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Sample records for protein network coupled

  1. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions.

    PubMed

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G

    2016-08-04

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease.

  2. Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins.

    PubMed

    Soltan Ghoraie, Laleh; Burkowski, Forbes; Zhu, Mu

    2015-03-01

    Recent studies have highlighted the role of coupled side-chain fluctuations alone in the allosteric behavior of proteins. Moreover, examination of X-ray crystallography data has recently revealed new information about the prevalence of alternate side-chain conformations (conformational polymorphism), and attempts have been made to uncover the hidden alternate conformations from X-ray data. Hence, new computational approaches are required that consider the polymorphic nature of the side chains, and incorporate the effects of this phenomenon in the study of information transmission and functional interactions of residues in a molecule. These studies can provide a more accurate understanding of the allosteric behavior. In this article, we first present a novel approach to generate an ensemble of conformations and an efficient computational method to extract direct couplings of side chains in allosteric proteins, and provide sparse network representations of the couplings. We take the side-chain conformational polymorphism into account, and show that by studying the intrinsic dynamics of an inactive structure, we are able to construct a network of functionally crucial residues. Second, we show that the proposed method is capable of providing a magnified view of the coupled and conformationally polymorphic residues. This model reveals couplings between the alternate conformations of a coupled residue pair. To the best of our knowledge, this is the first computational method for extracting networks of side chains' alternate conformations. Such networks help in providing a detailed image of side-chain dynamics in functionally important and conformationally polymorphic sites, such as binding and/or allosteric sites.

  3. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions.

    PubMed

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  4. Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

    PubMed Central

    Di Roberto, Raphaël B.; Chang, Belinda; Trusina, Ala; Peisajovich, Sergio G.

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease. PMID:27487915

  5. Buried ionizable networks are an ancient hallmark of G protein-coupled receptor activation

    PubMed Central

    Isom, Daniel G.; Dohlman, Henrik G.

    2015-01-01

    Seven-transmembrane receptors (7TMRs) have evolved in prokaryotes and eukaryotes over hundreds of millions of years. Comparative structural analysis suggests that these receptors may share a remote evolutionary origin, despite their lack of sequence similarity. Here we used structure-based computations to compare 221 7TMRs from all domains of life. Unexpectedly, we discovered that these receptors contain spatially conserved networks of buried ionizable groups. In microbial 7TMRs these networks are used to pump ions across the cell membrane in response to light. In animal 7TMRs, which include light- and ligand-activated G protein-coupled receptors (GPCRs), homologous networks were found to be characteristic of activated receptor conformations. These networks are likely relevant to receptor function because they connect the ligand-binding pocket of the receptor to the nucleotide-binding pocket of the G protein. We propose that agonist and G protein binding facilitate the formation of these electrostatic networks and promote important structural rearrangements such as the displacement of transmembrane helix-6. We anticipate that robust classification of activated GPCR structures will aid the identification of ligands that target activated GPCR structural states. PMID:25902551

  6. The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components.

    PubMed

    Borroto-Escuela, Dasiel O; Brito, Ismel; Romero-Fernandez, Wilber; Di Palma, Michael; Oflijan, Julia; Skieterska, Kamila; Duchou, Jolien; Van Craenenbroeck, Kathleen; Suárez-Boomgaard, Diana; Rivera, Alicia; Guidolin, Diego; Agnati, Luigi F; Fuxe, Kjell

    2014-05-14

    G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/~ismel/GPCR-Nets/index.html.

  7. The G protein-coupled receptor heterodimer network (GPCR-HetNet) and its hub components.

    PubMed

    Borroto-Escuela, Dasiel O; Brito, Ismel; Romero-Fernandez, Wilber; Di Palma, Michael; Oflijan, Julia; Skieterska, Kamila; Duchou, Jolien; Van Craenenbroeck, Kathleen; Suárez-Boomgaard, Diana; Rivera, Alicia; Guidolin, Diego; Agnati, Luigi F; Fuxe, Kjell

    2014-01-01

    G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/~ismel/GPCR-Nets/index.html. PMID:24830558

  8. G-protein-coupled receptor signaling and the EGF network in endocrine systems.

    PubMed

    Hsieh, Minnie; Conti, Marco

    2005-09-01

    The epidermal growth factor (EGF) network is composed of a complex array of growth factors synthesized as precursors and expressed on the cell surface. These latent growth factors are activated by cleavage and shedding from the cell surface and act by binding to various homo- and hetero-dimers of the EGF receptors (ErbBs). Although the exact molecular steps are poorly understood, ligand binding to G-protein-coupled receptors as diverse as the beta-adrenoceptors or the lysophosphatidic acid receptors leads to shedding of EGF growth factors and activation of EGF receptors. Recent observations from the pituitary and in the ovary are providing new insight into the role of this network in endocrine systems.

  9. Coupled biopolymer networks

    NASA Astrophysics Data System (ADS)

    Schwarz, J. M.; Zhang, Tao

    2015-03-01

    The actin cytoskeleton provides the cell with structural integrity and allows it to change shape to crawl along a surface, for example. The actin cytoskeleton can be modeled as a semiflexible biopolymer network that modifies its morphology in response to both external and internal stimuli. Just inside the inner nuclear membrane of a cell exists a network of filamentous lamin that presumably protects the heart of the cell nucleus--the DNA. Lamins are intermediate filaments that can also be modeled as semiflexible biopolymers. It turns out that the actin cytoskeletal biopolymer network and the lamin biopolymer network are coupled via a sequence of proteins that bridge the outer and inner nuclear membranes. We, therefore, probe the consequences of such a coupling via numerical simulations to understand the resulting deformations in the lamin network in response to perturbations in the cytoskeletal network. Such study could have implications for mechanical mechanisms of the regulation of transcription, since DNA--yet another semiflexible polymer--contains lamin-binding domains, and, thus, widen the field of epigenetics.

