Showing papers on "Protein–protein interaction published in 2002"

Principles of protein-protein interactions.

Abstract: In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. Thus experimental investigation of protein-protein interactions has been extensive, including recent large-scale screens using mass spectrometry. The role of computational research on protein-protein interactions encompasses not only prediction, but also understanding the nature of the interactions and their three-dimensional structures. I will discuss properties such as sequence conservation and co-regulation of genes and proteins involved in different types of physical interactions. Given that all proteins consist of their evolutionary units, the domains, all interactions occur between these domains. The interactions between domains belonging to different protein families will be the second topic of my talk.

Relating Whole-Genome Expression Data with Protein-Protein Interactions

Abstract: We investigate the relationship of protein-protein interactions with mRNA expression levels, by integrating a variety of data sources for yeast. We focus on known protein complexes that have clearly defined interactions between their subunits. We find that subunits of the same protein complex show significant coexpression, both in terms of similarities of absolute mRNA levels and expression profiles, e.g., we can often see subunits of a complex having correlated patterns of expression over a time course. We classify the yeast protein complexes as either permanent or transient, with permanent ones being maintained through most cellular conditions. We find that, generally, permanent complexes, such as the ribosome and proteasome, have a particularly strong relationship with expression, while transient ones do not. However, we note that several transient complexes, such as the RNA polymerase II holoenzyme and the replication complex, can be subdivided into smaller permanent ones, which do have a strong relationship to gene expression. We also investigated the interactions in aggregated, genome-wide data sets, such as the comprehensive yeast two-hybrid experiments, and found them to have only a weak relationship with gene expression, similar to that of transient complexes. (Further details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)

Analyzing yeast protein–protein interaction data obtained from different sources

Abstract: High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.

