Pairwise Protein-protein Interactions Research Articles

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells.

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Higher-order protein assembly controls kinetochore formation.

To faithfully segregate chromosomes during vertebrate mitosis, kinetochore-microtubule interactions must be restricted to a single site on each chromosome. Prior work on pair-wise kinetochore protein interactions has been unable to identify the mechanisms that prevent outer kinetochore formation in regions with a low density of CENP-A nucleosomes. To investigate the impact of higher-order assembly on kinetochore formation, we generated oligomers of the inner kinetochore protein CENP-T using two distinct, genetically engineered systems in human cells. Although individual CENP-T molecules interact poorly with outer kinetochore proteins, oligomers that mimic centromeric CENP-T density trigger the robust formation of functional, cytoplasmic kinetochore-like particles. Both in cells and in vitro, each molecule of oligomerized CENP-T recruits substantially higher levels of outer kinetochore components than monomeric CENP-T molecules. Our work suggests that the density dependence of CENP-T restricts outer kinetochore recruitment to centromeres, where densely packed CENP-A recruits a high local concentration of inner kinetochore proteins.

Multilocus functional characterization of indigenous and exotic inbreds for dgat1-2, fatb, ge2 and wri1a genes affecting kernel oil and fatty acid profile in maize

Demand for maize oil is progressively increasing due to its diverse industrial applications, aside from its primary role in human nutrition and animal feed. Oil content and composition are two crucial determinants of maize oil in the international market. As kernel oil in maize is a complex quantitative trait, improving this trait presents a challenge for plant breeders and biotechnologists. Here, we characterized a set of 292 diverse maize inbreds of both indigenous and exotic origin by exploiting functional polymorphism of the dgat1-2, fatb, ge2, and wri1a genes governing kernel oil in maize. Genotyping using gene-based functional markers revealed a lower frequencies of dgat1-2 (0.15) and fatb (0.12) mutant alleles and a higher frequencies of wild-type alleles (Dgat1-2: 0.85; fatB: 0.88). The favorable wri1a allele was conserved across genotypes, while its wild-type allele (WRI1a) was not detected. In contrast, none of the genotypes possessed the ge2 favorable allele. The frequency of favorable alleles of both dgat1-2 and fatb decreased to 0.03 when considered together. Furthermore, pairwise protein-protein interactions among target gene products were conducted to understand the effect of one protein on another and their responses to kernel oil through functional enrichments. Thus, the identified maize genotypes with dgat1-2, fatb, and wri1a favourable alleles, along with insights gained through the protein-protein association network, serve as prominent and unique genetic resources for high-oil maize breeding programs. This is the first comprehensive report on the functional characterization of diverse genotypes at the molecular and protein levels.

Interaction and assembly of the DNA replication core proteins of Kaposi's sarcoma-associated herpesvirus.

Kaposi's sarcoma-associated herpesvirus (KSHV) encodes six highly conserved core replication proteins essential for the viral lytic DNA synthesis, including ORF6 (single-stranded DNA-binding protein), ORF9 (DNA polymerase), ORF40/41 (primase-associated factor), ORF44 (helicase), ORF56 (primase), and ORF59 (polymerase processivity factor). Since the protein-protein interactions among KSHV core replication proteins are largely unknown, this study aimed to decipher their interrelationships. Herein, we propose a protein-protein interaction network of these six core replication proteins according to the results obtained from confocal fluorescence microscopy, coimmunoprecipitation, and mammalian two-hybrid (GAL4/VP16) reporter assays. In this interaction network, ORF40/41 plays a central role in the connection of different replication subcomplexes. In addition to the well-conserved helicase-primase subcomplex (consisting of ORF44, ORF56, and ORF40/41) and the replisome subcomplex (consisting of ORF9 and ORF59), our data also suggest that several discrete, stable subcomplexes exist in the cell nucleus. Among these replication subcomplexes in the nucleus, the tetrameric subcomplex composed of ORF44, ORF56, ORF40/41, and ORF6 exhibited the ability to trigger a DNA damage response. By using an established GAL4/VP16-based reporter system and confocal fluorescence microscopy, we additionally found that the heat shock protein 90 (Hsp90) inhibitor, radicicol, significantly inhibited the formation of both the helicase-primase subcomplex and the replisome subcomplex in a dose-dependent manner. Collectively, these data not only provide further insights into the interaction and assembly of KSHV-encoded core replication proteins but also suggest a critical role of Hsp90 in assisting the construction of the viral core replication machinery.IMPORTANCEEukaryotic DNA replication is a highly regulated process that requires multiple replication enzymes assembled onto DNA replication origins. Due to the complexity of the cell's DNA replication machinery, most of what we know about cellular DNA replication has come from the study of viral systems. Herein, we focus our study on the assembly of the Kaposi's sarcoma-associated herpesvirus core replication complex and propose a pairwise protein-protein interaction network of six highly conserved viral core replication proteins. A detailed understanding of the interaction and assembly of the viral core replication proteins may provide opportunities to develop new strategies against viral propagation.