  10. Coupled adaptive complex networks.

    PubMed

    Shai, S; Dobson, S

    2013-04-01

    Adaptive networks, which combine topological evolution of the network with dynamics on the network, are ubiquitous across disciplines. Examples include technical distribution networks such as road networks and the internet, natural and biological networks, and social science networks. These networks often interact with or depend upon other networks, resulting in coupled adaptive networks. In this paper we study susceptible-infected-susceptible (SIS) epidemic dynamics on coupled adaptive networks, where susceptible nodes are able to avoid contact with infected nodes by rewiring their intranetwork connections. However, infected nodes can pass the disease through internetwork connections, which do not change with time: The dependencies between the coupled networks remain constant. We develop an analytical formalism for these systems and validate it using extensive numerical simulation. We find that stability is increased by increasing the number of internetwork links, in the sense that the range of parameters over which both endemic and healthy states coexist (both states are reachable depending on the initial conditions) becomes smaller. Finally, we find a new stable state that does not appear in the case of a single adaptive network but only in the case of weakly coupled networks, in which the infection is endemic in one network but neither becomes endemic nor dies out in the other. Instead, it persists only at the nodes that are coupled to nodes in the other network through internetwork links. We speculate on the implications of these findings. PMID:23679478

  11. Coupled adaptive complex networks

    NASA Astrophysics Data System (ADS)

    Shai, S.; Dobson, S.

    2013-04-01

    Adaptive networks, which combine topological evolution of the network with dynamics on the network, are ubiquitous across disciplines. Examples include technical distribution networks such as road networks and the internet, natural and biological networks, and social science networks. These networks often interact with or depend upon other networks, resulting in coupled adaptive networks. In this paper we study susceptible-infected-susceptible (SIS) epidemic dynamics on coupled adaptive networks, where susceptible nodes are able to avoid contact with infected nodes by rewiring their intranetwork connections. However, infected nodes can pass the disease through internetwork connections, which do not change with time: The dependencies between the coupled networks remain constant. We develop an analytical formalism for these systems and validate it using extensive numerical simulation. We find that stability is increased by increasing the number of internetwork links, in the sense that the range of parameters over which both endemic and healthy states coexist (both states are reachable depending on the initial conditions) becomes smaller. Finally, we find a new stable state that does not appear in the case of a single adaptive network but only in the case of weakly coupled networks, in which the infection is endemic in one network but neither becomes endemic nor dies out in the other. Instead, it persists only at the nodes that are coupled to nodes in the other network through internetwork links. We speculate on the implications of these findings.

  12. G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

    PubMed Central

    Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

    2016-01-01

    The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

  13. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

    PubMed

    Agerskov, Claus; Mortensen, Rasmus M; Bohr, Henrik G

    2015-01-01

    A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors µ-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drugdesign. This is done by training on structural features, selected using a "minimum redundancy, maximum relevance"-test, and testing for successful prediction of categorized binding strength. An extensive comparison of the neural network performances was made in order to select the optimal architecture. Deep belief networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew`s coefficient of 0.925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical characteristics could give the lowest observed IC50 values, meaning largest bio-effect pr. nM substance, around 0.03-0.06 nM. These ligand characteristics could be total number of atoms, their types etc. In conclusion, deep belief networks trained on drug-molecule structures were demonstrated as powerful computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques.

  14. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

    PubMed

    Agerskov, Claus; Mortensen, Rasmus M; Bohr, Henrik G

    2015-01-01

    A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors µ-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drugdesign. This is done by training on structural features, selected using a "minimum redundancy, maximum relevance"-test, and testing for successful prediction of categorized binding strength. An extensive comparison of the neural network performances was made in order to select the optimal architecture. Deep belief networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew`s coefficient of 0.925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical characteristics could give the lowest observed IC50 values, meaning largest bio-effect pr. nM substance, around 0.03-0.06 nM. These ligand characteristics could be total number of atoms, their types etc. In conclusion, deep belief networks trained on drug-molecule structures were demonstrated as powerful computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques. PMID:26463104

  15. The polarity protein Par6 is coupled to the microtubule network during molluscan early embryogenesis

    SciTech Connect

    Homma, Taihei; Shimizu, Miho; Kuroda, Reiko

    2011-01-07

    Research highlights: {yields} The cDNAs encoding Par6 and aPKC homologues were cloned from the snail Lymnaea stagnalis. {yields} L. stagnalis Par6 directly interacts with tubulin and microtubules and localizes to the microtubule cytoskeleton during the early embryogenesis. {yields} Identical sequence and localization of LsPar6 for the dextral and the sinistral snails exclude the possibility of the gene being the primary determinant of body handedness. -- Abstract: Cell polarity, which directs the orientation of asymmetric cell division and segregation of fate determinants, is a fundamental feature of development and differentiation. Regulators of polarity have been extensively studied, and the critical importance of the Par (partitioning-defective) complex as the polarity machinery is now recognized in a wide range of eukaryotic systems. The Par polarity module is evolutionarily conserved, but its mechanism and cooperating factors vary among different systems. Here we describe the cloning and characterization of a pond snail Lymnaea stagnalis homologue of partitioning-defective 6 (Lspar6). The protein product LsPar6 shows high affinity for microtubules and localizes to the mitotic apparatus during embryonic cell division. In vitro assays revealed direct binding of LsPar6 to tubulin and microtubules, which is the first evidence of the direct interaction between the two proteins. The interaction is mediated by two distinct regions of LsPar6 both located in the N-terminal half. Atypical PKC, a functional partner of Par6, was also found to localize to the mitotic spindle. These results suggest that the L. stagnalis Par complex employs the microtubule network in cell polarity processes during the early embryogenesis. Identical sequence and localization of LsPar6 for the dextral and the sinistral snails exclude the possibility of the gene being the primary determinant of handedness.

  16. Coupled transport protein systems.

    PubMed

    Thatcher, Jack D

    2013-04-16

    This set of animated lessons provides examples of how transport proteins interact in coupled systems to produce physiologic effects. The gastric pumps animation depicts the secretion of hydrochloric acid into the gastric lumen. The animation called glucose absorption depicts glucose absorption by intestinal epithelial cells. The CFTR animation explains how the cystic fibrosis conductance transmembrane regulator (CFTR) functions as a key component of a coupled system of transport proteins that clears the pulmonary system of mucus and inhaled particulates. These animations serve as valuable resources for any collegiate-level course that describes these processes. Courses that might use them include introductory biology, biochemistry, biophysics, cell biology, pharmacology, and physiology.

  17. Local coupled feedforward neural network.

    PubMed

    Sun, Jianye

    2010-01-01

    In this paper, the local coupled feedforward neural network is presented. Its connection structure is same as that of Multilayer Perceptron with one hidden layer. In the local coupled feedforward neural network, each hidden node is assigned an address in an input space, and each input activates only the hidden nodes near it. For each input, only the activated hidden nodes take part in forward and backward propagation processes. Theoretical analysis and simulation results show that this neural network owns the "universal approximation" property and can solve the learning problem of feedforward neural networks. In addition, its characteristic of local coupling makes knowledge accumulation possible.