Inferring Domain–Domain Interactions From Protein–Protein Interactions

Abstract: With the advancement of genomic technology and genome-wide analysis of organisms, more and more organisms are being studied extensively for gene expression on a global scale. Expression profiling is now being used increasingly to analyze gene functions or to functionally group genes on the basis of their expression profiles (Lockhart and Winzeler 2000). After the completion of the genome sequence of Saccharomyces cerevisiae (Goffeau et al. 1996), a budding yeast, many researchers have undertaken the task of functionally analyzing the yeast genome, comprising ∼6280 proteins (YPD), of which roughly one-third do not have known functions (Mewes et al. 2002). Genes can be clustered on the basis of similar expression profiles. This makes it possible to assign a biological function to genes, depending on the functions of other genes in the cluster (Eisen et al. 1998). However, expression profiling gives an indirect measure of a gene product's biological and cellular function. A more complete study of an organism could possibly be achieved by looking at not only the mRNA levels but also the proteins they encode. It is well known that mRNA levels alone are not sufficient to group genes into different functions, because not all mRNAs end up being translated. Most biological functions within a cell are carried out by proteins and most cellular processes and biochemical events are ultimately achieved by interactions of proteins with one another. Thus, it is important to look at protein expression and their interactions simultaneously. Affinity chromatography, two-hybrid assay, copurification, coimmunoprecipitation, and cross-linking are some of the tools used to verify proteins that are associated physically with one another. Among these techniques, the two-hybrid assay has been used widely to analyze protein–protein interactions in Saccharomyces cerevisiae (Ito et al. 2000, 2001a; Uetz et al. 2000). Their protein interaction profiles have made it possible to look at the interaction networks comprising a large number of proteins and to also functionally classify proteins of unknown function. Uetz et al. (2000) used two different approaches in their two-hybrid experiments. The first was a protein array approach with 192 yeast proteins as bait, Gal4–DNA-binding domain fusions, and ∼6000 yeast transformants as prey, Gal4-activation domain fusions. The second, an interaction sequence tag (IST) approach, used high-throughput screens of an activation domain library encoding ∼6000 yeast genes that were pooled. All yeast proteins were cloned into DNA-binding domain vectors. Of the 6144 yeast ORF PCR products, 5345 were successfully cloned. Their first approach revealed 281 interactions, with less stringent selection criteria, using HIS3. The second approach revealed 692 interactions with the more stringent URA3 selection method. Ito et al. (2001a) used a similar method and reported 4549 interactions among 3278 proteins. Some interactions in both data sets were repeated (bait and prey exchanged). They imposed a more rigorous selection criterion including four reporter genes, ADE2, HIS3, URA3, and MEL1, to minimize false positives due to promoter-specific activation. All of these genes have Gal4-responsive promoter. Computational methods have been developed to predict protein–protein interactions. Those approaches include the Rosetta stone/gene fusion method (Enright et al. 1999; Marcotte et al. 1999a), the phylogenetic profile method (Pellegrini et al. 1999) and the method combining multiple sources of data (Marcotte et al. 1999b). Other computational methods to predict protein–protein interaction have been presented on the basis of different principles, including the interaction domain pair profile method (Rain et al. 2001; Wojcik and Schachter 2001) and the support vector machine learning method (Bock and Gough 2001). Gomez et al. (2001) developed probabilistic models for protein–protein interactions. Sprinzak and Margalit (2001) analyzed over-represented sequence-signature pairs among protein–protein interactions. In our study, we use the protein–protein interaction (PPI) data sets of Uetz and Ito to predict domain–domain interactions (DDI) in yeast proteins. The protein-domain information is obtained from a protein-domain family database called PFAM (Bateman et al. 2000). Because every protein can be characterized by either a distinct domain or a combination of domains, understanding domain interactions is crucial to understanding the nature and extent of biomolecular interactions. Our study predicts probable domain–domain interactions solely on the basis of the information of protein–protein interactions. Because proteins interact with one another through their specific domains, predicting domain–domain interactions on a global scale from the entire protein interaction data set make it possible to predict previously unknown protein–protein interactions from their domains. Thus, domain interactions extend the functional significance of proteins and present a global view of the protein–protein interaction network within a cell responsible for carrying out various biological and cellular functions. It is known that the yeast two-hybrid assay is not accurate in determining protein–protein interactions, and the interaction data used in our study certainly contain many false positive and false negative errors (Legrain and Selig 2000; Hazbun and Fields 2001; Mrowka et al. 2001). Taking into account these errors, we apply the Maximum Likelihood approach to estimate the probability of domain–domain interactions. We have also taken into account multiplicity of observations in the two data sets as evidenced by exchanged baits and preys, repeated interactions, and synonymously used gene names. To assess the accuracy of our method, we predict protein–protein interactions using the inferred domain–domain interactions, and compare them with the observed interactions. The following results are obtained: (1) Our method has shown robustness in analyzing incomplete data sets and dealing with various experimental errors, and we achieve 42.5% specificity and 77.6% sensitivity using the combined Uetz and Ito data. The relative low specificity may be caused by the fact that the observed protein–protein interactions in the Uetz and Ito combined data represent only a small fraction of all of the real interactions. (2) Comparing our predicted protein–protein interactions with the MIPS protein–protein interactions obtained by methods other than the two-hybrid assays, we show that the prediction rate of our method is about 100 times better than that of a random assignment. (3) We also compare the gene expression profile correlation coefficients of our predictions with those of random protein pairs, and our predictions have a higher mean correlation coefficient. (4) Finally, we check for biological significance of our novel predictions, and find several interesting interactions such as RPS0A interacting with APG17 and TAF40 interacting with SPT3, which are consistent with the functions of the proteins. A complete description of our model and the results are given in the sections below.

Beta-lactamase protein fragment complementation assays as in vivo and in vitro sensors of protein protein interactions.

Abstract: We have previously described a strategy for detecting protein protein interactions based on protein interaction assisted folding of rationally designed fragments of enzymes. We call this strategy the protein fragment complementation assay (PCA). Here we describe PCAs based on the enzyme TEM-1 beta-lactamase (EC: 3.5.2.6), which include simple colorimetric in vitro assays using the cephalosporin nitrocefin and assays in intact cells using the fluorescent substrate CCF2/AM (ref. 6). Constitutive protein protein interactions of the GCN4 leucine zippers and of apoptotic proteins Bcl2 and Bad, and the homodimerization of Smad3, were tested in an in vitro assay using cell lysates. With the same in vitro assay, we also demonstrate interactions of protein kinase PKB with substrate Bad. The in vitro assay is facile and amenable to high-throughput modes of screening with signal-to-background ratios in the range of 10:1 to 250:1, which is superior to other PCAs developed to date. Furthermore, we show that the in vitro assay can be used for quantitative analysis of a small molecule induced protein interaction, the rapamycin-induced interaction of FKBP and yeast FRB (the FKBP-rapamycin binding domain of TOR (target of rapamycin)). The assay reproduces the known dissociation constant and number of sites for this interaction. The combination of in vitro colorimetric and in vivo fluorescence assays of beta-lactamase in mammalian cells suggests a wide variety of sensitive and high-throughput large-scale applications, including in vitro protein array analysis of protein protein or enzyme protein interactions and in vivo applications such as clonal selection for cells expressing interacting protein partners.