Evidence of an intracellular interaction between the Escherichia coli enzymes EntC and EntB and identification of a potential electrostatic channeling surface

Siderophores are high-affinity small-molecule chelators employed by bacteria to acquire iron from the extracellular environment. The Gram-negative bacterium Escherichia coli synthesizes and secretes enterobactin, a tris-catechol siderophore. Enterobactin is synthesized by six cytoplasmic enzyme activities: EntC, EntB (isochorismatase (IC) domain), EntA, EntE, EntB (aryl carrier protein (ArCP) domain), and EntF. While various pairwise protein-protein interactions have been reported between EntB, EntA, EntE, and EntF, evidence for an interaction between EntC and EntB has remained elusive. We have employed bacterial two-hybrid assays and in vivo crosslinking to demonstrate an intracellular EntC-EntB interaction. A T18-EntC/T25-EntB co-transformant exhibited a positive two-hybrid signal compared to a control T18-EntC/T25 co-transformant. In vivo formaldehyde crosslinking of E. coli cells co-expressing HA-tagged EntB and H6-tagged EntC resulted in an observable ∼80 kDa band on Western blots that cross-reacted with anti-HA and anti-H6, corresponding to one HA-EntB monomer (33 kDa) crosslinked with one H6-EntC monomer (45 kDa). This band disappeared upon sample boiling, confirming it to be a formaldehyde-crosslinked species. Bands of molecular masses greater than 80 kDa that cross-reacted with both antibodies were also observed. Automated docking of the crystal structures of monomeric EntC and dimeric EntB resulted in a top-ranked candidate docked ensemble in which the active sites of EntC and EntB were oriented in apposition and connected by an electropositive surface potentially capable of channeling negatively charged isochorismate. These research outcomes provide the first reported evidence of an EntC-EntB interaction, as well as the first experimental evidence of higher-order complexes containing EntC and EntB.

Comparison of path-based centrality measures in protein-protein interaction networks revealed proteins with phenotypic relevance during adaptation to changing nitrogen environments

Plants must rapidly adapt to changes in nutrient conditions. Especially adaptations to changing nitrogen environments are very complex involving also major adjustments on the protein level. Here, we used a size-exclusion chromatography-coupled to mass spectrometry approach to study the dynamics of protein-protein interactions induced by transition from full nutrition to nitrogen starvation. Comparison of interaction networks established for each nutrient condition revealed a large overlap of proteins which were part of the protein-protein interaction network, but that same set of proteins underwent different interactions at each treatment. Network topology parameter betweenness centrality (BC) was found to best reflect the relevance of individual proteins in the information flow within each network. Changes in BC for individual proteins may therefore indicate their involvement in the cellular adjustments to the new condition. Based on this analysis, a set of proteins was identified showing high nitrogen-dependent changes in their BC values: The receptor kinase AT5G49770, co-receptor QSK1, and proton-ATPase AHA2. Mutants of those proteins showed a nitrate-dependent root growth phenotype. Individual interactions within the reconstructed network were tested using FRET-FLIM technology. Taken together, we present a systematic strategy comparing dynamic changes in protein-protein interaction networks based on their network parameters to identify regulatory nodes. SignificanceProtein-protein interactions are known to be important in cellular signaling events, but the dynamic changes in interaction networks induced by external stimuli are still rarely studied. We systematically analyzed how changes in the nutrient environment induced a rewiring of protein-protein interactions in roots. We observed small changes in overall protein abundances, but instead a rewiring of pairwise protein-protein interactions. Betweenness centrality was found to be the optimal network topology parameter to identify protein candidates with high relevance to the information flow in the (dynamic) network. Predicted interactions of those relevant nodes were confirmed in FLIM/FRET experiments and in phenotypic analysis. The network approach described here may be a useful application in dynamic network analysis more generally.

