Mammalian two-hybrids come of age

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Characterization of allosteric modulators that disrupt androgen receptor co-activator protein-protein interactions to alter transactivation-Drug leads for metastatic castration resistant prostate cancer

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4-2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H3-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC50 of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1-1 and S2-6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3-11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Combinatorial Approaches for Efficient Design of Photoswitchable Protein-Protein Interactions as In Vivo Actuators

Light switchable two-component protein dimerization systems offer versatile manipulation and dissection of cellular events in living systems. Over the past 20 years, the field has been driven by the discovery of photoreceptor-based interaction systems, the engineering of light-actuatable binder proteins, and the development of photoactivatable compounds as dimerization inducers. This perspective is to categorize mechanisms and design approaches of these dimerization systems, compare their advantages and limitations, and bridge them to emerging applications. Our goal is to identify new opportunities in combinatorial protein design that can address current engineering challenges and expand in vivo applications.

Yeast Two-Hybrid System for Mapping Novel Dengue Protein Interactions

Yeast two-hybrid (Y2H) systems are one of the principal choices for identifying novel binary protein-protein interactions (PPIs). Since its development, it has contributed for the discovery of several PPIs between pathogens and host, allowing not only a comprehensive look at the disease pathogenesis but also for therapeutic strategies. Identification of viral-host PPIs that impact on viral replication and pathogenesis can lead to new advances in antiviral therapies such as the development of drug candidates and vaccine design. In this chapter, we revise the Y2H key parameters necessary for screening PPIs and discuss the possible approaches for using this technique to identify novel dengue-host protein interactions.

Creating Red Light-Switchable Protein Dimerization Systems as Genetically Encoded Actuators with High Specificity

Protein dimerization systems controlled by red light with increased tissue penetration depth are a highly needed tool for clinical applications such as cell and gene therapies. However, mammalian applications of existing red light-induced dimerization systems are hampered by limitations of their two components: a photosensory protein (or photoreceptor) which often requires a mammalian exogenous chromophore and a naturally occurring photoreceptor binding protein typically having a complex structure and nonideal binding properties. Here, we introduce an efficient, generalizable method (COMBINES-LID) for creating highly specific, reversible light-induced heterodimerization systems independent of any existing binders to a photoreceptor. It involves a two-step binder screen (phage display and yeast two-hybrid) of a combinatorial nanobody library to obtain binders that selectively engage a light-activated form of a photoswitchable protein or domain not the dark form. Proof-of-principle was provided by engineering nanobody-based, red light-induced dimerization (nanoReD) systems comprising a truncated bacterial phytochrome sensory module using a mammalian endogenous chromophore, biliverdin, and light-form specific nanobodies. Selected nanoReD systems were biochemically characterized, exhibiting low dark activity and high induction specificity, and further demonstrated for the reversible control of protein translocation and activation of gene expression in mice. Overall, COMBINES-LID opens new opportunities for creating genetically encoded actuators for the optical manipulation of biological processes.

A Novel FRET Approach Quantifies the Interaction Strength of Peroxisomal Targeting Signals and Their Receptor in Living Cells

Measuring Förster-resonance-energy-transfer (FRET) efficiency allows the investigation of protein-protein interactions (PPI), but extracting quantitative measures of affinity necessitates highly advanced technical equipment or isolated proteins. We demonstrate the validity of a recently suggested novel approach to quantitatively analyze FRET-based experiments in living mammalian cells using standard equipment using the interaction between different type-1 peroxisomal targeting signals (PTS1) and their soluble receptor peroxin 5 (PEX5) as a model system. Large data sets were obtained by flow cytometry coupled FRET measurements of cells expressing PTS1-tagged EGFP together with mCherry fused to the PTS1-binding domain of PEX5, and were subjected to a fitting algorithm extracting a quantitative measure of the interaction strength. This measure correlates with results obtained by in vitro techniques and a two-hybrid assay, but is unaffected by the distance between the fluorophores. Moreover, we introduce a live cell competition assay based on this approach, capable of depicting dose- and affinity-dependent modulation of the PPI. Using this system, we demonstrate the relevance of a sequence element next to the core tripeptide in PTS1 motifs for the interaction strength between PTS1 and PEX5, which is supported by a structure-based computational prediction of the binding energy indicating a direct involvement of this sequence in the interaction.