Noninvasive imaging of protein–protein interactions in living subjects by using reporter protein complementation and reconstitution strategies

Abstract: In this study we have developed bioluminescence-imaging strategies to noninvasively and quantitatively image protein-protein interactions in living mice by using a cooled charge-coupled device camera and split reporter technology. We validate both complementation and intein-mediated reconstitution of split firefly luciferase proteins driven by the interaction of two strongly interacting proteins, MyoD and Id. We use transient transfection of cells and image MyoD-Id interaction after induction of gene expression in cell culture and in cells implanted into living mice. Techniques to study protein-protein interactions in living subjects will allow the study of cellular networks, including signal transduction pathways, as well as development and optimization of pharmaceuticals for modulating protein-protein interactions.

Interrogating protein interaction networks through structural biology

Abstract: Protein–protein interactions are central to most biological processes. Although much recent effort has been put into methods to identify interacting partners, there has been a limited focus on how these interactions compare with those known from three-dimensional (3D) structures. Because comparison of protein interactions often involves considering homologous, but not identical, proteins, a key issue is whether proteins that are homologous to an interacting pair will interact in the same way, or interact at all. Accordingly, we describe a method to test putative interactions on complexes of known 3D structure. Given a 3D complex and alignments of homologues of the interacting proteins, we assess the fit of any possible interacting pair on the complex by using empirical potentials. For studies of interacting protein families that show different specificities, the method provides a ranking of interacting pairs useful for prioritizing experiments. We evaluate the method on interacting families of proteins with multiple complex structures. We then consider the fibroblast growth factor/receptor system and explore the intersection between complexes of known structure and interactions proposed between yeast proteins by methods such as two-hybrids. We provide confirmation for several interactions, in addition to suggesting molecular details of how they occur.

Prediction of protein--protein interaction sites in heterocomplexes with neural networks.

Abstract: In this paper we address the problem of extracting features relevant for predicting protein–protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.

MULTIPROSPECTOR: An algorithm for the prediction of protein–protein interactions by multimeric threading

Abstract: In this postgenomic era, the ability to identify protein–protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein–protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z-score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI-BLAST using a (less) permissive E-value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein–protein interactions are made; 1138 of these 2865 interactions have counterparts in the Database of Interacting Proteins. In contrast, PSI-BLAST made 1781 predictions, and 1215 have counterparts in DIP. An estimation of the false-negative rate for yeast-predicted interactions has also been provided. Thus, a promising approach to help assist in the assignment of protein–protein interactions on a genomic scale has been developed. Proteins 2002;49:350–364. © 2002 Wiley-Liss, Inc.

Analysis of MADS box protein-protein interactions in living plant cells.

Abstract: Over the last decade, the yeast two-hybrid system has become the tool to use for the identification of protein–protein interactions and recently, even complete interactomes were elucidated by this method. Nevertheless, it is an artificial system that is sensitive to errors resulting in the identification of false-positive and false-negative interactions. In this study, plant MADS box transcription factor interactions identified by yeast two-hybrid systems where studied in living plant cells by a technique based on fluorescence resonance energy transfer (FRET). Petunia MADS box proteins were fused to either cyan fluorescent protein or yellow fluorescent protein and transiently expressed in protoplasts followed by FRET-spectral imaging microscopy and FRET-fluorescence lifetime imaging microscopy to detect FRET and hence protein–protein interactions. All petunia MADS box heterodimers identified in yeast were confirmed in protoplasts. However, in contrast to the yeast two-hybrid results, homodimerization was demonstrated in plant cells for three petunia MADS box proteins. Heterodimers were identified between the ovule-specific MADS box protein FLORAL BINDING PROTEIN 11 and members of the petunia FLORAL BINDING PROTEIN 2 subfamily, which are also expressed in ovules, suggesting that these dimers play a role in ovule development. Furthermore, the role of dimerization in translocation of MADS box protein dimers to the nucleus is demonstrated, and the nuclear localization signal of MADS box proteins has been mapped to the N-terminal region of the MADS domain by means of mutant analyses.

