Protein interaction platforms: visualization of interacting proteins in yeast.
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ABSTRACT:
SUBMITTER: Schmitz AM
PROVIDER: S-EPMC2819136 | biostudies-literature | 2009 Jul
REPOSITORIES: biostudies-literature
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Project description:BackgroundEvolutionary rates of proteins in a protein-protein interaction network are primarily governed by the protein connectivity and/or expression level. A recent study revealed the importance of the features of the interacting protein partners, viz., the coefficient of functionality and clustering coefficient in controlling the protein evolutionary rates in a protein-protein interaction (PPI) network.ResultsBy multivariate regression analysis we found that the three parameters: probability of complex formation, expression level and degree of a protein independently guide the evolutionary rates of proteins in the PPI network. The contribution of the complex forming property of a protein and its expression level led to nearly 43% of the total variation as observed from the first principal component. We also found that for complex forming proteins in the network, those which have partners sharing the same functional class evolve faster than those having partners belonging to different functional classes. The proteins in the dense parts of the network evolve faster than their counterparts which are present in the sparse regions of the network. Taking into account the complex forming ability, we found that all the complex forming proteins considered in this study evolve slower than the non-complex forming proteins irrespective of their localization in the network or the affiliation of their partners to same/different functional classes.ConclusionsWe have shown here that the functionality and clustering coefficient correlated with the degree of the protein in the protein-protein interaction network. We have identified the significant relationship of the complex-forming property of proteins and their evolutionary rates even when they are classified according to the features of their interacting partners. Our study implies that the evolutionarily constrained proteins are actually members of a larger number of protein complexes and this justifies why they have enhanced expression levels.
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Project description:Protein aggregation is the phenomenon of protein self-association potentially leading to detrimental effects on physiology, which is closely related to numerous human diseases such as Alzheimer's and Parkinson's disease. Despite progress in understanding the mechanism of protein aggregation, how natural selection against protein aggregation acts on subunits of protein complexes and on proteins with different contributions to organism fitness remains largely unknown. Here, we perform a proteome-wide analysis by using an experimentally validated algorithm TANGO and utilizing sequence, interactomic and phenotype-based functional genomic data from yeast, fly, and nematode. We find that proteins that are capable of forming homooligomeric complex have lower aggregation propensity compared with proteins that do not function as homooligomer. Further, proteins that are essential to the fitness of an organism have lower aggregation propensity compared with nonessential ones. Our finding suggests that the selection force against protein aggregation acts across different hierarchies of biological system.
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Project description:This chapter will discuss various adaptations of the yeast two-hybrid method for analyzing protein interactions that can be used to identify small ubiquitin-related modifier (SUMO) interacting proteins and to determine the nature of the SUMO-protein interactions that occur. SUMO binds to a protein in two different ways: covalently and noncovalently. In a covalent interaction an isopeptide bond forms between the glycine residue at the C terminus of the mature SUMO and a lysine side-chain on the substrate protein. Alternatively, SUMO can interact noncovalently with another protein, usually via insertion of a beta strand from a substrate SUMO-interacting motif (SIM) into a hydrophobic groove next to the SUMO beta2 strand. By mutating either the C-terminal diglycine motif or amino acids within the beta2 strand of SUMO, these respective interactions can be abolished. The expression of the two-hybrid SUMO constructs with either of these mutations can help distinguish the type of interaction that occurs between a SUMO and a given protein. Sumoylation can be verified by independent methods, such as a SUMO mobility shift assay. Finally, the chapter will compare the two-hybrid approach with mass spectrometric analysis as a means of identifying SUMO-interacting proteins.
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Project description:Neuroglobin (Ngb) is a globin protein that is highly and specifically expressed in brain neurons. A large volume of evidence has proven that Ngb is a neuroprotective molecule against hypoxic/ischemic brain injury and other related neurological disorder; however, the underlying mechanisms remain poorly understood. Aiming to provide more clues in understanding the molecular mechanisms of Ngb's neuroprotection, we performed yeast two-hybrid screening to search for proteins that interact with Ngb. From a mouse brain cDNA library, we found totally 36 proteins that potentially interact with Ngb, and 10 of them were each identified in multiple positive clones. The shared sequences within these multiple clones are more likely to be Ngb-interacting domains. In primary cultured mouse cortical neurons, immuno-precipitation was performed to confirm the interactions of selected proteins with Ngb. The discovered Ngb-interacting proteins in this study include those involved in energy metabolism, mitochondria function, and signaling pathways for cell survival and proliferation. Our findings provide molecular targets for investigating protein interaction-based biological functions and neuroprotective mechanisms of Ngb.