Assays to Interrogate the Ability of Compounds to Inhibit the AF-2 or AF-1 Transactivation Domains of the Androgen Receptor

Prostate cancer is the leading cause of cancer and second leading cause of cancer-related death in men in the United States. Twenty percent of patients receiving the standard of care androgen deprivation therapy (ADT) eventually progress to metastatic and incurable castration-resistant prostate cancer (CRPC). Current FDA-approved drugs for CRPC target androgen receptor (AR) binding or androgen production, but only provide a 2- to 5-month survival benefit due to the emergence of resistance. Overexpression of AR coactivators and the emergence of AR splice variants, both promote continued transcriptional activation under androgen-depleted conditions and represent drug resistance mechanisms that contribute to CRPC progression. The AR contains two transactivation domains, activation function 2 (AF-2) and activation function 1 (AF-1), which serve as binding surfaces for coactivators involved in the transcriptional activation of AR target genes. Full-length AR contains both AF-2 and AF-1 surfaces, whereas AR splice variants only have an AF-1 surface. We have recently prosecuted a high-content screening campaign to identify hit compounds that can inhibit or disrupt the protein-protein interactions (PPIs) between AR and transcriptional intermediary factor 2 (TIF2), one of the coactivators implicated in CRPC disease progression. Since an ideal inhibitor/disruptor of AR-coactivator PPIs would target both the AF-2 and AF-1 surfaces, we describe here the development and validation of five AF-2- and three AF-1-focused assays to interrogate and prioritize hits that disrupt both transactivation surfaces. The assays were validated using a test set of seven known AR modulator compounds, including three AR antagonists and one androgen synthesis inhibitor that are FDA-approved ADTs, two investigational molecules that target the N-terminal domain of AR, and an inhibitor of the Hsp90 (heat shock protein) molecular chaperone.

Development and Applications of Superfolder and Split Fluorescent Protein Detection Systems in Biology

Molecular engineering of the green fluorescent protein (GFP) into a robust and stable variant named Superfolder GFP (sfGFP) has revolutionized the field of biosensor development and the use of fluorescent markers in diverse area of biology. sfGFP-based self-associating bipartite split-FP systems have been widely exploited to monitor soluble expression in vitro, localization, and trafficking of proteins in cellulo. A more recent class of split-FP variants, named « tripartite » split-FP, that rely on the self-assembly of three GFP fragments, is particularly well suited for the detection of protein–protein interactions. In this review, we describe the different steps and evolutions that have led to the diversification of superfolder and split-FP reporter systems, and we report an update of their applications in various areas of biology, from structural biology to cell biology.

A High-Throughput Candida albicans Two-Hybrid System

Candida albicans is a human fungal pathogen that does not follow the universal codon usage, as it translates the CUG codon into serine rather than leucine. This makes it difficult to study protein-protein interactions using the standard yeast two-hybrid (Y2H) system in the model organism Saccharomyces cerevisiae Due to the lack of adapted tools, only a small number of protein-protein interactions (PPIs) have been detected or studied using C. albicans-optimized tools despite the importance of PPIs to understand cell biology. However, with the sequencing of the whole genome of C. albicans, the availability of an ORFeome collection containing 5,099 open reading frames (ORFs) in Gateway-adapted donor vectors, and the creation of a Gateway-compatible C. albicans-specific two-hybrid (C2H) system, it became possible to study protein-protein interactions on a larger scale using C. albicans itself as the model organism. Erroneous translations are hereby eliminated compared to using the S. cerevisiae Y2H system. Here, we describe the technical adaptations and the first application of the C2H system for a high-throughput screen, thus making it possible to screen thousands of PPIs at once in C. albicans itself. This first, small-scale high-throughput screen, using Pho85 as a bait protein against 1,646 random prey proteins, yielded one interacting partner (Pcl5). The interaction found with the high-throughput setup was further confirmed with a low-throughput C2H experiment and with a coimmunoprecipitation (co-IP) experiment.IMPORTANCECandida albicans is a major fungal pathogen, and due to the rise of fungal infections and emerging resistance to the limited antifungals available, it is important to develop novel and more specific antifungals. Protein-protein interactions (PPIs) can be applied as very specific drug targets. However, because of the aberrant codon usage of C. albicans, the traditional yeast two-hybrid system in Saccharomyces cerevisiae is difficult to use, and only a limited number of PPIs have been described in C. albicans To overcome this, a C. albicans two-hybrid (C2H) system was developed in 2010. The current work describes, for the first time, the application of the C2H system in a high-throughput setup. We hereby show the usefulness of the C2H system to investigate and detect PPIs in C. albicans, making it possible to further elucidate protein networks in C. albicans, which has the potential to lead to the development of novel antifungals which specifically disrupt PPIs important for virulence.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Mitochondrial permeability transition pore: Back to the drawing board

Current models theorizing on what the mitochondrial permeability transition (mPT) pore is made of, implicate the c-subunit rings of ATP synthase complex. However, two very recent studies, one on atomistic simulations and in the other disrupting all genes coding for the c subunit disproved those models. As a consequence of this, the structural elements of the pore remain unknown. The purpose of the present short-review is to (i) briefly review the latest findings, (ii) serve as an index for more comprehensive reviews regarding mPT specifics, (iii) reiterate on the potential pitfalls while investigating mPT in conjunction to bioenergetics, and most importantly (iv) suggest to those in search of mPT pore identity, to also look elsewhere.