Protein–protein interactions monitored in mammalian cells via complementation of β-lactamase enzyme fragments

Abstract: We have defined inactive α and ω fragments of β-lactamase that can complement to form a functional enzyme in both bacteria and mammalian cells, serving as a readout for the interaction of proteins fused to the fragments. Critical to this advance was the identification of a tripeptide, Asn-Gly-Arg, which when juxtaposed at the carboxyl terminus of the α fragment increased complemented enzyme activity by up to 4 orders of magnitude. β-Lactamase is well suited to monitoring constitutive and inducible protein interactions because it is small (29 kDa), monomeric, and assayable with a fluorescent cell-permeable substrate. The negligible background, the magnitude of induced signal caused by enzymatic amplification, and detection of signal within minutes are unparalleled in mammalian protein interaction detection systems published to date.

A protein-domain microarray identifies novel protein-protein interactions.

Abstract: Protein domains mediate protein-protein interactions through binding to short peptide motifs in their corresponding ligands. These peptide recognition modules are critical for the assembly of multiprotein complexes. We have arrayed glutathione S-transferase (GST) fusion proteins, with a focus on protein interaction domains, on to nitrocellulose-coated glass slides to generate a protein-domain chip. Arrayed protein-interacting modules included WW (a domain with two conserved tryptophans), SH3 (Src homology 3), SH2, 14.3.3, FHA (forkhead-associated), PDZ (a domain originally identified in PSD-95, DLG and ZO-1 proteins), PH (pleckstrin homology) and FF (a domain with two conserved phenylalanines) domains. Here we demonstrate, using peptides, that the arrayed domains retain their binding integrity. Furthermore, we show that the protein-domain chip can 'fish' proteins out of a total cell lysate; these domain-bound proteins can then be detected on the chip with a specific antibody, thus producing an interaction map for a cellular protein of interest. Using this approach we have confirmed the domain-binding profile of the signalling molecule Sam68 (Src-associated during mitosis 68), and have identified a new binding profile for the core small nuclear ribonucleoprotein SmB'. This protein-domain chip not only identifies potential binding partners for proteins, but also promises to recognize qualitative differences in protein ligands (caused by post-translational modification), thus getting at the heart of signal transduction pathways.

Noninvasive quantitative imaging of protein–protein interactions in living subjects

Abstract: We are developing methods to image molecular and cellular events in living subjects. In this study, we validate imaging of protein—protein interactions in living mice by using bioluminescent optical imaging. We use the well studied yeast two-hybrid system adapted for mammalian cells and modify it to be inducible. We employ the NF-κB promoter to drive expression of two fusion proteins (VP16-MyoD and GAL4-ID). We modulate the NF-κB promoter through tumor necrosis factor α. Firefly luciferase reporter gene expression is driven by the interaction of MyoD and ID through a transcriptional activation strategy. We demonstrate the ability to detect this induced protein–protein interaction in cell culture and image this induced interaction in living mice by using transiently transfected cells. The current approach will be a valuable and potentially generalizable tool to noninvasively and quantitatively image protein–protein interactions in living subjects. The approaches validated should have important implications for the study of protein–protein interactions in cells maintained in their natural in vivo environment as well as for the in vivo evaluation of new pharmaceuticals targeted to modulate protein–protein interactions.

Protein-dependent ribozymes report molecular interactions in real time

Abstract: Most approaches to monitoring interactions between biological macromolecules require large amounts of material, rely upon the covalent modification of an interaction partner, or are not amenable to real-time detection. We have developed a generalizable assay system based on interactions between proteins and reporter ribozymes. The assay can be configured in a modular fashion to monitor the presence and concentration of a protein or of molecules that modulate protein function. We report two applications of the assay: screening for a small molecule that disrupts protein binding to its nucleic acid target and screening for protein protein interactions. We screened a structurally diverse library of antibiotics for small molecules that modulate the activity of HIV-1 Rev-responsive ribozymes by binding to Rev. We identified an inhibitor that subsequently inhibited HIV-1 replication in cells. A simple format switch allowed reliable monitoring of domain-specific interactions between the blood-clotting factor thrombin and its protein partners. The rapid identification of interactions between proteins or of compounds that disrupt such interactions should have substantial utility for the drug-discovery process.

Interaction generality, a measurement to assess the reliability of a protein-protein interaction.