Simplified geometric representations of protein structures identify complementary interaction interfaces.

Protein‐protein interactions are critical to protein function, but three‐dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large‐scale prediction and assembly of 3D structures of multi‐protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a reduced representation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and evolutionary rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner‐specific potential surface complementarity. On two available benchmarks of 185 overall known protein complexes, we observe predictions comparable to other structure‐based tools at correctly identifying protein interaction surfaces. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor‐ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å Cα‐RMSD. Thus, a shape reduction of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

A large accessory protein interactome is rewired across environments.

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Mapping Interactions among Cell-Free Expressed Zika Virus Proteins.

The rapid spread of arthropod-borne Zika virus poses a serious public health threat that calls for effective ways of controlling and treating viral infection. This in turn necessitates better understanding of the mechanisms of virus assembly and its interaction with the host cells. In order to facilitate such efforts, we developed a new multihost expression vector pmCellFree that allows rapid and multiplexed production of ZIKV proteins in any in vitro translation system as well as in mammalian cells. Using a combination of in vitro expression in Leishmania cell-free system and AlphaLISA interaction assay, pairwise protein interactions of all ZIKV proteins were systematically tested. We identified thirty-three intraviral binary protein interactions, of which 13 interactions are novel. These findings were further validated by expressing selected protein pairs in mammalian HEK293T cell line and assessing their interactions in the cellular lysate. The results of these interaction assays were identical to those obtained with in vitro expressed proteins. The observed novel protein-protein interactions were further validated using a pulldown assay. The unrevealed novel protein interactions may point to the previously unappreciated complexity of the ZIKV assembly process and may play an important role in the infection process. These interactions may represent new targets for antiviral drug development.

Integration of Heterogeneous Experimental Data Improves Global Map of Human Protein Complexes.

Protein complexes play a significant role in the core functionality of cells. These complexes are typically identified by detecting densely connected subgraphs in protein-protein interaction (PPI) networks. Recently, multiple large-scale mass spectrometry-based experiments have significantly increased the availability of PPI data in order to further expand the set of known complexes. However, high-throughput experimental data generally are incomplete, show limited agreement between experiments, and show frequent false positive interactions. There is a need for computational approaches that can address these limitations in order to improve the coverage and accuracy of human protein complexes. Here, we present a new method that integrates data from multiple heterogeneous experiments and sources in order to increase the reliability and coverage of predicted protein complexes. We first fused the heterogeneous data into a feature matrix and trained classifiers to score pairwise protein interactions. We next used graph based methods to combine pairwise interactions into predicted protein complexes. Our approach improves the accuracy and coverage of protein pairwise interactions, accurately identifies known complexes, and suggests both novel additions to known complexes and entirely new complexes. Our results suggest that integration of heterogeneous experimental data helps improve the reliability and coverage of diverse high-throughput mass-spectrometry experiments, leading to an improved global map of human protein complexes.

Multi-functionality of proteins involved in GPCR and G protein signaling: making sense of structure-function continuum with intrinsic disorder-based proteoforms.

GPCR-G protein signaling system recognizes a multitude of extracellular ligands and triggers a variety of intracellular signaling cascades in response. In humans, this system includes more than 800 various GPCRs and a large set of heterotrimeric G proteins. Complexity of this system goes far beyond a multitude of pair-wise ligand-GPCR and GPCR-G protein interactions. In fact, one GPCR can recognize more than one extracellular signal and interact with more than one G protein. Furthermore, one ligand can activate more than one GPCR, and multiple GPCRs can couple to the same G protein. This defines an intricate multifunctionality of this important signaling system. Here, we show that the multifunctionality of GPCR-G protein system represents an illustrative example of the protein structure-function continuum, where structures of the involved proteins represent a complex mosaic of differently folded regions (foldons, non-foldons, unfoldons, semi-foldons, and inducible foldons). The functionality of resulting highly dynamic conformational ensembles is fine-tuned by various post-translational modifications and alternative splicing, and such ensembles can undergo dramatic changes at interaction with their specific partners. In other words, GPCRs and G proteins exist as sets of conformational/basic, inducible/modified, and functioning proteoforms characterized by a broad spectrum of structural features and possessing various functional potentials.