A sensitive, semi-quantitative mammalian two-hybrid assay

Protein-protein interactions critically determine the function of a protein within the cell. Several methods have been developed for the analysis of protein interactions, including two-hybrid assays in yeast and mammals. Mammalian two-hybrid systems provide the ideal physiological environment to study the interactions of mammalian proteins; however, these approaches are limited in sensitivity and their ability to quantify interaction strength. Here, we present an inducible mammalian two-hybrid (iM2H) system using the small-molecule dimerizer rapalog for recruitment of multiple transactivation domains into the M2H system. This inducibility, combined with additional improvements of the iM2H components, results in an up to 100-fold increase in sensitivity compared with conventional M2H approaches. In addition, we include a number of reference interactions in our iM2H approach, which enable semiquantitative assessment of protein interactions. Using Groucho/Tle proteins and their binding partners, we demonstrate the applicability of our iM2H to established protein networks. Finally, to test the applicability of our system for drug screening, the interference of a small-molecule inhibitor on a known protein-protein interaction was tested, and the particular advantages of the internal reference interactions were shown.

DNA Methylation Mediated Down-Regulation of miR-370 Regulates Cell Growth through Activation of the Wnt/β-Catenin Signaling Pathway in Human Osteosarcoma Cells

MicroRNA-370 (miR-370) has been observed to act as a tumor suppressor through the targeting of different proteins in a variety of tumors. Our previous study indicated that miR-370 was able to target forkhead box protein M1 (FOXM1) to inhibit cell growth and metastasis in human osteosarcoma cells. In this study, we reported that FOXM1 interacted with β-catenin in vitro and in vivo. Similar to FOXM1, critical components of the Wnt signaling pathway, including β-catenin, c-Myc, and Cyclin D1, were also highly expressed in different human osteosarcoma cells lines. Pharmacological inhibition of FOXM1 or β-catenin but not of c-Myc was associated with the increased expression of miR-370. Ectopic expression of miR-370 inhibited the downstream signaling of β-catenin. Moreover, osteosarcoma cells treated with 5-AZA-2'-deoxycytidine (AZA), a DNA methylation inhibitor, exhibited increased levels of miR-370 and decreased levels of β-catenin downstream targets, which resulted in inhibition of cell proliferation and colony formation ability. In conclusion, our results supported a model in which the DNA methylation-mediated down-regulation of miR-370 reduced its inhibitory effect on FOXM1, thereby promoting FOXM1-β-catenin interaction and activating the Wnt/β-Catenin signaling pathway in human osteosarcoma cells.

Detecting Membrane Protein-protein Interactions Using the Mammalian Membrane Two-hybrid (MaMTH) Assay

Protein-protein interactions (PPIs) play an integral role in numerous cellular processes. Membrane protein interactions, in particular, are critical in cellular responses to stresses and stimuli, with dysfunction of these PPIs (e.g., due to aberrant expression and/or mutation of interaction partners) leading to a diverse array of pathological states. Exploration of the interaction space and dynamics of membrane proteins is difficult due to the limitations of current techniques used to study proteins in the biochemically complex environment of biological membranes. In the protocols below, we describe a newly developed membrane protein interaction assay called the Mammalian-Membrane Two-Hybrid (MaMTH), designed specifically for the detection of integral membrane PPIs in the context of living mammalian cells. Prior to using MaMTH, cell lines of interest are genetically modified to encode a reporter of choice. MaMTH "bait" and "prey" constructs of interest are also generated using Gateway cloning technology. The assay is then performed by co-transfection of baits and preys, with bait-prey interaction quantifiably assessed by way of a reporter signal (e.g., light (luciferase), fluorescence (GFP). © 2017 by John Wiley & Sons, Inc.

LUMIER: A Discovery Tool for Mammalian Protein Interaction Networks

Protein-protein interactions (PPIs) play an essential role in all biological processes. In vivo, PPIs occur dynamically and depend on extracellular cues. To discover novel protein-protein interactions in mammalian cells, we developed a high-throughput automated technology called LUMIER (LUminescence-based Mammalian IntERactome). In this approach, we co-express a Luciferase (LUC)-tagged fusion protein along with a Flag-tagged protein in an efficiently transfectable cell line such as HEK-293T cells. The interaction between the two proteins is determined by co-immunoprecipitation using an anti-Flag antibody, and the presence of the LUC-tagged interactor in the complex is subsequently detected via its luciferase activity. LUMIER can easily detect transmembrane protein partners, interactions that are signaling- or splice isoform-dependent, as well as those that may occur only in the presence of posttranslational modifications. Using various collections of Flag-tagged proteins, we have generated protein interaction networks for several TGF-β family receptors, Wnt pathway members, and have systematically analyzed the effect of neural-specific alternative splicing on protein interaction networks. The results have provided important insights into the physiological and functional relevance of some of the novel interactions found. LUMIER is highly scalable and can be used for both low- and high-throughput strategies. LUMIER is thus a valuable tool for the identification and characterization of dynamically regulated PPIs in mammalian systems. Here, we describe a manual version of LUMIER in a 96-well format that can be easily implemented in any laboratory.