Abstract: Here we introduce the ‘interaction generality’ measure, a new method for computationally assessing the reliability of protein–protein interactions obtained in biological experiments. This measure is basically the number of proteins involved in a given interaction and also adopts the idea that interactions observed in a complicated interaction network are likely to be true positives. Using a group of yeast protein–protein interactions identified in various biological experiments, we show that interactions with low generalities are more likely to be reproducible in other independent assays. We constructed more reliable networks by eliminating interactions whose generalities were above a particular threshold. The rate of interactions with common cellular roles increased from 63% in the unadjusted estimates to 79% in the refined networks. As a result, the rate of cross-talk between proteins with different cellular roles decreased, enabling very clear predictions of the functions of some unknown proteins. The results suggest that the interaction generality measure will make interaction data more useful in all organisms and may yield insights into the biological roles of the proteins studied.

Hierarchical, clustered protein interactions with U4/U6 snRNA: a biochemical role for U4/U6 proteins

Abstract: During activation of the spliceosome, the U4/U6 snRNA duplex is dissociated, releasing U6 for subsequent base pairing with U2 snRNA. Proteins that directly bind the U4/U6 interaction domain potentially could mediate these structural changes. We thus investigated binding of the human U4/U6-specific proteins, 15.5K, 61K and the 20/60/90K protein complex, to U4/U6 snRNA in vitro. We demonstrate that protein 15.5K is a nucleation factor for U4/U6 snRNP assembly, mediating the interaction of 61K and 20/60/90K with U4/U6 snRNA. A similar hierarchical assembly pathway is observed for the U4atac/U6atac snRNP. In addition, we show that protein 61K directly contacts the 5′ portion of U4 snRNA via a novel RNA-binding domain. Furthermore, the 20/60/90K heteromer requires stem II but not stem I of the U4/U6 duplex for binding, and this interaction involves a direct contact between protein 90K and U6. This uneven clustering of the U4/U6 snRNP-specific proteins on U4/U6 snRNA is consistent with a sequential dissociation of the U4/U6 duplex prior to spliceosome catalysis.

Detergent extraction identifies different VirB protein subassemblies of the type IV secretion machinery in the membranes of Agrobacterium tumefaciens.

Abstract: The VirB/D4 type IV secretion system of Agrobacterium tumefaciens translocates virulence factors (VirE2, VirF, and the VirD2-T-DNA complex) to plant cells. The membrane-bound translocation machinery consists of 12 proteins (VirB1–11 and VirD4) required for substrate translocation. Protein–protein interactions in the membranes were analyzed after extraction with the mild detergent dodecyl-β-d-maltoside followed by separation under native conditions. Incubation of the membranes with increasing concentrations of the detergent differentially extracted virulence proteins. Separation of the solubilized proteins by blue native electrophoresis revealed cofractionation between two classes of protein complexes containing VirB7. The first class, consisting of major T-pilus component VirB2 and associated proteins VirB5 and VirB7, comigrated in the low molecular mass portion of the gel of about 100 kDa. The second class contains putative translocation complex core components VirB8, VirB9, and VirB10 in the high molecular mass portion of the gel larger than 232 kDa, as well as VirB7. Solubilized proteins were characterized further by gel filtration chromatography. This procedure separated T-pilus-associated proteins VirB2, VirB5, and VirB7 in the low molecular mass range from the other components of the translocation machinery and the substrates VirE2 and VirD2. Fractionation of VirB7-containing complexes (VirB7-VirB7 homodimers and VirB7-VirB9 heterodimers) suggested that they may link the T-pilus components to the core of the translocation machinery. Based on previously described VirB protein interactions and biochemical analysis of C58 wild type as well as of virB5 and virB6 deletion mutants, a model of T-pilus assembly in A. tumefaciens is suggested.

Subunit composition of a bicomponent toxin : staphylococcal leukocidin forms an octameric transmembrane pore

Abstract: Staphylococcal leukocidin pores are formed by the obligatory interaction of two distinct polypeptides, one of class F and one of class S, making them unique in the family of β-barrel pore-forming toxins (β-PFTs). By contrast, other β-PFTs form homo-oligomeric pores; for example, the staphylococcal α-hemolysin (αHL) pore is a homoheptamer. Here, we deduce the subunit composition of a leukocidin pore by two independent methods: gel shift electrophoresis and site-specific chemical modification during single-channel recording. Four LukF and four LukS subunits coassemble to form an octamer. This result in part explains properties of the leukocidin pore, such as its high conductance compared to the αHL pore. It is also pertinent to the mechanism of assembly of β-PFT pores and suggests new possibilities for engineering these proteins.

