A surface display yeast two-hybrid screening system for high-throughput protein interactome mapping

Despite the wide acceptance of yeast two-hybrid (Y2H) system for protein-protein interaction analysis and discovery, conventional Y2H assays are not well suited for high-throughput screening of the protein interaction network (“interactome”) on a genomic scale due to several limitations, including labor-intensive agar plating and colony selection methods associated with the use of nutrient selection markers, complicated reporter analysis methods associated with the use of LacZ enzyme reporters, and incompatibility of the liquid handling robots. We recently reported a robust liquid culture Y2H system based on quantitative analysis of yeast-enhanced green fluorescent protein (yEGFP) reporters that greatly increased the analysis throughput and compatibility with liquid handling robots. To further advance its utility in high-throughput complementary DNA (cDNA) library screening, we report the development of a novel surface display Y2H (sdY2H) library screening system with uniquely integrated surface display hemagglutination (sdHA) antigen and yEGFP reporters. By introduction of a surface reporter sdHA into the yEGFP-based Y2H system, positive Y2H targets are quickly isolated from library cells by a simple magnetic separation without a large plating effort. Moreover, the simultaneous scoring of multiple reporters, including sdHA, yEGFP, and conventional nutrient markers, greatly increased the specificity of the Y2H assay. The feasibility of the sdY2H assay on large cDNA library screening was demonstrated by the successful recovery of positive P53/T interaction pairs at a target-to-background ratio of 1:1,000,000. Together with the massive parallel DNA sequencing technology, it may provide a powerful proteomic tool for high-throughput interactome mapping on a genomic scale.     

Increasing confidence of protein interactomes using network topological metrics

MOTIVATION: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches. RESULTS: We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity. AVAILABILITY: The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.     

Screening for ligands using a generic and high-throughput light-scattering-based assay

Rapid identification of small molecules that interact with protein targets using a generic screening method greatly facilitates the development of therapeutic agents. The authors describe a novel method for performing homogeneous biophysical assays in a high-throughput format. The use of light scattering as a method to evaluate protein stability during thermal denaturation in a 384-well format yields a robust assay with a low frequency of false positives. This novel method leads to the identification of interacting small molecules without the addition of extraneous fluorescent probes. The analysis and interpretation of data is rapid, with sensitivity for protein stability comparable to differential scanning calorimetry. The authors propose potential uses in drug discovery, structural genomics, and functional genomics as a method to evaluate small-molecule interactions, identify natural cofactors that stabilize target proteins, and identify natural substrates and products for previously uncharacterized protein targets.     

Aptamers as reagents for high-throughput screening

The identification of new drug candidates from chemical libraries is a major component of discovery research in many pharmaceutical companies. Given the large size of many conventional and combinatorial libraries and the rapid increase in the number of possible therapeutic targets, the speed with which efficient high-throughput screening (HTS) assays can be developed can be a rate-limiting step in the discovery process. We show here that aptamers, nucleic acids that bind other molecules with high affinity, can be used as versatile reagents in competition binding HTS assays to identify and optimize small-molecule ligands to protein targets. To illustrate this application, we have used labeled aptamers to platelet-derived growth factor B-chain and wheat germ agglutinin to screen two sets of potential small-molecule ligands. In both cases, binding affinities of all ligands tested (small molecules and aptamers) were strongly correlated with their inhibitory potencies in functional assays. The major advantages of using aptamers in HTS assays are speed of aptamer identification, high affinity of aptamers for protein targets, relatively large aptamer-protein interaction surfaces, and compatibility with various labeling/detection strategies. Aptamers may be particularly useful in HTS assays with protein targets that have no known binding partners such as orphan receptors. Since aptamers that bind to proteins are often specific and potent antagonists of protein function, the use of aptamers for target validation can be coupled with their subsequent use in HTS.     

Predicting co-complexed protein pairs using genomic and proteomic data integration

Background  

Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.     

A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, “Clone-seq”, leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.     

Barcode fusion genetics-protein-fragment complementation assay (BFG-PCA): tools and resources that expand the potential for binary protein interaction discovery

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.     

Increasing specificity in high-throughput yeast two-hybrid experiments

Since its inception, the yeast two-hybrid (Y2H) system has proven to be an efficient system to identify novel protein-protein interactions. However, Y2H screens are sometimes criticized for generating high rates of false-positives. Minimizing false-positive interactions is especially important in proteome wide high-throughput (HT) Y2H. Here, we summarize various approaches that reduce false-positives in HT-Y2H projects. We evaluated the potential of examining putative positives after removing the prey encoding plasmid by negative selection. We found that this method reliably identifies false-positives caused by spontaneous conversion of baits into auto-activators and provides significant time-savings in HT screens. In addition, we present a method to eliminate an important source of false-positives: contaminating prey plasmids. Y2H interactors can be wrongly identified due to the presence of two or more different plasmids in the cells of a single yeast colony. Of these independent plasmids, only one encodes a genuine interactor. Contaminating plasmids are eliminated by extended culture of yeast cells under positive selection for the interaction, allowing the identification of the true interaction partner.     

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.     

