also predicted novel, a priori interactions. A particularly intriguing prediction of EGFR interaction with the cytoskeleton-associated protein PDLIM1 was verified within cells using co-immunoprecipitation and in situ proximity ligation assays. Our approach thus offers a new way to discover protein-protein interactions in a dynamic context- and phosphorylation site-specific manner.     

Computational design of a new hydrogen bond network and at least a 300-fold specificity switch at a protein-protein interface

The redesign of protein-protein interactions is a stringent test of our understanding of molecular recognition and specificity. Previously we engineered a modest specificity switch into the colicin E7 DNase-Im7 immunity protein complex by identifying mutations that are disruptive in the native complex, but can be compensated by mutations on the interacting partner. Here we extend the approach by systematically sampling alternate rigid body orientations to optimize the interactions in a binding mode specific manner. Using this protocol we designed a de novo hydrogen bond network at the DNase-immunity protein interface and confirmed the design with X-ray crystallographic analysis. Subsequent design of the second shell of interactions guided by insights from the crystal structure on tightly bound water molecules, conformational strain, and packing defects yielded new binding partners that exhibited specificities of at least 300-fold between the cognate and the non-cognate complexes. This multi-step approach should be applicable to the design of polar protein-protein interactions and contribute to the re-engineering of regulatory networks mediated by protein-protein interactions.     

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     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.     

A novel mammalian display system for the selection of protein-protein interactions by decoy receptor engagement

The emerging field of proteomics has created a need for new high-throughput methodologies for the analysis of gene products. An attractive approach is to develop systems that allow for clonal selection of interacting protein pairs from large molecular libraries. In this study, we have characterized a novel approach for identification and selection of protein-protein interactions, denoted SPIRE (selection of protein interactions by receptor engagement), which is based on a mammalian expression system. We have demonstrated proof of concept by creating a general plasma membrane bound decoy receptor, by displaying a protein or a peptide genetically fused to a trunctated version of the CD40 molecule. When this decoy receptor is engaged by a ligand to the displayed protein/peptide, the receptor expressing cell is rescued from apoptosis. To design a high-throughput system with a highly parallel capacity, we utilized the B cell line WEHI-231, as carrier of the decoy receptor. One specific peptide-displaying cell could be identified and amplified, based on a specific receptor engagement, in a background of 12 500 wild-type cells after four selections. This demonstrates that the approach may serve as a tool in post-genomic research for identifying protein-protein interactions, without prior knowledge of either component.     

Improved two-dimensional reversed phase-reversed phase LC-MS/MS approach for identification of peptide-protein interactions

Quantitative mass spectrometry (MS) in combination with affinity purification approaches allows for an unbiased study of protein-protein and peptide-protein interactions. In shotgun approaches that are based on proteolytic digestion of complex protein mixtures followed by two-dimensional liquid-phase chromatography, the separation effort prior to MS analysis is focused on tryptic peptides. Here we developed an improved offline 2-D liquid chromatography-MS/MS approach for the identification and quantification of binding proteins utilizing reversed-phase capillary columns with acidic acetonitrile-containing eluents in both chromatographic dimensions. A specific fractionation scheme was applied in order to obtain samples with evenly distributed peptides and to fully utilize the separation space in the second dimension nanoLC-MS/MS. We report peptide-protein interaction studies to identify phosphorylation-dependent binding partners of the T cell adapter protein ADAP. The results of the SILAC-based pull-down experiments show this approach is well suited for distinguishing phosphorylation-specific interactions from unspecific binding events. The data provide further evidence that phosphorylated Tyr 595 of ADAP may serve as a direct binding site for the SH2 domains of the T cell proteins SLP76 and NCK. From a technical point of view we provide a detailed protocol for an offline 2-D RP-RP LC-MS/MS method that offers a robust and time-saving alternative for quantitative interactome analysis.     

Unraveling protein-protein interactions in living cells with fluorescence fluctuation brightness analysis

Fluorescence correlation spectroscopy is a potentially powerful tool for measuring protein-protein interactions directly in single living cells. We previously reported on the detection of homodimer formation in cells using molecular brightness analysis. Here, we extend the technique to detect binding between different proteins. Proteins are labeled with the fluorescent markers YFP and CFP. We first determine the coexpression ratio of both proteins by measuring the intensity ratio with a dual-color setup. The effect of fluorescence resonance energy transfer on the intensity ratio is explicitly taken into account. The brightness of cells coexpressing both proteins is measured in a single-color setup. Selecting the laser wavelength of the two-photon light source allows us to either coexcite both proteins or to selectively excite YFP-labeled proteins. This approach enables us to distinguish between homodimer and heterodimer formation. We first present the theory and then demonstrate experimental feasibility using the ligand binding domains of retinoic acid receptor (RARLBD) and of retinoid X receptor (RXRLBD). Both proteins form heterodimers, and RXRLBD also forms homodimers in the presence of its agonist. We explore binding between these proteins in the presence and absence of RXR agonist. Our results demonstrate that brightness analysis offers a quantitative method for determining protein interactions in cells.     

Strategies to search and design stabilizers of protein-protein interactions: a feasibility study

Since protein-protein interactions play a pivotal role in the communication on the molecular level in virtually every biological system and process, the search and design for modulators of such interactions is of utmost importance. In recent years many inhibitors for specific protein-protein interactions have been developed, however, in only a few cases, small and druglike molecules are able to interfere in the complex formation of proteins. On the other hand, there are several small molecules known to modulate protein-protein interactions by means of stabilizing an already assembled complex. To achieve this goal, a ligand is binding to a pocket, which is located rim-exposed at the interface of the interacting proteins, for example as the phytotoxin Fusicoccin, which stabilizes the interaction of plant H+-ATPase and 14-3-3 protein by nearly a factor of 100. To suggest alternative leads, we performed a virtual screening campaign to discover new molecules putatively stabilizing this complex. Furthermore, we screen a dataset of 198 transient recognition protein-protein complexes for cavities, which are located rim-exposed at their interfaces. We provide evidence for high similarity between such rim-exposed cavities and usual ligands accommodating active sites of enzymes. This analysis suggests that rim-exposed cavities at protein-protein interfaces are druggable binding sites. Therefore, the principle of stabilizing protein-protein interactions seems to be a promising alternative to the approach of the competitive inhibition of such interactions by small molecules.     

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.