Disease-causing mutations in subunits of OXPHOS complex I affect certain physical interactions

Mitochondrial complex I (CI) is the largest multi-subunit oxidative phosphorylation (OXPHOS) protein complex. Recent availability of a high-resolution human CI structure, and from two non-human mammals, enabled predicting the impact of mutations on interactions involving each of the 44 CI subunits. However, experimentally assessing the impact of the predicted interactions requires an easy and high-throughput method. Here, we created such a platform by cloning all 37 nuclear DNA (nDNA) and 7 mitochondrial DNA (mtDNA)-encoded human CI subunits into yeast expression vectors to serve as both ‘prey’ and ‘bait’ in the split murine dihydrofolate reductase (mDHFR) protein complementation assay (PCA). We first demonstrated the capacity of this approach and then used it to examine reported pathological OXPHOS CI mutations that occur at subunit interaction interfaces. Our results indicate that a pathological frame-shift mutation in the MT-ND2 gene, causing the replacement of 126 C-terminal residues by a stretch of only 30 amino acids, resulted in loss of specificity in ND2-based interactions involving these residues. Hence, the split mDHFR PCA is a powerful assay for assessing the impact of disease-causing mutations on pairwise protein-protein interactions in the context of a large protein complex, thus offering a possible mechanistic explanation for the underlying pathogenicity.

The interaction networks of the budding yeast and human DNA replication-initiation proteins

ABSTRACTDNA replication is a stringently regulated cellular process. In proliferating cells, DNA replication-initiation proteins (RIPs) are sequentially loaded onto replication origins during the M-to-G1 transition to form the pre-replicative complex (pre-RC), a process known as replication licensing. Subsequently, additional RIPs are recruited to form the pre-initiation complex (pre-IC). RIPs and their regulators ensure that chromosomal DNA is replicated exactly once per cell cycle. Origin recognition complex (ORC) binds to, and marks replication origins throughout the cell cycle and recruits other RIPs including Noc3p, Ipi1-3p, Cdt1p, Cdc6p and Mcm2-7p to form the pre-RC. The detailed mechanisms and regulation of the pre-RC and its exact architecture still remain unclear. In this study, pairwise protein-protein interactions among 23 budding yeast and 16 human RIPs were systematically and comprehensively examined by yeast two-hybrid analysis. This study tested 470 pairs of yeast and 196 pairs of human RIPs, from which 113 and 96 positive interactions, respectively, were identified. While many of these interactions were previously reported, some were novel, including various ORC and MCM subunit interactions, ORC self-interactions, and the interactions of IPI3 and NOC3 with several pre-RC and pre-IC proteins. Ten of the novel interactions were further confirmed by co-immunoprecipitation assays. Furthermore, we identified the conserved interaction networks between the yeast and human RIPs. This study provides a foundation and framework for further understanding the architectures, interactions and functions of the yeast and human pre-RC and pre-IC.

Looking Into the Binary Interactome of Enterobacteriaceae Family of Bacteria

Protein-protein interactions (PPIs) regulate most of the biological activities within a cell. A set of pairwise PPIs in seven genera of bacterial pathogens (Salmonella, Escherichia, Shigella, Yersinia, Klebsiella, Photorhabdus, and Pantoea) of Enterobacteriaceae family was analysed. At genotypic level, the correlation coefficient analysis of the mutation spectra of the ten sets of directly interacting protein partners in Escherichia coli recognised all the ‘interacting partners' in Escherichia coli. Extending the correlation analysis to include strains from the rest of the bacterial genera decreased the recognition efficiency providing quantitative evidence that binary interactome have incomplete superposition across species. At phenotype level, a reliable classification of bacterial pathogens was obtained by measuring PPI variations in terms of between phylogenetic distance correlation distances among ten sets of proteins partners. This forces us to rethink upon the possibility of PPI rewiring with a consequent change in physiological role of the same protein.

Dissecting the Contingent Interactions of Protein Complexes with the Optimized Yeast Cytosine Deaminase Protein-Fragment Complementation Assay.