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Project description:This unit presents protocols designed to detect interacting proteins. Using yeast as a "test tube" and transcriptional activation of a reporter system, interacting proteins can be identified. The system can also be used to test complex formation for proteins for which there exists a reason to expect interaction.
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Project description:BackgroundProtein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners).MethodologyOur approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.ConclusionsOur method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.AvailabilityWe provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.
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Project description:Cellular signaling is primarily directed via protein-protein interactions. PDZ (PSD-95/Discs large/ZO-1 homologous) domains are well known protein-protein interaction modules involved in various key signaling pathways. Human Tax-interacting protein 1 (TIP-1), also known as glutaminase interaction protein (GIP), is a Class I PDZ domain protein that recognizes the consensus binding motif X-S/T-X-V/I/L-COOH of the C-terminus of its target proteins. We recently reported that TIP-1 not only interacts via the C-terminus of its target partner proteins but also recognizes an internal motif defined by the consensus sequence S/T-X-V/L-D in the target protein. Identification of new target partners containing either a C-terminal or internal recognition motif has rapidly expanded the TIP-1 protein interaction network. TIP-1 being composed solely of a single PDZ domain is unique among PDZ containing proteins. Since it is involved in many important signaling pathways, it is a possible target for drug design. In this mini review, we have discussed human TIP-1, its structure, mechanism of function, its interactions with target proteins containing different recognition motifs, and its involvement in human diseases. Understanding the molecular mechanisms of TIP-1 interactions with distinct target partners and their role in human diseases will be useful for designing novel therapeutics.
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Project description:The cAMP-PKA pathway consists of an extracellular ligand-sensitive G protein-coupled receptor, a G protein signal transmitter, and the effector, adenylate cyclase, of which the product, cAMP, acts as an intracellular second messenger. cAMP activates PKA by dissociating the regulatory subunit from the catalytic subunit. Yeast cells (Saccharomyces cerevisiae) contain a glucose/sucrose-sensitive seven-transmembrane domain receptor, Gpr1, that was proposed to activate adenylate cyclase through the G(alpha) protein Gpa2. Consistently, we show here that adenylate cyclase binds only to active, GTP-bound Gpa2. Two related kelch-repeat proteins, Krh1/Gpb2 and Krh2/Gpb1, are associated with Gpa2 and were suggested to act as G(beta) mimics for Gpa2, based on their predicted seven-bladed beta-propeller structure. However, we find that although Krh1 associates with both GDP and GTP-bound Gpa2, it displays a preference for GTP-Gpa2. The strong down-regulation of PKA targets by Krh1 and Krh2 does not require Gpa2 but is strictly dependent on both the catalytic and the regulatory subunits of PKA. Krh1 directly interacts with PKA by means of the catalytic subunits, and Krh1/2 stimulate the association between the catalytic and regulatory subunits in vivo. Indeed, both a constitutively active GPA2 allele and deletion of KRH1/2 lower the cAMP requirement of PKA for growth. We propose that active Gpa2 relieves the inhibition imposed by the kelch-repeat proteins on PKA, thereby bypassing adenylate cyclase for direct regulation of PKA. Importantly, we show that Krh1/2 also enhance the association between mouse R and C subunits, suggesting that Krh control of PKA has been evolutionarily conserved.
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Project description:UnlabelledDemonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies.ImportanceThe exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy.
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Project description:We posit the likely architecture of complex diseases is that subgroups of patients share variants in genes in specific networks which are sufficient to give rise to a shared phenotype. We developed Proteinarium, a multi-sample protein-protein interaction (PPI) tool, to identify clusters of patients with shared gene networks. Proteinarium converts user defined seed genes to protein symbols and maps them onto the STRING interactome. A PPI network is built for each sample using Dijkstra's algorithm. Pairwise similarity scores are calculated to compare the networks and cluster the samples. A layered graph of PPI networks for the samples in any cluster can be visualized. To test this newly developed analysis pipeline, we reanalyzed publicly available data sets, from which modest outcomes had previously been achieved. We found significant clusters of patients with unique genes which enhanced the findings in the original study.
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