A Yeast 2-Hybrid Screen in Batch to Compare Protein Interactions.

Screening for protein-protein interactions using the yeast 2-hybrid assay has long been an effective tool, but its use has largely been limited to the discovery of high-affinity interactors that are highly enriched in the library of interacting candidates. In a traditional format, the yeast 2-hybrid assay can yield too many colonies to analyze when conducted at low stringency where low affinity interactors might be found. Moreover, without a comprehensive and complete interrogation of the same library against different bait plasmids, a comparative analysis cannot be achieved. Although some of these problems can be addressed using arrayed prey libraries, the cost and infrastructure required to operate such screens can be prohibitive. As an alternative, we have adapted the yeast 2-hybrid assay to simultaneously uncover dozens of transient and static protein interactions within a single screen utilizing a strategy termed DEEPN (Dynamic Enrichment for Evaluation of Protein Networks), which incorporates high-throughput DNA sequencing and computation to follow the evolution of a population of plasmids that encode interacting partners. Here, we describe customized reagents and protocols that allow a DEEPN screen to be executed easily and cost-effectively.

Identification of putative regulatory region of insulin-like androgenic gland hormone gene (IAG) in the prawn Macrobrachium nipponense and proteins that interact with IAG by using yeast two-hybrid system.

Insulin-like androgenic gland hormone gene (IAG) is a sex regulator specifically expressed in male crustaceans, controlling the male sexual differentiation, spermatogenesis and reproductive strategy. Our previous study reported the cloning and characterization of the prawn Macrobrachium nipponense IAG (MnIAG). In this study, we further identified a 2214-bp MnIAG 5'-flanking region, and analyzed its transcription factor binding sites and transcriptional activity. The results showed that there were two potential promoter core sequences, three TATA boxes and one CAAT box existing in the MnIAG 5'-flanking region as well as many potential transcription factor binding sites, such as SRY, Sox-5, GATA-1, etc. Notably, the transcriptional activity was weak in this region, and a negative regulatory region was found in -604 to -231bp. In addition, we constructed M. nipponense yeast libraries and identified proteins interacting with the MnIAG protein by yeast two hybridization assay. The yeast two-hybrid screening yielded ten positive clones, of which five were annotated by NCBI database, namely heat shock protein 21, NADH dehydrogenase, zinc finger protein, beta-N-acetylglucosaminidase and a hypothetical protein. The identification of MnIAG putative regulatory region and proteins that interact with IAG will facilitate our understanding of the regulatory role of MnIAG and provide a foundation for deep insight into the prawn sex differentiation mechanism and signaling transduction pathways.

From the static interactome to dynamic protein complexes: Three challenges.

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein-protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

Analysis of membrane protein complexes using the split-ubiquitin membrane yeast two-hybrid (MYTH) system.

Recent research has begun to elucidate the global network of cytosolic and membrane protein interactions. The resulting interactome map facilitates numerous biological studies, including those for cell signalling, protein trafficking and protein regulation. Due to the hydrophobic nature of membrane proteins such as tyrosine kinases, G-protein coupled receptors, membrane bound phosphatases and transporters it is notoriously difficult to study their relationship to signaling molecules, the cytoskeleton, or any other interacting partners. Although conventional yeast-two hybrid is a simple and robust technique that is effective in the identification of specific protein-protein interactions, it is limited in its use for membrane proteins. However, the split-ubiquitin membrane based yeast two-hybrid assay (MYTH) has been described as a tool that allows for the identification of membrane protein interactions. In the MYTH system, ubiquitin has been split into two halves, each of which is fused to a protein, at least one of which is membrane bound. Upon interaction of these two proteins, the two halves of ubiquitin are reconstituted and a transcription factor that is fused to the membrane protein is released. The transcription factor then enters the nucleus and activates transcription of reporter genes. Currently, large-scale MYTH screens using cDNA or gDNA libraries are performed to identify and map the binding partners of various membrane proteins. Thus, the MYTH system is proving to be a powerful tool for the elucidation of specific protein-protein interactions, contributing greatly to the mapping of the membrane protein interactome.