Here, we present a detailed protocol for studying in yeast cells the contingent interaction between a substrate and its multisubunit enzyme complex by using a death selection technique known as the optimized yeast cytosine deaminase protein-fragment complementation assay (OyCD PCA). In yeast, the enzyme cytosine deaminase (encoded by FCY1) is involved in pyrimidine metabolism. The PCA is based on an engineered form of yeast cytosine deaminase optimized by directed evolution for maximum activity (OyCD), which acts as a reporter converting the pro-drug 5-fluorocytosine (5-FC) to 5-fluorouracil (5-FU), a toxic compound that kills the cell. Cells that have OyCD PCA activity convert 5-FC to 5-FU and die. Using this assay, it is possible to assess how regulatory subunits of an enzyme contribute to the overall interaction between the catalytic subunit and the potential substrates. Furthermore, OyCD PCA can be used to dissect different functions of mutant forms of a protein as a mutant can disrupt interaction with one partner, while retaining interaction with others. As it is scalable to a medium- or high-throughput format, OyCD PCA can be used to study hundreds to thousands of pairwise protein-protein interactions in different deletion strains. In addition, OyCD PCA vectors (pAG413GAL1-ccdB-OyCD-F[1] and pAG415GAL1-ccdB-OyCD-F[2]) have been designed to be compatible with the proprietary Gateway technology. It is therefore easy to generate fusion genes with the OyCD reporter fragments. As an example, we will focus on the yeast cyclin-dependent protein kinase 1 (Cdk1, encoded by CDC28), its regulatory cyclin subunits, and its substrates or binding partners.

Bacterial Interactomes: Interacting Protein Partners Share Similar Function and Are Validated in Independent Assays More Frequently Than Previously Reported

Numerous affinity purification-mass spectrometry (AP-MS) and yeast two-hybrid screens have each defined thousands of pairwise protein-protein interactions (PPIs), most of which are between functionally unrelated proteins. The accuracy of these networks, however, is under debate. Here, we present an AP-MS survey of the bacterium Desulfovibrio vulgaris together with a critical reanalysis of nine published bacterial yeast two-hybrid and AP-MS screens. We have identified 459 high confidence PPIs from D. vulgaris and 391 from Escherichia coli Compared with the nine published interactomes, our two networks are smaller, are much less highly connected, and have significantly lower false discovery rates. In addition, our interactomes are much more enriched in protein pairs that are encoded in the same operon, have similar functions, and are reproducibly detected in other physical interaction assays than the pairs reported in prior studies. Our work establishes more stringent benchmarks for the properties of protein interactomes and suggests that bona fide PPIs much more frequently involve protein partners that are annotated with similar functions or that can be validated in independent assays than earlier studies suggested.

Optimizing recombinant antibodies for intracellular function using hitchhiker-mediated survival selection

The 'hitchhiker' mechanism of the bacterial twin-arginine translocation pathway has previously been adapted as a genetic selection for detecting pairwise protein interactions in the cytoplasm of living Escherichia coli cells. Here, we extended this method, called FLI-TRAP, for rapid isolation of intracellular antibodies (intrabodies) in the single-chain Fv format that possess superior traits simply by demanding bacterial growth on high concentrations of antibiotic. Following just a single round of survival-based enrichment using FLI-TRAP, variants of an intrabody against the dimerization domain of the yeast Gcn4p transcription factor were isolated having significantly greater intracellular stability that translated to yield enhancements of >10-fold. Likewise, an intrabody specific for the non-amyloid component region of α-synuclein was isolated that has ~8-fold improved antigen-binding affinity. Collectively, our results illustrate the potential of the FLI-TRAP method for intracellular stabilization and affinity maturation of intrabodies, all without the need for purification or immobilization of the antigen.

The potato amylase inhibitor gene SbAI regulates cold-induced sweetening in potato tubers by modulating amylase activity.

Potato cold-induced sweetening (CIS) is critical for the postharvest quality of potato tubers. Starch degradation is considered to be one of the key pathways in the CIS process. However, the functions of the genes that encode enzymes related to starch degradation in CIS and the activity regulation of these enzymes have received less attention. A potato amylase inhibitor gene known as SbAI was cloned from the wild potato species Solanum berthaultii. This genetic transformation confirmed that in contrast to the SbAI suppression in CIS-resistant potatoes, overexpressing SbAI in CIS-sensitive potatoes resulted in less amylase activity and a lower rate of starch degradation accompanied by a lower reducing sugar (RS) content in cold-stored tubers. This finding suggested that the SbAI gene may play crucial roles in potato CIS by modulating the amylase activity. Further investigations indicated that pairwise protein-protein interactions occurred between SbAI and α-amylase StAmy23, β-amylases StBAM1 and StBAM9. SbAI could inhibit the activities of both α-amylase and β-amylase in potato tubers primarily by repressing StAmy23 and StBAM1, respectively. These findings provide the first evidence that SbAI is a key regulator of the amylases that confer starch degradation and RS accumulation in cold-stored potato tubers.