Protein-protein interaction analysis for functional characterization of helicases

Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3. Proteomics methods are our main focus - affinity pull-downs and chemical cross-linking followed by mass spectrometry. We review advantages and limitations of these methods and provide general guidelines for their implementation in the functional analysis of helicases.

A plasmid-based expression system to study protein-protein interactions at the Golgi in vivo

There is still an unmet need for simple methods to verify, visualize, and confirm protein-protein interactions in vivo. Here we describe a plasmid-based system to study such interactions. The system is based on the transmembrane domain (TMD) of the EF-hand Ca(2+) sensor protein calneuron-2. We show that fusion of 28 amino acids that include the TMD of calneuron-2 to proteins of interest results in prominent localization on the cytoplasmic side of the Golgi. The recruitment of binding partners to the protein of interest fused to this sequence can then be easily visualized by fluorescent tags.

How do oncoprotein mutations rewire protein-protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype.

Two-hybrid-based systems: powerful tools for investigation of membrane traffic machineries

Protein-protein interactions regulate biological processes and are fundamental for cell functions. Recently, efforts have been made to define interactomes, which are maps of protein-protein interactions that are useful for understanding biological pathways and networks and for investigating how perturbations of these networks lead to diseases. Therefore, interactomes are becoming fundamental for establishing the molecular basis of human diseases and contributing to the discovery of effective therapies. Interactomes are constructed based on experimental data present in the literature and computational predictions of interactions. Several biochemical, genetic and biotechnological techniques have been used in the past to identify protein-protein interactions. The yeast two-hybrid system has beyond doubt represented a revolution in the field, being a versatile tool and allowing the immediate identification of the interacting proteins and isolation of the cDNA coding for the interacting peptide after in vivo screening. Recently, variants of the yeast two-hybrid assay have been developed, including high-throughput systems that promote the rapidly growing field of proteomics. In this review we will focus on the role of this technique in the discovery of Rab interacting proteins, highlighting the importance of high-throughput two-hybrid screening as a tool to study the complexity of membrane traffic machineries.

Efficient prediction of human protein-protein interactions at a global scale

BACKGROUND

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.

RESULTS

On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.

CONCLUSIONS

The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Detecting protein-protein interactions based on kinase-mediated growth induction of mammalian cells

Detection of protein–protein interactions (PPIs) is important for understanding numerous processes in mammalian cells; however, existing PPI detection methods often give significant background signals. Here, we propose a novel PPI-detection method based on kinase-mediated growth induction of mammalian cells. In this method, target proteins are fused to the intracellular domain of c-kit (c-kit ICD) and expressed in interleukin-3-dependent mammalian cells. The PPI induces dimerization and activation of c-kit ICDs, which leads to cell growth in the absence of interleukin-3. Using this system, we successfully detected the ligand-dependent homo-interaction of FKBP_F36V and hetero-interaction of FKBP and FRB_T2098L, as well as the constitutive interaction between MDM2 and a known peptide inhibitor. Intriguingly, cells expressing high-affinity peptide chimeras are selected from the mixture of the cell populations dominantly expressing low-affinity peptide chimeras. These results indicate that this method can detect PPIs with low background levels and is suitable for peptide inhibitor screening.

The mammalian-membrane two-hybrid assay (MaMTH) for probing membrane-protein interactions in human cells

Cell signaling, one of key processes in both normal cellular function and disease, is coordinated by numerous interactions between membrane proteins that change in response to stimuli. We present a split ubiquitin-based method for detection of integral membrane protein-protein interactions (PPIs) in human cells, termed mammalian-membrane two-hybrid assay (MaMTH). We show that this technology detects stimulus (hormone or agonist)-dependent and phosphorylation-dependent PPIs. MaMTH can detect changes in PPIs conferred by mutations such as those in oncogenic ErbB receptor variants or by treatment with drugs such as the tyrosine kinase inhibitor erlotinib. Using MaMTH as a screening assay, we identified CRKII as an interactor of oncogenic EGFR(L858R) and showed that CRKII promotes persistent activation of aberrant signaling in non-small cell lung cancer cells. MaMTH is a powerful tool for investigating the dynamic interactomes of human integral membrane proteins.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