Efficient and robust prediction algorithms for protein complexes using Gomory-Hu trees.

Two-Hybrid (Y2H) Protein-Protein interaction (PPI) data suffer from high False Positive and False Negative rates, thus making searching for protein complexes in PPI networks a challenge. To overcome these limitations, we propose an efficient approach which measures connectivity between proteins not by edges, but by edge-disjoint paths. We model the number of edge-disjoint paths as a network flow and efficiently represent it in a Gomory-Hu tree. By manipulating the tree, we are able to isolate groups of nodes sharing more edge-disjoint paths with each other than with the rest of the network, which are our putative protein complexes. We examine the performance of our algorithm with Variation of Information and Separation measures and show that it belongs to a group of techniques which are robust against increased false positive and false negative rates. We apply our approach to yeast , mouse, worm, and human Y2H PPI networks, where it shows promising results. On yeast network, we identify 38 statistically significant protein clusters, 20 of which correspond to protein complexes and 16 to functional modules.

Probabilistic modeling of systematic errors in two-hybrid experiments.

UNLABELLED: We describe a novel probabilistic approach to estimating errors in two-hybrid (2H) experiments. Such experiments are frequently used to elucidate protein-protein interaction networks in a high-throughput fashion; however, a significant challenge with these is their relatively high error rate, specifically, a high false-positive rate. We describe a comprehensive error model for 2H data, accounting for both random and systematic errors. The latter arise from limitations of the 2H experimental protocol: in theory, the reporting mechanism of a 2H experiment should be activated if and only if the two proteins being tested truly interact; in practice, even in the absence of a true interaction, it may be activated by some proteins - either by themselves or through promiscuous interaction with other proteins. We describe a probabilistic relational model that explicitly models the above phenomenon and use Markov Chain Monte Carlo (MCMC) algorithms to compute both the probability of an observed 2H interaction being true as well as the probability of individual proteins being self-activating/promiscuous. This is the first approach that explicitly models systematic errors in protein-protein interaction data; in contrast, previous work on this topic has modeled errors as being independent and random. By explicitly modeling the sources of noise in 2H systems, we find that we are better able to make use of the available experimental data. In comparison with Bader et al.'s method for estimating confidence in 2H predicted interactions, the proposed method performed 5-10% better overall, and in particular regimes improved prediction accuracy by as much as 76%. SUPPLEMENTARY INFORMATION: http://theory.csail.mit.edu/probmod2H

RNA-protein interactions in the yeast three-hybrid system: affinity, sensitivity, and enhanced library screening.

The yeast three-hybrid system has become a useful tool in analyzing RNA-protein interactions. An RNA sequence is tested in combination with an RNA-binding protein linked to a transcription activation domain (AD). A productive RNA-protein interaction activates a reporter gene in vivo. The system has been used to test candidate RNA-protein pairs, to isolate mutations in each interacting partner, and to identify proteins that bind a given RNA sequence. However, the relationship between reporter gene activation and in vitro affinity of an RNA-protein interaction has not been examined systematically. This limits interpretation of the data and complicates the development of new strategies. Here, we analyze several key parameters of the three-hybrid system, using as a model the interaction of a PUF protein, FBF-1, with a range of RNA targets. We compare activation of two reporter genes as a function of the in vitro affinity of the interaction. HIS3 and LacZ expression levels are directly related to affinity over a 10-fold range of Kd. Expression of the reporter genes also is directly related to the abundance of the activation domain fusion protein. We describe a new yeast strain, YBZ1, that simplifies screening of cDNA/AD libraries. This strain possesses a tandem, head-to-tail dimer of a high-affinity variant of MS2 coat protein, fused to a monomer of the LexA DNA-binding protein. We show that the use of this strain in cDNA library screens increases the number of genuine, sequence-specific positives detected, and at the same time reduces the background of false, RNA-independent positives.

Discovering protein-protein interactions.