Interaction specificity and coexpression of rice NPR1 homologs 1 and 3 (NH1 and NH3), TGA transcription factors and Negative Regulator of Resistance (NRR) proteins

BackgroundThe nonexpressor of pathogenesis-related genes 1, NPR1 (also known as NIM1 and SAI1), is a key regulator of SA-mediated systemic acquired resistance (SAR) in Arabidopsis. In rice, the NPR1 homolog 1 (NH1) interacts with TGA transcriptional regulators and the Negative Regulator of Resistance (NRR) protein to modulate the SAR response. Though five NPR1 homologs (NHs) have been identified in rice, only NH1 and NH3 enhance immunity when overexpressed. To understand why NH1 and NH3, but not NH2, NH4, or NH5, contribute to the rice immune response, we screened TGA transcription factors and NRR-like proteins for interactions specific to NH1 and NH3. We also examined their co-expression patterns using publicly available microarray data.ResultsWe tested five NHs, four NRR homologs (RHs), and 13 rice TGA proteins for pair-wise protein interactions using yeast two-hybrid (Y2H) and split YFP assays. A survey of 331 inter-family interactions revealed a broad, complex protein interaction network. To investigate preferred interaction partners when all three families of proteins were present, we performed a bridged split YFP assay employing YFPN-fused TGA, YFPC-fused RH, and NH proteins without YFP fusions. We found 64 tertiary interactions mediated by NH family members among the 120 sets we examined. In the yeast two-hybrid assay, each NH protein was capable of interacting with most TGA and RH proteins. In the split YFP assay, NH1 was the most prevalent interactor of TGA and RH proteins, NH3 ranked the second, and NH4 ranked the third. Based on their interaction with TGA proteins, NH proteins can be divided into two subfamilies: NH1, NH2, and NH3 in one family and NH4 and NH5 in the other.In addition to evidence of overlap in interaction partners, co-expression analyses of microarray data suggest a correlation between NH1 and NH3 expression patterns, supporting their common role in rice immunity. However, NH3 is very tightly co-expressed with RH1 and RH2, while NH1 is strongly, inversely co-expressed with RH proteins, representing a difference between NH1 and NH3 expression patterns.ConclusionsOur genome-wide surveys reveal that each rice NH protein can partner with many rice TGA and RH proteins and that each NH protein prefers specific interaction partners. NH1 and NH3 are capable of interacting strongly with most rice TGA and RH proteins, whereas NH2, NH4, and NH5 have weaker, limited interaction with TGA and RH proteins in rice cells. We have identified rTGA2.1, rTGA2.2, rTGA2.3, rLG2, TGAL2 and TGAL4 proteins as the preferred partners of NH1 and NH3, but not NH2, NH4, or NH5. These TGA proteins may play an important role in NH1- and NH3-mediated immune responses. In contrast, NH4 and NH5 preferentially interact with TGAL5, TGAL7, TGAL8 and TGAL9, which are predicted to be involved in plant development.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-461) contains supplementary material, which is available to authorized users.

Detecting Protein Complexes Based on Uncertain Graph Model.

Advanced biological technologies are producing large-scale protein-protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise protein interactions can be modeled as a graph, where vertices represent proteins and edges represent PPIs. However most of current algorithms detect protein complexes based on deterministic graphs, whose edges are either present or absent. Neighboring information is neglected in these methods. Based on the uncertain graph model, we propose the concept of expected density to assess the density degree of a subgraph, the concept of relative degree to describe the relationship between a protein and a subgraph in a PPI network. We develop an algorithm called DCU (detecting complex based on uncertain graph model) to detect complexes from PPI networks. In our method, the expected density combined with the relative degree is used to determine whether a subgraph represents a complex with high cohesion and low coupling. We apply our method and the existing competing algorithms to two yeast PPI networks. Experimental results indicate that our method performs significantly better than the state-of-the-art methods and the proposed model can provide more insights for future study in PPI networks.