  18. Controlling allosteric networks in proteins

    NASA Astrophysics Data System (ADS)

    Dokholyan, Nikolay

    2013-03-01

    We present a novel methodology based on graph theory and discrete molecular dynamics simulations for delineating allosteric pathways in proteins. We use this methodology to uncover the structural mechanisms responsible for coupling of distal sites on proteins and utilize it for allosteric modulation of proteins. We will present examples where inference of allosteric networks and its rewiring allows us to ``rescue'' cystic fibrosis transmembrane conductance regulator (CFTR), a protein associated with fatal genetic disease cystic fibrosis. We also use our methodology to control protein function allosterically. We design a novel protein domain that can be inserted into identified allosteric site of target protein. Using a drug that binds to our domain, we alter the function of the target protein. We successfully tested this methodology in vitro, in living cells and in zebrafish. We further demonstrate transferability of our allosteric modulation methodology to other systems and extend it to become ligh-activatable.

  19. Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions.

    PubMed

    Baltoumas, Fotis A; Theodoropoulou, Margarita C; Hamodrakas, Stavros J

    2016-06-01

    A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

  20. Coupled map networks as communication schemes

    NASA Astrophysics Data System (ADS)

    García, P.; Parravano, A.; Cosenza, M. G.; Jiménez, J.; Marcano, A.

    2002-04-01

    Networks of chaotic coupled maps are considered as string and language generators. It is shown that such networks can be used as encrypting systems where the cipher text contains information about the evolution of the network and also about the way to select the plain text symbols from the string associated with the network evolution. The secret key provides the network parameters, such as the coupling strengths.

  1. Support Networks of Dual Career Couples.

    ERIC Educational Resources Information Center

    Lloyd, Sally A.; And Others

    Although social networks play an important role in supporting families under stress, there is some evidence that families living a stressful dual career life style may have limited network resources. To describe support networks of dual career couples and to examine the relationship between the supportiveness of the network and satisfaction with…

  2. Chimeras in networks with purely local coupling

    NASA Astrophysics Data System (ADS)

    Laing, Carlo R.

    2015-11-01

    Chimera states in spatially extended networks of oscillators have some oscillators synchronized while the remainder are asynchronous. These states have primarily been studied in networks with nonlocal coupling, and more recently in networks with global coupling. Here, we present three networks with only local coupling (diffusive, to nearest neighbors) which are numerically found to support chimera states. One of the networks is analyzed using a self-consistency argument in the continuum limit, and this is used to find the boundaries of existence of a chimera state in parameter space.

  3. Chimeras in networks with purely local coupling.

    PubMed

    Laing, Carlo R

    2015-11-01

    Chimera states in spatially extended networks of oscillators have some oscillators synchronized while the remainder are asynchronous. These states have primarily been studied in networks with nonlocal coupling, and more recently in networks with global coupling. Here, we present three networks with only local coupling (diffusive, to nearest neighbors) which are numerically found to support chimera states. One of the networks is analyzed using a self-consistency argument in the continuum limit, and this is used to find the boundaries of existence of a chimera state in parameter space.

  4. A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors

    PubMed Central

    2012-01-01

    Background G protein coupled receptors (GPCRs) are seven helical transmembrane proteins that function as signal transducers. They bind ligands in their extracellular and transmembrane regions and activate cognate G proteins at their intracellular surface at the other side of the membrane. The relay of allosteric communication between the ligand binding site and the distant G protein binding site is poorly understood. In this study, GREMLIN [1], a recently developed method that identifies networks of co-evolving residues from multiple sequence alignments, was used to identify those that may be involved in communicating the activation signal across the membrane. The GREMLIN-predicted long-range interactions between amino acids were analyzed with respect to the seven GPCR structures that have been crystallized at the time this study was undertaken. Results GREMLIN significantly enriches the edges containing residues that are part of the ligand binding pocket, when compared to a control distribution of edges drawn from a random graph. An analysis of these edges reveals a minimal GPCR binding pocket containing four residues (T1183.33, M2075.42, Y2686.51 and A2927.39). Additionally, of the ten residues predicted to have the most long-range interactions (A1173.32, A2726.55, E1133.28, H2115.46, S186EC2, A2927.39, E1223.37, G902.57, G1143.29 and M2075.42), nine are part of the ligand binding pocket. Conclusions We demonstrate the use of GREMLIN to reveal a network of statistically correlated and functionally important residues in class A GPCRs. GREMLIN identified that ligand binding pocket residues are extensively correlated with distal residues. An analysis of the GREMLIN edges across multiple structures suggests that there may be a minimal binding pocket common to the seven known GPCRs. Further, the activation of rhodopsin involves these long-range interactions between extracellular and intracellular domain residues mediated by the retinal domain. PMID:22748306

  5. Information Filtering on Coupled Social Networks

    PubMed Central

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  6. Novel effects of FTY720 on perinuclear reorganization of keratin network induced by sphingosylphosphorylcholine: Involvement of protein phosphatase 2A and G-protein-coupled receptor-12.

    PubMed

    Park, Mi Kyung; Park, Soyeun; Kim, Hyun Ji; Kim, Eun Ji; Kim, So Yeon; Kang, Gyeoung Jin; Byun, Hyun Jung; Kim, Sang Hee; Lee, Ho; Lee, Chang Hoon

    2016-03-15

    Sphingosylphosphorylcholine (SPC) evokes perinuclear reorganization of keratin 8 (K8) filaments and regulates the viscoelasticity of metastatic cancer cells leading to enhanced migration. Few studies have addressed the compounds modulating the viscoelasticity of metastatic cancer cells. We studied the effects of sphingosine (SPH), sphingosine 1-phosphate (S1P), FTY720 and FTY720-phosphate (FTY720P) on SPC-induced K8 phosphorylation and reorganization using Western blot and confocal microscopy, and also evaluated the elasticity of PANC-1 cells by atomic force microscopy. FTY720, FTY720P, SPH, and S1P concentration-dependently inhibited SPC-evoked phosphorylation and reorganization of K8, and migration of PANC-1 cells. SPC triggered reduction and narrow distribution of elastic constant K and conversely, FTY720 blocked them. A common upstream regulator of JNK and ERK, protein phosphatase 2A (PP2A) expression was reduced by SPC, but was restored by FTY720 and FTY72P. Butyryl forskolin, a PP2A activator, suppressed SPC-induced K8 phosphorylation and okadaic acid, a PP2A inhibitor, induced K8 phosphorylation. Gene silencing of PP2A also led to K8 phosphorylation, reorganization and migration. We also investigated the involvement of GPR12, a high-affinity SPC receptor, in SPC-evoked keratin phosphorylation and reorganization. GPR12 siRNA suppressed the SPC-triggered phosphorylation and reorganization of K8. GPR12 overexpression stimulated keratin phosphorylation and reorganization even without SPC. FTY720 and FTY720P suppressed the GPR12-induced phosphorylation and reorganization of K8. The collective data indicates that FTY720 and FTY720P suppress SPC-induced phosphorylation and reorganization of K8 in PANC-1 cells by restoring the expression of PP2A via GPR12. These findings might be helpful in the development of compounds that modulate the viscoelasticity of metastatic cancer cells and various SPC actions. PMID:26872988