Identification of protein interactions involved in cellular signaling

Protein-protein interactions drive biological processes. They are critical for all intra- and extracellular functions, and the technologies to analyze them are widely applied throughout the various fields of biological sciences. This study takes an in-depth view of some common principles of cellular regulation and provides a detailed account of approaches required to comprehensively map signaling protein-protein interactions in any particular cellular system or condition. We provide a critical review of the benefits and disadvantages of the yeast two-hybrid method and affinity purification coupled with mass spectrometric procedures for identification of signaling protein-protein interactions. In particular, we emphasize the quantitative and qualitative differences between tandem affinity and one-step purification (such as FLAG and Strep tag) methods. Although applicable to all types of interaction studies, a special section is devoted in this review to aspects that should be considered when attempting to identify signaling protein interactions that often are transient and weak by nature. Finally, we discuss shotgun and quantitative information that can be gleaned by MS-coupled methods for analysis of multiprotein complexes.

Proteomic approaches to the study of papillomavirus-host interactions

The identification of interactions between viral and host cellular proteins has provided major insights into papillomavirus research, and these interactions are especially relevant to the role of papillomaviruses in the cancers with which they are associated. Recent advances in mass spectrometry technology and data processing now allow the systematic identification of such interactions. This has led to an improved understanding of the different pathologies associated with the many papillomavirus types, and the diverse nature of these viruses is reflected in the spectrum of interactions with host proteins. Here we review a history of proteomic approaches, particularly as applied to the papillomaviruses, and summarize current techniques. Current proteomic studies on the papillomaviruses use yeast-two-hybrid or affinity purification-mass spectrometry approaches. We detail the advantages and disadvantages of each and describe current examples of papillomavirus proteomic studies, with a particular focus on the HPV E6 and E7 oncoproteins.

Large Scale Chemical Cross-linking Mass Spectrometry Perspectives

The spectacular heterogeneity of a complex protein mixture from biological samples becomes even more difficult to tackle when one’s attention is shifted towards different protein complex topologies, transient interactions, or localization of PPIs. Meticulous protein-by-protein affinity pull-downs and yeast-two-hybrid screens are the two approaches currently used to decipher proteome-wide interaction networks. Another method is to employ chemical cross-linking, which gives not only identities of interactors, but could also provide information on the sites of interactions and interaction interfaces. Despite significant advances in mass spectrometry instrumentation over the last decade, mapping Protein-Protein Interactions (PPIs) using chemical cross-linking remains time consuming and requires substantial expertise, even in the simplest of systems. While robust methodologies and software exist for the analysis of binary PPIs and also for the single protein structure refinement using cross-linking-derived constraints, undertaking a proteome-wide cross-linking study is highly complex. Difficulties include i) identifying cross-linkers of the right length and selectivity that could capture interactions of interest; ii) enrichment of the cross-linked species; iii) identification and validation of the cross-linked peptides and cross-linked sites.

In this review we examine existing literature aimed at the large-scale protein cross-linking and discuss possible paths for improvement. We also discuss short-length cross-linkers of broad specificity such as formaldehyde and diazirine-based photo-cross-linkers. These cross-linkers could potentially capture many types of interactions, without strict requirement for a particular amino-acid to be present at a given protein-protein interface. How these shortlength, broad specificity cross-linkers be applied to proteome-wide studies? We will suggest specific advances in methodology, instrumentation and software that are needed to make such a leap.

Transcription activator-like effector hybrids for conditional control and rewiring of chromosomal transgene expression

The ability to conditionally rewire pathways in human cells holds great therapeutic potential. Transcription activator-like effectors (TALEs) are a class of naturally occurring specific DNA binding proteins that can be used to introduce targeted genome modifications or control gene expression. Here we present TALE hybrids engineered to respond to endogenous signals and capable of controlling transgenes by applying a predetermined and tunable action at the single-cell level. Specifically, we first demonstrate that combinations of TALEs can be used to modulate the expression of stably integrated genes in kidney cells. We then introduce a general purpose two-hybrid approach that can be customized to regulate the function of any TALE either using effector molecules or a heterodimerization reaction. Finally, we demonstrate the successful interface of TALEs to specific endogenous signals, namely hypoxia signaling and microRNAs, essentially closing the loop between cellular information and chromosomal transgene expression.

High-throughput analysis of peptide-binding modules

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput (HTP) analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls associated with HTP analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system

The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.

Mapping protein-protein proximity in the purinosome

The enzymes in the human de novo purine synthesis pathway were found to form a cellular complex, the purinosome, upon culturing cells in purine-depleted medium (An, S., Kumar R., Sheets, E. D., and Benkovic, S. J. (2008) Science 320, 103-106). Purinosome formation and dissociation were found to be modulated by several factors, including the microtubule network and cell signaling involving protein phosphorylation. To determine whether the pathway enzymes are in physical contact, we probed for the protein-protein interactions (PPIs) within the purinosome with a novel application of the Tango PPI reporter system (Barnea, G., Strapps, W., Herrada, G., Berman, Y., Ong, J., Kloss, B., Axel, R., and Lee, K. J. (2008) Proc. Natl. Acad. Sci. U.S.A. 105, 64-69). We found PPIs among all six enzymes within the pathway and evidence for a core involving the first three enzymes. We also captured purinosomes under both purine-rich and purine-depleted conditions. The results provide additional insights into the transient nature and topography of the purinosome.