The Chediak-Higashi Protein Interacts with SNARE Complex and Signal Transduction Proteins

Abstract: Chediak-Higashi syndrome (CHS) is an inherited immunodeficiency disease characterized by giant lysosomes and impaired leukocyte degranulation. CHS results from mutations in the lysosomal trafficking regulator (LYST) gene, which encodes a 425-kD cytoplasmic protein of unknown function. The goal of this study was to identify proteins that interact with LYST as a first step in understanding how LYST modulates lysosomal exocytosis. Fourteen cDNA fragments, covering the entire coding domain of LYST, were used as baits to screen five human cDNA libraries by a yeast two-hybrid method, modified to allow screening in the activation and the binding domain, three selectable markers, and more stringent confirmation procedures. Five of the interactions were confirmed by an in vitro binding assay. Twenty-one proteins that interact with LYST were identified in yeast two-hybrid screens. Four interactions, confirmed directly, were with proteins important in vesicular transport and signal transduction (the SNARE-complex protein HRS, 14-3-3, and casein kinase II). On the basis of protein interactions, LYST appears to function as an adapter protein that may juxtapose proteins that mediate intracellular membrane fusion reactions. The pathologic manifestations observed in CHS patients and in mice with the homologous mutation beige suggest that understanding the role of LYST may be relevant to the treatment of not only CHS but also of diseases such as asthma, urticaria, and lupus, as well as to the molecular dissection of the CHS-associated cancer predisposition.

Evidence for direct protein-protein interaction between members of the enterobacterial Hha/YmoA and H-NS families of proteins.

Abstract: Escherichia coli nucleoid-associated H-NS protein interacts with the Hha protein, a member of a new family of global modulators that also includes the YmoA protein from Yersinia enterocolitica. This interaction has been found to be involved in the regulation of the expression of the toxin α-hemolysin. In this study, we further characterize the interaction between H-NS and Hha. We show that the presence of DNA in preparations of copurified His-Hha and H-NS is not directly implicated in the interaction between the proteins. The precise molecular mass of the H-NS protein retained by Hha, obtained by mass spectrometry analysis, does not show any posttranslational modification other than removal of the N-terminal Met residue. We constructed an H-NS-His recombinant protein and found that, as expected, it interacts with Hha. We used a Ni2+-nitrilotriacetic acid agarose method for affinity chromatography copurification of proteins to identify the H-NS protein of Y. enterocolitica. We constructed a six-His-YmoA recombinant protein derived from YmoA, the homologue of Hha in Y. enterocolitica, and found that it interacts with Y. enterocolitica H-NS. We also cloned and sequenced the hns gene of this microorganism. In the course of these experiments we found that His-YmoA can also retain H-NS from E. coli. We also found that the hns gene of Y. enterocolitica can complement an hns mutation of E. coli. Finally, we describe for the first time systematic characterization of missense mutant alleles of hha and truncated Hha′ proteins, and we report a striking and previously unnoticed similarity of the Hha family of proteins to the oligomerization domain of the H-NS proteins.

Protein–Protein Interactions Between Large Proteins: Two-Hybrid Screening Using a Functionally Classified Library Composed of Long cDNAs

Abstract: Large proteins have multiple domains that are potentially capable of binding many kinds of partners. It is conceivable, therefore, that such proteins could function as an intricate framework of assembly protein complexes. To comprehensively study protein–protein interactions between large KIAA proteins, we have constructed a library composed of 1087 KIAA cDNA clones based on prior functional classifications done in silico. We were guided by two principles that raise the success rate for detecting interactions per tested combination: we avoided testing low-probability combinations, and reduced the number of potential false negatives that arise from the fact that large proteins cannot reliably be expressed in yeast. The latter was addressed by constructing a cDNA library comprised of random fragments encoding large proteins. Cytoplasmic domains of KIAA transmembrane proteins (>1000 amino acids) were used as bait for yeast two-hybrid screening. Our analyses reveal that several KIAA proteins bearing a transmembrane region have the capability of binding to other KIAA proteins containing domains (e.g., PDZ, SH3, rhoGEF, and spectrin) known to be localized to highly specialized submembranous sites, indicating that they participate in cellular junction formation, receptor or channel clustering, and intracellular signaling events. Our representative library should be a very useful resource for detecting previously unidentified interactions because it complements conventional expression libraries, which seldom contain large cDNAs. [Interaction data accession numbers are BIND ID 12487–12570. Supplemental material is available online at http://www.genome.org.]