  7. The protein folding network

    NASA Astrophysics Data System (ADS)

    Rao, Francesco; Caflisch, Amedeo

    2004-03-01

    Networks are everywhere. The conformation space of a 20-residue antiparallel beta-sheet peptide [1], sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. As previously found for the World-Wide Web as well as for social and biological networks , the conformation space contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the network shows a hierarchical modularity [2] which is consistent with the funnel mechanism of folding [3] and is not observed for a random heteropolymer lacking a native state. Here we show that the conformation space network describes the free energy landscape without requiring projections into arbitrarily chosen reaction coordinates. The network analysis provides a basis for understanding the heterogeneity of the folding transition state and the existence of multiple pathways. [1] P. Ferrara and A. Caflisch, Folding simulations of a three-stranded antiparallel beta-sheet peptide, PNAS 97, 10780-10785 (2000). [2] Ravasz, E. and Barabási, A. L. Hierarchical organization in complex networks. Phys. Rev. E 67, 026112 (2003). [3] Dill, K. and Chan, H From Levinthal to pathways to funnels. Nature Struct. Biol. 4, 10-19 (1997)

  8. Enhancing robustness of coupled networks under targeted recoveries

    PubMed Central

    Gong, Maoguo; Ma, Lijia; Cai, Qing; Jiao, Licheng

    2015-01-01

    Coupled networks are extremely fragile because a node failure of a network would trigger a cascade of failures on the entire system. Existing studies mainly focused on the cascading failures and the robustness of coupled networks when the networks suffer from attacks. In reality, it is necessary to recover the damaged networks, and there are cascading failures in recovery processes. In this study, firstly, we analyze the cascading failures of coupled networks during recoveries. Then, a recovery robustness index is presented for evaluating the resilience of coupled networks to cascading failures in the recovery processes. Finally, we propose a technique aiming at protecting several influential nodes for enhancing robustness of coupled networks under the recoveries, and adopt six strategies based on the potential knowledge of network centrality to find the influential nodes. Experiments on three coupling networks demonstrate that with a small number of influential nodes protected, the robustness of coupled networks under the recoveries can be greatly enhanced. PMID:25675980

  9. A Network of Mitogen-Activated Protein Kinases Links G Protein-Coupled Receptors to the c-jun Promoter: a Role for c-Jun NH2-Terminal Kinase, p38s, and Extracellular Signal-Regulated Kinase 5

    PubMed Central

    Marinissen, Maria Julia; Chiariello, Mario; Pallante, Michael; Gutkind, J. Silvio

    1999-01-01

    The expression of the c-jun proto-oncogene is rapidly induced in response to mitogens acting on a large variety of cell surface receptors. The resulting functional activity of c-Jun proteins appears to be critical for cell proliferation. Recently, we have shown that a large family of G protein-coupled receptors (GPCRs), represented by the m1 muscarinic receptor, can initiate intracellular signaling cascades that result in the activation of mitogen-activated protein kinases (MAPK) and c-Jun NH2-terminal kinases (JNK) and that the activation of JNK but not of MAPK correlated with a remarkable increase in the expression of c-jun mRNA. Subsequently, however, we obtained evidence that GPCRs can potently stimulate the activity of the c-jun promoter through MEF2 transcription factors, which do not act downstream from JNK. In view of these observations, we set out to investigate further the nature of the signaling pathway linking GPCRs to the c-jun promoter. Utilizing NIH 3T3 cells, we found that GPCRs can activate the c-jun promoter in a JNK-independent manner. Additionally, we demonstrated that these GPCRs can elevate the activity of novel members of the MAPK family, including ERK5, p38α, p38γ, and p38δ, and that the activation of certain kinases acting downstream from MEK5 (ERK5) and MKK6 (p38α and p38γ) is necessary to fully activate the c-jun promoter. Moreover, in addition to JNK, ERK5, p38α, and p38γ were found to stimulate the c-jun promoter by acting on distinct responsive elements. Taken together, these results suggest that the pathway linking GPCRs to the c-jun promoter involves the integration of numerous signals transduced by a highly complex network of MAPK, rather than resulting from the stimulation of a single linear protein kinase cascade. Furthermore, our findings suggest that each signaling pathway affects one or more regulatory elements on the c-jun promoter and that the transcriptional response most likely results from the temporal integration

  10. Effect of the biopolymer mixing ratio on the formation of electrostatically coupled whey protein-κ- and ι-carrageenan networks in the presence and absence of oil droplets.

    PubMed

    Lam, Ricky S H; Nickerson, Michael T

    2014-08-27

    The rheological properties of 1.0% (w/w) whey protein isolate (WPI)-κ-/ι-carrageenan (CG) mixtures were investigated during a slow acidification process by glucono-δ-lactone from pH 7.00 to ∼4.20 as a function of biopolymer mixing ratio and in the presence and absence of oil droplets. In all cases, electrostatic coupled biopolymer and emulsion gel networks were formed at pH values corresponding to where attractive interactions between WPI and CG began. Formed WPI-CG complexes were found to be surface active, capable of lowering interfacial tension and forming viscoelastic interfacial films within emulsion-based systems. Both biopolymer and emulsion-based gels increased in strength and elasticity as the CG content increased, regardless of the type of CG present. However, WPI-ι-CG coupled networks were stronger than WPI-κ-CG networks, presumably due to the higher number of sulfate groups attracting the WPI molecules.