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.

Visualizing protein partnerships in living cells and organisms

In recent years, scientists have expanded their focus from cataloging genes to characterizing the multiple states of their translated products. One anticipated result is a dynamic map of the protein association networks and activities that occur within the cellular environment. While in vitro-derived network maps can illustrate which of a multitude of possible protein-protein associations could exist, they supply a falsely static picture lacking the subtleties of subcellular location (where) or cellular state (when). Generating protein association network maps that are informed by both subcellular location and cell state requires novel approaches that accurately characterize the state of protein associations in living cells and provide precise spatiotemporal resolution. In this review, we highlight recent advances in visualizing protein associations and networks under increasingly native conditions. These advances include second generation protein complementation assays (PCAs), chemical and photo-crosslinking techniques, and proximity-induced ligation approaches. The advances described focus on background reduction, signal optimization, rapid and reversible reporter assembly, decreased cytotoxicity, and minimal functional perturbation. Key breakthroughs have addressed many challenges and should expand the repertoire of tools useful for generating maps of protein interactions resolved in both time and space.

PUB-MS: a mass spectrometry-based method to monitor protein-protein proximity in vivo

The common techniques to study protein-protein proximity in vivo are not well adapted to the capabilities and the expertise of a standard proteomics laboratory, typically based on the use of mass spectrometry. With the aim of closing this gap, we have developed PUB-MS (for proximity utilizing biotinylation and mass spectrometry), an approach to monitor protein-protein proximity, based on biotinylation of a protein fused to a biotin-acceptor peptide (BAP) by a biotin-ligase, BirA, fused to its interaction partner. The biotinylation status of the BAP can be further detected by either Western analysis or mass spectrometry. The BAP sequence was redesigned for easy monitoring of the biotinylation status by LC-MS/MS. In several experimental models, we demonstrate that the biotinylation in vivo is specifically enhanced when the BAP- and BirA-fused proteins are in proximity to each other. The advantage of mass spectrometry is demonstrated by using BAPs with different sequences in a single experiment (allowing multiplex analysis) and by the use of stable isotopes. Finally, we show that our methodology can be also used to study a specific subfraction of a protein of interest that was in proximity with another protein at a predefined time before the analysis.

Recent advances in protein-protein interaction prediction: experimental and computational methods

INTRODUCTION

Proteins within the cell act as part of complex networks, which allow pathways and processes to function. Therefore, understanding how proteins interact is a significant area of current research.

AREAS COVERED

This review aims to present an overview of key experimental techniques (yeast two-hybrid, tandem affinity purification and protein microarrays) used to discover protein-protein interactions (PPIs), as well as to briefly discuss certain computational methods for predicting protein interactions based on gene localization, phylogenetic information, 3D structural modeling or primary protein sequence data. Due to the large-scale applicability of primary sequence-based methods, the authors have chosen to focus on this strategy for our review. There is an emphasis on a recent algorithm called Protein Interaction Prediction Engine (PIPE) that can predict global PPIs. The readers will discover recent advances both in the practical determination of protein interaction and the strategies that are available to attempt to anticipate interactions without the time and costs of experimental work.

EXPERT OPINION

Global PPI maps can help understand the biology of complex diseases and facilitate the identification of novel drug target sites. This study describes different techniques used for PPI prediction that we believe will significantly impact the development of the field in a new future. We expect to see a growing number of similar techniques capable of large-scale PPI predictions.

Investigating a macromolecular complex: the toolkit of methods

Structural biologists studying macromolecular complexes spend considerable effort doing strictly "non-structural" work: investigating the physiological relevance and biochemical properties of a complex, preparing homogeneous samples for structural analysis, and experimentally validating structure-based hypotheses regarding function or mechanism. Familiarity with the diverse perspectives and techniques available for studying complexes helps in the critical assessment of non-structural data, expedites the pre-structural characterization of a complex and facilitates the investigation of function. Here we survey the approaches and techniques used to study macromolecular complexes from various viewpoints, including genetics, cell and molecular biology, biochemistry/biophysics, structural biology, and systems biology/bioinformatics. The aim of this overview is to heighten awareness of the diversity of perspectives and experimental tools available for investigating complexes and of their usefulness for the structural biologist.