Generation and Characterization of Yeast Two-Hybrid cDNA Libraries Derived From Two Distinct Mouse Pluripotent Cell Types

Pluripotent stem cells have the therapeutic potential in future regenerative medicine applications. Therefore, it is highly important to understand the molecular mechanisms governing the pluripotency and differentiation potential of these cells. Our current knowledge of pluripotent cells is largely limited owing to the candidate gene/protein approach rather than studying the complex interactions of the proteins. Experimentally, yeast two-hybrid system (Y2H) is by far the most useful and widely used method to detect the protein–protein interactions in high-throughput screenings. Unfortunately, currently there is no GAL4-based pluripotent stem cell-specific cDNA library available for screening the interaction proteins impeding the large-scale studies. In this study, we report the construction of Y2H cDNA libraries derived from mouse pluripotent embryonic stem cells (ESCs) and multipotent adult germ-line stem cells (maGSCs) in GAL4-based Y2H vector system with very high transformation efficiency. Furthermore, we have constructed two different baits and screened for interaction partners in an effort to characterize the libraries and also as a part of our ongoing studies. Consequently, many putative interaction proteins were identified in both cases and their interaction was further validated by direct-Y2H. The observed interactions between bait proteins and their respective analyzed putative interaction proteins were further confirmed using two independent approaches in mammalian cells, thus highlighting the biological significance of the identified interactor (s). Finally, we would like to make these cDNA libraries as a resource that can be distributed to the research community.     

Local modeling of global interactome networks

MOTIVATION: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function. RESULTS: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein-protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data. AVAILABILITY: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org CONTACT: dscholtens@northwestern.edu SUPPLEMENTARY INFORMATION: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling     

A Novel Scoring Approach for Protein Co-Purification Data Reveals High Interaction Specificity

Large-scale protein interaction networks (PINs) have typically been discerned using affinity purification followed by mass spectrometry (AP/MS) and yeast two-hybrid (Y2H) techniques. It is generally recognized that Y2H screens detect direct binary interactions while the AP/MS method captures co-complex associations; however, the latter technique is known to yield prevalent false positives arising from a number of effects, including abundance. We describe a novel approach to compute the propensity for two proteins to co-purify in an AP/MS data set, thereby allowing us to assess the detected level of interaction specificity by analyzing the corresponding distribution of interaction scores. We find that two recent AP/MS data sets of yeast contain enrichments of specific, or high-scoring, associations as compared to commensurate random profiles, and that curated, direct physical interactions in two prominent data bases have consistently high scores. Our scored interaction data sets are generally more comprehensive than those of previous studies when compared against four diverse, high-quality reference sets. Furthermore, we find that our scored data sets are more enriched with curated, direct physical associations than Y2H sets. A high-confidence protein interaction network (PIN) derived from the AP/MS data is revealed to be highly modular, and we show that this topology is not the result of misrepresenting indirect associations as direct interactions. In fact, we propose that the modularity in Y2H data sets may be underrepresented, as they contain indirect associations that are significantly enriched with false negatives. The AP/MS PIN is also found to contain significant assortative mixing; however, in line with a previous study we confirm that Y2H interaction data show weak disassortativeness, thus revealing more clearly the distinctive natures of the interaction detection methods. We expect that our scored yeast data sets are ideal for further biological discovery and that our scoring system will prove useful for other AP/MS data sets.     

Zeptosens' protein microarrays: a novel high performance microarray platform for low abundance protein analysis

Protein microarrays are considered an enabling technology, which will significantly expand the scope of current protein expression and protein interaction analysis. Current technologies, such as two-dimensional gel electrophoresis (2-DE) in combination with mass spectrometry, allowing the identification of biologically relevant proteins, have a high resolving power, but also considerable limitations. As was demonstrated by Gygi et al. (Proc. Nat. Acad. Sci. USA 2000,97, 9390-9395), most spots in 2-DE, observed from whole cell extracts, are from high abundance proteins, whereas low abundance proteins, such as signaling molecules or kinases, are only poorly represented. Protein microarrays are expected to significantly expedite the discovery of new markers and targets of pharmaceutical interest, and to have the potential for high-throughput applications. Key factors to reach this goal are: high read-out sensitivity for quantification also of low abundance proteins, functional analysis of proteins, short assay analysis times, ease of handling and the ability to integrate a variety of different targets and new assays. Zeptosens has developed a revolutionary new bioanalytical system based on the proprietary planar waveguide technology which allows us to perform multiplexed, quantitative biomolecular interaction analysis with highest sensitivity in a microarray format upon utilizing the specific advantages of the evanescent field fluorescence detection. The analytical system, comprising an ultrasensitive fluorescence reader and microarray chips with integrated microfluidics, enables the user to generate a multitude of high fidelity data in applications such as protein expression profiling or investigating protein-protein interactions. In this paper, the important factors for developing high performance protein microarray systems, especially for targeting low abundant messengers of relevant biological information, will be discussed and the performance of the system will be demonstrated in experimental examples.     

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.     

A Flow Cytometry-Based FRET Assay to Identify and Analyse Protein-Protein Interactions in Living Cells

Background

Försters resonance energy transfer (FRET) microscopy is widely used for the analysis of protein interactions in intact cells. However, FRET microscopy is technically challenging and does not allow assessing interactions in large cell numbers. To overcome these limitations we developed a flow cytometry-based FRET assay and analysed interactions of human and simian immunodeficiency virus (HIV and SIV) Nef and Vpu proteins with cellular factors, as well as HIV Rev multimer-formation.

Results

Amongst others, we characterize the interaction of Vpu with CD317 (also termed Bst-2 or tetherin), a host restriction factor that inhibits HIV release from infected cells and demonstrate that the direct binding of both is mediated by the Vpu membrane-spanning region. Furthermore, we adapted our assay to allow the identification of novel protein interaction partners in a high-throughput format.

Conclusion

The presented combination of FRET and FACS offers the precious possibility to discover and define protein interactions in living cells and is expected to contribute to the identification of novel therapeutic targets for treatment of human diseases.