  11. Vicinal coupling constants and protein dynamics.

    PubMed

    Hoch, J C; Dobson, C M; Karplus, M

    1985-07-16

    The effects of motional averaging on the analysis of vicinal spin-spin coupling constants derived from proton NMR studies of proteins have been examined. Trajectories obtained from molecular dynamics simulations of bovine pancreatic trypsin inhibitor and of hen egg white lysozyme were used in conjunction with an expression for the dependence of the coupling constant on the intervening dihedral angle to calculate the time-dependent behavior of the coupling constants. Despite large fluctuations, the time-average values of the coupling constants are not far from those computed for the average structure in the cases where fluctuations occur about a single potential well. The calculated differences show a high correlation with the variation in the magnitude of the fluctuations of individual dihedral angles. For the cases where fluctuations involve multiple sites, large differences are found between the time-average values and the average structure values for the coupling constants. Comparison of the simulation results with the experimental trends suggests that side chains with more than one position are more common in proteins than is inferred from X-ray results. It is concluded that for the main chain, motional effects do not introduce significant errors where vicinal coupling constants are used in structure determinations; however, for side chains, the motional average can alter deductions about the structure. Accurately measured coupling constants are shown to provide information concerning the magnitude of dihedral angle fluctuations.

  12. Regulation of heartbeat by G protein-coupled ion channels.

    PubMed

    Brown, A M

    1990-12-01

    The coupling of ion channels to receptors by G proteins is the subject of this American Physiological Society Walter B. Cannon Memorial "Physiology in Perspective" Lecture. This subject is particularly appropriate because it includes a molecular explanation of a homeostatic mechanism involving the autonomic nervous system and the latter subject preoccupied Dr. Cannon during most of his career. With the use of reconstitution methods, we and others have shown that heterotrimeric guanine nucleotide-binding (G) proteins couple receptors to ion channels by both membrane-delimited, direct pathways and cytoplasmic second messenger pathways. Furthermore, one set of receptors may be coupled to as many as three different sets of ion channels to form networks. Dual G protein pathways lead to the prediction of biphasic ion current responses in cell signaling, and this prediction was confirmed. In sinoatrial pacemaker cells, the pacemaking hyperpolarization-activated inward current (If) is directly regulated by the G proteins Gs and Go, and the two can act simultaneously. This could explain the classical observation that vagal inhibition of heart rate is greater during sympathetic stimulation. Because deactivation of the muscarinic response occurs much faster than the G protein alpha-subunit hydrolyzes guanosine 5'-triphosphate, we looked for accessory cellular factors. A surprising result was that the small monomeric ras G protein blocked the muscarinic pathway. The significance of this observation is unknown, but it appears that small and large G proteins may interact in ion channel signaling pathways.

  13. Regulation of heartbeat by G protein-coupled ion channels.

    PubMed

    Brown, A M

    1990-12-01

    The coupling of ion channels to receptors by G proteins is the subject of this American Physiological Society Walter B. Cannon Memorial "Physiology in Perspective" Lecture. This subject is particularly appropriate because it includes a molecular explanation of a homeostatic mechanism involving the autonomic nervous system and the latter subject preoccupied Dr. Cannon during most of his career. With the use of reconstitution methods, we and others have shown that heterotrimeric guanine nucleotide-binding (G) proteins couple receptors to ion channels by both membrane-delimited, direct pathways and cytoplasmic second messenger pathways. Furthermore, one set of receptors may be coupled to as many as three different sets of ion channels to form networks. Dual G protein pathways lead to the prediction of biphasic ion current responses in cell signaling, and this prediction was confirmed. In sinoatrial pacemaker cells, the pacemaking hyperpolarization-activated inward current (If) is directly regulated by the G proteins Gs and Go, and the two can act simultaneously. This could explain the classical observation that vagal inhibition of heart rate is greater during sympathetic stimulation. Because deactivation of the muscarinic response occurs much faster than the G protein alpha-subunit hydrolyzes guanosine 5'-triphosphate, we looked for accessory cellular factors. A surprising result was that the small monomeric ras G protein blocked the muscarinic pathway. The significance of this observation is unknown, but it appears that small and large G proteins may interact in ion channel signaling pathways. PMID:1701981

  14. Interdependencies and Causalities in Coupled Financial Networks

    PubMed Central

    Vodenska, Irena; Aoyama, Hideaki; Fujiwara, Yoshi; Iyetomi, Hiroshi; Arai, Yuta

    2016-01-01

    We explore the foreign exchange and stock market networks for 48 countries from 1999 to 2012 and propose a model, based on complex Hilbert principal component analysis, for extracting significant lead-lag relationships between these markets. The global set of countries, including large and small countries in Europe, the Americas, Asia, and the Middle East, is contrasted with the limited scopes of targets, e.g., G5, G7 or the emerging Asian countries, adopted by previous works. We construct a coupled synchronization network, perform community analysis, and identify formation of four distinct network communities that are relatively stable over time. In addition to investigating the entire period, we divide the time period into into “mild crisis,” (1999–2002), “calm,” (2003–2006) and “severe crisis” (2007–2012) sub-periods and find that the severe crisis period behavior dominates the dynamics in the foreign exchange-equity synchronization network. We observe that in general the foreign exchange market has predictive power for the global stock market performances. In addition, the United States, German and Mexican markets have forecasting power for the performances of other global equity markets. PMID:26977806

  15. Interdependencies and Causalities in Coupled Financial Networks.

    PubMed

    Vodenska, Irena; Aoyama, Hideaki; Fujiwara, Yoshi; Iyetomi, Hiroshi; Arai, Yuta

    2016-01-01

    We explore the foreign exchange and stock market networks for 48 countries from 1999 to 2012 and propose a model, based on complex Hilbert principal component analysis, for extracting significant lead-lag relationships between these markets. The global set of countries, including large and small countries in Europe, the Americas, Asia, and the Middle East, is contrasted with the limited scopes of targets, e.g., G5, G7 or the emerging Asian countries, adopted by previous works. We construct a coupled synchronization network, perform community analysis, and identify formation of four distinct network communities that are relatively stable over time. In addition to investigating the entire period, we divide the time period into into "mild crisis," (1999-2002), "calm," (2003-2006) and "severe crisis" (2007-2012) sub-periods and find that the severe crisis period behavior dominates the dynamics in the foreign exchange-equity synchronization network. We observe that in general the foreign exchange market has predictive power for the global stock market performances. In addition, the United States, German and Mexican markets have forecasting power for the performances of other global equity markets. PMID:26977806