Building protein-protein interaction networks with proteomics and informatics tools

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

Identification of residues in the N-terminal PAS domains important for dimerization of Arnt and AhR

The basic helix–loop–helix (bHLH).PAS dimeric transcription factors have crucial roles in development, stress response, oxygen homeostasis and neurogenesis. Their target gene specificity depends in part on partner protein choices, where dimerization with common partner Aryl hydrocarbon receptor nuclear translocator (Arnt) is an essential step towards forming active, DNA binding complexes. Using a new bacterial two-hybrid system that selects for loss of protein interactions, we have identified 22 amino acids in the N-terminal PAS domain of Arnt that are involved in heterodimerization with aryl hydrocarbon receptor (AhR). Of these, Arnt E163 and Arnt S190 were selective for the AhR/Arnt interaction, since mutations at these positions had little effect on Arnt dimerization with other bHLH.PAS partners, while substitution of Arnt D217 affected the interaction with both AhR and hypoxia inducible factor-1α but not with single minded 1 and 2 or neuronal PAS4. Arnt uses the same face of the N-terminal PAS domain for homo- and heterodimerization and mutational analysis of AhR demonstrated that the equivalent region is used by AhR when dimerizing with Arnt. These interfaces differ from the PAS β-scaffold surfaces used for dimerization between the C-terminal PAS domains of hypoxia inducible factor-2α and Arnt, commonly used for PAS domain interactions.

Addressing the Challenges of Detecting Epistasis in Genome-Wide Association Studies of Common Human Diseases Using Biological Expert Knowledge

Recent technological developments in the field of genetics have given rise to an abundance of research tools, such as genome-wide genotyping, that allow researchers to conduct genome-wide association studies (GWAS) for detecting genetic variants that confer increased or decreased susceptibility to disease. However, discovering epistatic, or gene-gene, interactions in high dimensional datasets is a problem due to the computational complexity that results from the analysis of all possible combinations of single-nucleotide polymorphisms (SNPs). A recently explored approach to this problem employs biological expert knowledge, such as pathway or protein-protein interaction information, to guide an analysis by the selection or weighting of SNPs based on this knowledge. Narrowing the evaluation to gene combinations that have been shown to interact experimentally provides a biologically concise reason why those two genes may be detected together statistically. This chapter discusses the challenges of discovering epistatic interactions in GWAS and how biological expert knowledge can be used to facilitate genome-wide genetic studies.

Filtering and interpreting large-scale experimental protein-protein interaction data

Rarely acting in isolation, it is invariably the physical associations among proteins that define their biological activity, necessitating the study of the cellular meshwork of protein-protein interactions (PPI) before a full appreciation of gene function can be achieved. The past few years have seen a marked expansion in the both the sheer volume and number of organisms for which high-quality interaction data is available, with high-throughput interaction screening and detection techniques showing consistent improvement both in scale and sensitivity. Although techniques for large-scale PPI mapping are increasingly being applied to new organisms, including human, there is a corresponding need to rigorously evaluate, benchmark, and impartially filter the results. This chapter explores methods for PPI dataset evaluation, including a survey of previous techniques applied by landmark studies in the field and a discussion of promising new experimental approaches. We further outline practical suggestions and useful tools for interpreting newly generated PPI data. As the majority of large-scale experimental data has been generated for the budding yeast S. cerevisiae, most of the techniques and datasets described are from the perspective of this model unicellular eukaryote; however, extensions to other organisms including mammals are mentioned where possible.

Formation and toxicity of soluble polyglutamine oligomers in living cells

BACKGROUND

Aggregation and cytotoxicity of mutant proteins containing an expanded number of polyglutamine (polyQ) repeats is a hallmark of several diseases, including Huntington's disease (HD). Within cells, mutant Huntingtin (mHtt) and other polyglutamine expansion mutant proteins exist as monomers, soluble oligomers, and insoluble inclusion bodies (IBs). Determining which of these forms constitute a toxic species has proven difficult. Recent studies support a role for IBs as a cellular coping mechanism to sequester levels of potentially toxic soluble monomeric and oligomeric species of mHtt.

METHODOLOGY/PRINCIPAL FINDINGS

When fused to a fluorescent reporter (GFP) and expressed in cells, the soluble monomeric and oligomeric polyglutamine species are visually indistinguishable. Here, we describe two complementary biophysical fluorescence microscopy techniques to directly detect soluble polyglutamine oligomers (using Htt exon 1 or Htt(ex1)) and monitor their fates in live cells. Photobleaching analyses revealed a significant reduction in the mobilities of mHtt(ex1) variants consistent with their incorporation into soluble microcomplexes. Similarly, when fused to split-GFP constructs, both wildtype and mHtt(ex1) formed oligomers, as evidenced by the formation of a fluorescent reporter. Only the mHtt(ex1) split-GFP oligomers assembled into IBs. Both FRAP and split-GFP approaches confirmed the ability of mHtt(ex1) to bind and incorporate wildtype Htt into soluble oligomers. We exploited the irreversible binding of split-GFP fragments to forcibly increase levels of soluble oligomeric mHtt(ex1). A corresponding increase in the rate of IBs formation and the number formed was observed. Importantly, higher levels of soluble mHtt(ex1) oligomers significantly correlated with increased mutant cytotoxicity, independent of the presence of IBs.