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein-protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein-protein interactions, covering both the conventional experimental methods and new computational approaches.

VisANT: an online visualization and analysis tool for biological interaction data.

BACKGROUND: New techniques for determining relationships between biomolecules of all types--genes, proteins, noncoding DNA, metabolites and small molecules--are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems. RESULTS: We present VisANT, an application for integrating biomolecular interaction data into a cohesive, graphical interface. This software features a multi-tiered architecture for data flexibility, separating back-end modules for data retrieval from a front-end visualization and analysis package. VisANT is a freely available, open-source tool for researchers, and offers an online interface for a large range of published data sets on biomolecular interactions, including those entered by users. This system is integrated with standard databases for organized annotation, including GenBank, KEGG and SwissProt. VisANT is a Java-based, platform-independent tool suitable for a wide range of biological applications, including studies of pathways, gene regulation and systems biology. CONCLUSION: VisANT has been developed to provide interactive visual mining of biological interaction data sets. The new software provides a general tool for mining and visualizing such data in the context of sequence, pathway, structure, and associated annotations. Interaction and predicted association data can be combined, overlaid, manipulated and analyzed using a variety of built-in functions. VisANT is available at http://visant.bu.edu.

Is there a bias in proteome research?

Advances in technology have enabled us to take a fresh look at data acquired by traditional single experiments and to compare them with genomewide data. The differences can be tremendous, as we show here, in the field of proteomics. We have compared data sets of protein-protein interactions in Saccharomyces cerevisiae that were detected by an identical underlying technical method, the yeast two-hybrid system. We found that the individually identified protein-protein interactions are considerably different from those identified by two genomewide scans. Interacting proteins in the pooled database from single publications are much more closely related to each other with respect to transcription profiles when compared to genomewide data. This difference may have been introduced by two factors: by a selection process in individual publications and by false positives in the whole-genome scans. If we assume that the differences are a result of false positives in the whole-genome data, the scans would contain 47%, 44%, and 91% of false positives for the UETZ, ITO-core, and ITO-full data, respectively. If, however, the true fraction of false positives is considerably lower than estimated here, the data from hypothesis-driven experiments must have been subjected to a serious selection process.

Interaction between emerin and nuclear lamins.

Emerin is an inner nuclear membrane protein that is involved in X-linked recessive Emery-Dreifuss muscular dystrophy (X-EDMD). Although the function of this protein is still unknown, we revealed that C-terminus transmembrane domain-truncated emerin (amino acid 1-225) binds to lamin A with higher affinity than lamin C. Screening for the emerin binding protein and immunoprecipitation analysis showed that lamin A binds to emerin specifically. We also used the yeast two-hybrid system to clarify that this interaction requires the top half of the tail domain (amino acid 384-566) of lamin A. Lamin A and lamin C are alternative splicing products of the lamin A/C gene that is responsible for autosomal dominant Emery-Dreifuss muscular dystrophy (AD-EDMD). These results indicate that the emerin-lamin interaction requires the tail domains of lamin A and lamin C. The data also suggest that the lamin A-specific region (amino acids 567-664) plays some indirect role in the difference in emerin-binding capacity between lamin A and lamin C. This is the first report that refers the difference between lamin A and lamin C in the interaction with emerin. These data also suggest that lamin A is important for nuclear membrane integrity.

Approaches to detecting false positives in yeast two-hybrid systems.

While many novel associations predicted by two-hybrid library screens reflect actual biological associations of two proteins in vivo, at times the functional co-relevance of two proteins scored as interacting in the two-hybrid system is unlikely. The reason for this positive score remains obscure, which leads to designating such clones as false positives. After investigating the effect of over-expressing a series of putative false positives in yeast, we determined that expression of some of these clones induces an array of biological effects in yeast, including altered growth rate and cell permeability, that bias perceived activity of LacZ reporters. Based on these observations, we identify four simple strategies that can assist in determining whether a protein is likely to have been selected in a two-hybrid screen because of indirect metabolic effects.