  16. Molecular signatures of G-protein-coupled receptors.

    PubMed

    Venkatakrishnan, A J; Deupi, Xavier; Lebon, Guillaume; Tate, Christopher G; Schertler, Gebhard F; Babu, M Madan

    2013-02-14

    G-protein-coupled receptors (GPCRs) are physiologically important membrane proteins that sense signalling molecules such as hormones and neurotransmitters, and are the targets of several prescribed drugs. Recent exciting developments are providing unprecedented insights into the structure and function of several medically important GPCRs. Here, through a systematic analysis of high-resolution GPCR structures, we uncover a conserved network of non-covalent contacts that defines the GPCR fold. Furthermore, our comparative analysis reveals characteristic features of ligand binding and conformational changes during receptor activation. A holistic understanding that integrates molecular and systems biology of GPCRs holds promise for new therapeutics and personalized medicine. PMID:23407534

  17. Protein Sectors: Statistical Coupling Analysis versus Conservation

    PubMed Central

    Teşileanu, Tiberiu; Colwell, Lucy J.; Leibler, Stanislas

    2015-01-01

    Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed “sectors”. The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation. PMID:25723535

  18. Cascading failures in coupled networks: The critical role of node-coupling strength across networks

    PubMed Central

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao

    2016-01-01

    The robustness of coupled networks against node failure has been of interest in the past several years, while most of the researches have considered a very strong node-coupling method, i.e., once a node fails, its dependency partner in the other network will fail immediately. However, this scenario cannot cover all the dependency situations in real world, and in most cases, some nodes cannot go so far as to fail due to theirs self-sustaining ability in case of the failures of their dependency partners. In this paper, we use the percolation framework to study the robustness of interdependent networks with weak node-coupling strength across networks analytically and numerically, where the node-coupling strength is controlled by an introduced parameter α. If a node fails, each link of its dependency partner will be removed with a probability 1−α. By tuning the fraction of initial preserved nodes p, we find a rich phase diagram in the plane p−α, with a crossover point at which a first-order percolation transition changes to a second-order percolation transition. PMID:27748446

  19. Cascading failures in coupled networks: The critical role of node-coupling strength across networks

    NASA Astrophysics Data System (ADS)

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao

    2016-10-01

    The robustness of coupled networks against node failure has been of interest in the past several years, while most of the researches have considered a very strong node-coupling method, i.e., once a node fails, its dependency partner in the other network will fail immediately. However, this scenario cannot cover all the dependency situations in real world, and in most cases, some nodes cannot go so far as to fail due to theirs self-sustaining ability in case of the failures of their dependency partners. In this paper, we use the percolation framework to study the robustness of interdependent networks with weak node-coupling strength across networks analytically and numerically, where the node-coupling strength is controlled by an introduced parameter α. If a node fails, each link of its dependency partner will be removed with a probability 1‑α. By tuning the fraction of initial preserved nodes p, we find a rich phase diagram in the plane p‑α, with a crossover point at which a first-order percolation transition changes to a second-order percolation transition.

  20. Symmetry-broken states on networks of coupled oscillators

    NASA Astrophysics Data System (ADS)

    Jiang, Xin; Abrams, Daniel M.

    2016-05-01

    When identical oscillators are coupled together in a network, dynamical steady states are often assumed to reflect network symmetries. Here, we show that alternative persistent states may also exist that break the symmetries of the underlying coupling network. We further show that these symmetry-broken coexistent states are analogous to those dubbed "chimera states," which can occur when identical oscillators are coupled to one another in identical ways.

  1. Symmetry-broken states on networks of coupled oscillators.

    PubMed

    Jiang, Xin; Abrams, Daniel M

    2016-05-01

    When identical oscillators are coupled together in a network, dynamical steady states are often assumed to reflect network symmetries. Here, we show that alternative persistent states may also exist that break the symmetries of the underlying coupling network. We further show that these symmetry-broken coexistent states are analogous to those dubbed "chimera states," which can occur when identical oscillators are coupled to one another in identical ways.

  2. Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium

    NASA Astrophysics Data System (ADS)

    Dahmen, David; Bos, Hannah; Helias, Moritz

    2016-07-01

    Randomly coupled Ising spins constitute the classical model of collective phenomena in disordered systems, with applications covering glassy magnetism and frustration, combinatorial optimization, protein folding, stock market dynamics, and social dynamics. The phase diagram of these systems is obtained in the thermodynamic limit by averaging over the quenched randomness of the couplings. However, many applications require the statistics of activity for a single realization of the possibly asymmetric couplings in finite-sized networks. Examples include reconstruction of couplings from the observed dynamics, representation of probability distributions for sampling-based inference, and learning in the central nervous system based on the dynamic and correlation-dependent modification of synaptic connections. The systematic cumulant expansion for kinetic binary (Ising) threshold units with strong, random, and asymmetric couplings presented here goes beyond mean-field theory and is applicable outside thermodynamic equilibrium; a system of approximate nonlinear equations predicts average activities and pairwise covariances in quantitative agreement with full simulations down to hundreds of units. The linearized theory yields an expansion of the correlation and response functions in collective eigenmodes, leads to an efficient algorithm solving the inverse problem, and shows that correlations are invariant under scaling of the interaction strengths.

  3. Erosion of synchronization: Coupling heterogeneity and network structure

    NASA Astrophysics Data System (ADS)

    Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex

    2016-06-01

    We study the dynamics of network-coupled phase oscillators in the presence of coupling frustration. It was recently demonstrated that in heterogeneous network topologies, the presence of coupling frustration causes perfect phase synchronization to become unattainable even in the limit of infinite coupling strength. Here, we consider the important case of heterogeneous coupling functions and extend previous results by deriving analytical predictions for the total erosion of synchronization. Our analytical results are given in terms of basic quantities related to the network structure and coupling frustration. In addition to fully heterogeneous coupling, where each individual interaction is allowed to be distinct, we also consider partially heterogeneous coupling and homogeneous coupling in which the coupling functions are either unique to each oscillator or identical for all network interactions, respectively. We demonstrate the validity of our theory with numerical simulations of multiple network models, and highlight the interesting effects that various coupling choices and network models have on the total erosion of synchronization. Finally, we consider some special network structures with well-known spectral properties, which allows us to derive further analytical results.