CONCLUSIONS/SIGNIFICANCE

Our study describes powerful and sensitive tools for investigating soluble oligomeric forms of expanded polyglutamine proteins, and their impact on cell viability. Moreover, these methods should be applicable for the detection of soluble oligomers of a wide variety of aggregation prone proteins.

Characterization and optimization of a novel protein-protein interaction biosensor high-content screening assay to identify disruptors of the interactions between p53 and hDM2

We present here the characterization and optimization of a novel imaging-based positional biosensor high-content screening (HCS) assay to identify disruptors of p53-hDM2 protein-protein interactions (PPIs). The chimeric proteins of the biosensor incorporated the N-terminal PPI domains of p53 and hDM2, protein targeting sequences (nuclear localization and nuclear export sequence), and fluorescent reporters, which when expressed in cells could be used to monitor p53-hDM2 PPIs through changes in the subcellular localization of the hDM2 component of the biosensor. Coinfection with the recombinant adenovirus biosensors was used to express the NH-terminal domains of p53 and hDM2, fused to green fluorescent protein and red fluorescent protein, respectively, in U-2 OS cells. We validated the p53-hDM2 PPI biosensor (PPIB) HCS assay with Nutlin-3, a compound that occupies the hydrophobic pocket on the surface of the N-terminus of hDM2 and blocks the binding interactions with the N-terminus of p53. Nutlin-3 disrupted the p53-hDM2 PPIB in a concentration-dependent manner and provided a robust, reproducible, and stable assay signal window that was compatible with HCS. The p53-hDM2 PPIB assay was readily implemented in HCS and we identified four (4) compounds in the 1,280-compound Library of Pharmacologically Active Compounds that activated the p53 signaling pathway and elicited biosensor signals that were clearly distinct from the responses of inactive compounds. Anthracycline (topoisomerase II inhibitors such as mitoxantrone and ellipticine) and camptothecin (topoisomerase I inhibitor) derivatives including topotecan induce DNA double strand breaks, which activate the p53 pathway through the ataxia telangiectasia mutated-checkpoint kinase 2 (ATM-CHK2) DNA damage response pathway. Although mitoxantrone, ellipticine, camptothecin, and topotecan all exhibited concentration-dependent disruption of the p53-hDM2 PPIB, they were much less potent than Nutlin-3. Further, their corresponding cellular images and quantitative HCS data did not completely match the Nutlin-3 phenotypic profile.

IC-tagging and protein relocation to ARV muNS inclusions: a method to study protein-protein interactions in the cytoplasm or nucleus of living cells

Background

Characterization of protein-protein interactions is essential for understanding cellular functions. Although there are many published methods to analyze protein-protein interactions, most of them present serious limitations. In a different study we have characterized a novel avian reovirus muNS-based protein tagging and inclusion targeting method, and demonstrated its validity to purify free an immobilized protein.

Methodology/Principal Findings

Here we present a method to identify protein-protein interactions inside living eukaryotic cells (tested in primate and avian cells). When p53 was tagged with Intercoil (IC; muNS residues 477–542), it not only got integrated into muNS cytoplasmic inclusions, but also attracted its known ligand SV40 large T antigen (TAg) to these structures. We have also adapted this system to work within the cell nucleus, by creating muNS-related protein chimeras that form nuclear inclusions. We show that nuclear muNS-derived inclusions are as efficient as cytoplasmic ones in capturing IC-tagged proteins, and that the proteins targeted to nuclear inclusions are able to interact with their known ligands.

Conclusions/Significance

Our protein redistribution method does not present the architectural requirement of re-constructing a transcription factor as any of the two-hybrid systems do. The method is simple and requires only cell transfection and a fluorescence microscope. Our tagging method can be used either in the cytoplasm or the nucleus of living cells to test protein-protein interactions or to perform functional studies by protein ligand sequestration.

Strategies towards high-quality binary protein interactome maps

Many processes in a cell depend on protein-protein interactions (PPIs) and perturbations of these interactions can lead to diseases. Comprehensive knowledge of PPI networks will not only give us information on how the cell is organized, but will also provide new drug targets. Current binary PPI networks are mainly generated by high-throughput yeast two-hybrid. Due to the small overlap of these maps, it has long been assumed that these maps are of low quality containing many false positives. However, by using an orthogonal two-hybrid method, MAPPIT (mammalian protein-protein interaction trap), these maps were shown to be of high quality suggesting that the limited overlap is likely due to low sensitivity and not to low specificity.

Role for protein-protein interaction databases in human genetics

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.