  4. Coupling of protein dynamics with the solvent

    NASA Astrophysics Data System (ADS)

    Caliskan, Gokhan; Sauzan, Azzam; Mehtani, Disha; Sokolov, Alexei

    2003-03-01

    Glycerol and trehalose are among the many viscous solvents that are widely used for biostabilization and controlling the dynamics of proteins. It is believed that the suppression of the structural relaxations by high viscosity of solvent is responsible for improved stability in proteins. However, results of [1] and [2] demonstrate stronger suppression of biochemical activity and dynamics of proteins by liquid glycerol than by solid trehalose in a wide temperature range. The authors tried to explain the counterintuitive observations by a possible decoupling of the dynamics of the protein from trehalose. In order to test the validity of this assumption and to investigate the influence of the fast dynamics in proteins, the low frequency Raman scattering spectroscopy technique is used. Both relaxational and vibrational dynamics of glycerol, trehalose, and lysozyme in glycerol and in trehalose are studied in a wide temperature range. Dynamics of lysozyme in glycerol follows the strong temperature dependence of relaxational and vibrational dynamics of the bulk glycerol. On the other hand, the weak temperature dependence of dynamics of lysozyme in trehalose follows exactly the behavior of pure trehalose. This proves that there is a strong dynamic coupling between the protein and the solvents used. Interestingly, stronger relaxations in solid trehalose as compared to liquid glycerol are observed in the GHz region at low temperatures. This could be the reason for the enhanced protein activity observed in trehalose, compared to that in glycerol in this temperature range. Suppression of these fast relaxations should be the key for providing long-term stability to proteins. 1. Sastry, G.M. and N. Agmon, Trehalose prevents myoglobin collapse and preserves its internal mobility. BIOCHEMISTRY, 1997, 36(23): p. 7097-108. 2. Caliskan, G., et al., Influence of solvent on dynamics and stability of a protein. Journal of Non-Crystalline Solids, 2002, 307-310: p. 887-893.

  5. Pinning impulsive directed coupled delayed dynamical network and its applications

    NASA Astrophysics Data System (ADS)

    Lin, Chunnan; Wu, Quanjun; Xiang, Lan; Zhou, Jin

    2015-01-01

    The main objective of the present paper is to further investigate pinning synchronisation of a complex delayed dynamical network with directionally coupling by a single impulsive controller. By developing the analysis procedure of pinning impulsive stability for undirected coupled dynamical network previously, some simple yet general criteria of pinning impulsive synchronisation for such directed coupled network are derived analytically. It is shown that a single impulsive controller can always pin a given directed coupled network to a desired homogenous solution, including an equilibrium point, a periodic orbit, or a chaotic orbit. Subsequently, the theoretical results are illustrated by a directed small-world complex network which is a cellular neural network (CNN) and a directed scale-free complex network with the well-known Hodgkin-Huxley neuron oscillators. Numerical simulations are finally given to demonstrate the effectiveness of the proposed control methodology.

  6. Investigation of a protein complex network

    NASA Astrophysics Data System (ADS)

    Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

    2004-09-01

    The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

  7. Signaling through G protein coupled receptors

    PubMed Central

    2009-01-01

    Heterotrimeric G proteins (Gα, Gβ/Gγ subunits) constitute one of the most important components of cell signaling cascade. G Protein Coupled Receptors (GPCRs) perceive many extracellular signals and transduce them to heterotrimeric G proteins, which further transduce these signals intracellular to appropriate downstream effectors and thereby play an important role in various signaling pathways. GPCRs exist as a superfamily of integral membrane protein receptors that contain seven transmembrane α-helical regions, which bind to a wide range of ligands. Upon activation by a ligand, the GPCR undergoes a conformational change and then activate the G proteins by promoting the exchange of GDP/GTP associated with the Gα subunit. This leads to the dissociation of Gβ/Gγ dimer from Gα. Both these moieties then become free to act upon their downstream effectors and thereby initiate unique intracellular signaling responses. After the signal propagation, the GTP of Gα-GTP is hydrolyzed to GDP and Gα becomes inactive (Gα-GDP), which leads to its re-association with the Gβ/Gγ dimer to form the inactive heterotrimeric complex. The GPCR can also transduce the signal through G protein independent pathway. GPCRs also regulate cell cycle progression. Till to date thousands of GPCRs are known from animal kingdom with little homology among them, but only single GPCR has been identified in plant system. The Arabidopsis GPCR was reported to be cell cycle regulated and also involved in ABA and in stress signaling. Here I have described a general mechanism of signal transduction through GPCR/G proteins, structure of GPCRs, family of GPCRs and plant GPCR and its role. PMID:19826234

  8. Transferring network topological knowledge for predicting protein-protein interactions.

    PubMed

    Xu, Qian; Xiang, Evan Wei; Yang, Qiang

    2011-10-01

    Protein-protein interactions (PPIs) play an important role in cellular processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, the existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, we introduce the well-known collective matrix factorization technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish a correspondence between a source network and a target network via network-wide similarities. We test this method on two real PPI networks, Helicobacter pylori (as a target network) and human (as a source network). Our experimental results show that our method can achieve higher performance as compared with some baseline methods. PMID:21770035

  9. Integrating multiple networks for protein function prediction

    PubMed Central

    2015-01-01

    Background High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. Results We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. Conclusion MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request. PMID:25707434

  10. Protein-protein interaction networks (PPI) and complex diseases

    PubMed Central

    Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

    2014-01-01

    The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

  11. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

    The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

  12. Amplitude death of identical oscillators in networks with direct coupling.

    PubMed

    Illing, Lucas

    2016-08-01

    It is known that amplitude death can occur in networks of coupled identical oscillators if they interact via diffusive time-delayed coupling links. Here we consider networks of oscillators that interact via direct time-delayed coupling links. It is shown analytically that amplitude death is impossible for directly coupled Stuart-Landau oscillators, in contradistinction to the case of diffusive coupling. We demonstrate that amplitude death in the strict sense does become possible in directly coupled networks if the node dynamics is governed by second-order delay differential equations. Finally, we analyze in detail directly coupled nodes whose dynamics are described by first-order delay differential equations and find that, while amplitude death in the strict sense is impossible, other interesting oscillation quenching scenarios exist. PMID:27627306

  13. Amplitude death of identical oscillators in networks with direct coupling

    NASA Astrophysics Data System (ADS)

    Illing, Lucas

    2016-08-01

    It is known that amplitude death can occur in networks of coupled identical oscillators if they interact via diffusive time-delayed coupling links. Here we consider networks of oscillators that interact via direct time-delayed coupling links. It is shown analytically that amplitude death is impossible for directly coupled Stuart-Landau oscillators, in contradistinction to the case of diffusive coupling. We demonstrate that amplitude death in the strict sense does become possible in directly coupled networks if the node dynamics is governed by second-order delay differential equations. Finally, we analyze in detail directly coupled nodes whose dynamics are described by first-order delay differential equations and find that, while amplitude death in the strict sense is impossible, other interesting oscillation quenching scenarios exist.

  14. A Network Synthesis Model for Generating Protein Interaction Network Families

    PubMed Central

    Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun

    2012-01-01

    In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/. PMID:22912671

  15. Coupling entropy of co-processing model on social networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhanli

    2015-08-01

    Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

  16. Amplitude death in networks of delay-coupled delay oscillators.

    PubMed

    Höfener, Johannes M; Sethia, Gautam C; Gross, Thilo

    2013-09-28

    Amplitude death is a dynamical phenomenon in which a network of oscillators settles to a stable state as a result of coupling. Here, we study amplitude death in a generalized model of delay-coupled delay oscillators. We derive analytical results for degree homogeneous networks which show that amplitude death is governed by certain eigenvalues of the network's adjacency matrix. In particular, these results demonstrate that in delay-coupled delay oscillators amplitude death can occur for arbitrarily large coupling strength k. In this limit, we find a region of amplitude death which already occurs at small coupling delays that scale with 1/k. We show numerically that these results remain valid in random networks with heterogeneous degree distribution.

  17. Ontology integration to identify protein complex in protein interaction networks

    PubMed Central

    2011-01-01

    Background Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms. Methods We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes. Results The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches. PMID:22165991

  18. How do oncoprotein mutations rewire protein-protein interaction networks?

    PubMed

    Bowler, Emily H; Wang, Zhenghe; Ewing, Rob M

    2015-01-01

    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. PMID:26325016

  19. Unraveling Protein Networks with Power Graph Analysis

    PubMed Central

    Royer, Loïc; Reimann, Matthias; Andreopoulos, Bill; Schroeder, Michael

    2008-01-01

    Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks. PMID:18617988

  20. Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

    PubMed

    DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. PMID:25787689

  1. Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

    PubMed

    DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species.

  2. Complex network synchronization of chaotic systems with delay coupling

    SciTech Connect

    Theesar, S. Jeeva Sathya Ratnavelu, K.

    2014-03-05

    The study of complex networks enables us to understand the collective behavior of the interconnected elements and provides vast real time applications from biology to laser dynamics. In this paper, synchronization of complex network of chaotic systems has been studied. Every identical node in the complex network is assumed to be in Lur’e system form. In particular, delayed coupling has been assumed along with identical sector bounded nonlinear systems which are interconnected over network topology.

  3. Alignment-free protein interaction network comparison

    PubMed Central

    Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

    2014-01-01

    Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

  4. Weak chimeras in minimal networks of coupled phase oscillators.

    PubMed

    Ashwin, Peter; Burylko, Oleksandr

    2015-01-01

    We suggest a definition for a type of chimera state that appears in networks of indistinguishable phase oscillators. Defining a "weak chimera" as a type of invariant set showing partial frequency synchronization, we show that this means they cannot appear in phase oscillator networks that are either globally coupled or too small. We exhibit various networks of four, six, and ten indistinguishable oscillators, where weak chimeras exist with various dynamics and stabilities. We examine the role of Kuramoto-Sakaguchi coupling in giving degenerate (neutrally stable) families of weak chimera states in these example networks.

  5. Restoration of rhythmicity in diffusively coupled dynamical networks

    PubMed Central

    Zou, Wei; Senthilkumar, D. V.; Nagao, Raphael; Kiss, István Z.; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-01-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks. PMID:26173555

  6. Restoration of rhythmicity in diffusively coupled dynamical networks.

    PubMed

    Zou, Wei; Senthilkumar, D V; Nagao, Raphael; Kiss, István Z; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-01-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks. PMID:26173555

  7. Restoration of rhythmicity in diffusively coupled dynamical networks

    NASA Astrophysics Data System (ADS)

    Zou, Wei; Senthilkumar, D. V.; Nagao, Raphael; Kiss, István Z.; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

    2015-07-01

    Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks.

  8. Hepatitis C virus infection protein network

    PubMed Central

    de Chassey, B; Navratil, V; Tafforeau, L; Hiet, M S; Aublin-Gex, A; Agaugué, S; Meiffren, G; Pradezynski, F; Faria, B F; Chantier, T; Le Breton, M; Pellet, J; Davoust, N; Mangeot, P E; Chaboud, A; Penin, F; Jacob, Y; Vidalain, P O; Vidal, M; André, P; Rabourdin-Combe, C; Lotteau, V

    2008-01-01

    A proteome-wide mapping of interactions between hepatitis C virus (HCV) and human proteins was performed to provide a comprehensive view of the cellular infection. A total of 314 protein–protein interactions between HCV and human proteins was identified by yeast two-hybrid and 170 by literature mining. Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HCV are enriched in highly central and interconnected proteins. A global analysis on the basis of functional annotation highlighted the enrichment of cellular pathways targeted by HCV. A network of proteins associated with frequent clinical disorders of chronically infected patients was constructed by connecting the insulin, Jak/STAT and TGFβ pathways with cellular proteins targeted by HCV. CORE protein appeared as a major perturbator of this network. Focal adhesion was identified as a new function affected by HCV, mainly by NS3 and NS5A proteins. PMID:18985028

  9. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An interesting

  10. Transient electrical coupling regulates formation of neuronal networks.

    PubMed

    Szabo, Theresa M; Zoran, Mark J

    2007-01-19

    Electrical synapses are abundant before and during developmental windows of intense chemical synapse formation, and might therefore contribute to the establishment of neuronal networks. Transient electrical coupling develops and is then eliminated between regenerating Helisoma motoneurons 110 and 19 during a period of 48-72 h in vivo and in vitro following nerve injury. An inverse relationship exists between electrical coupling and chemical synaptic transmission at these synapses, such that the decline in electrical coupling is coincident with the emergence of cholinergic synaptic transmission. In this study, we have generated two- and three-cell neuronal networks to test whether predicted synaptogenic capabilities were affected by previous synaptic interactions. Electrophysiological analyses demonstrated that synapses formed in three-cell neuronal networks were not those predicted based on synaptogenic outcomes in two-cell networks. Thus, new electrical and chemical synapse formation within a neuronal network is dependent on existing connectivity of that network. In addition, new contacts formed with established networks have little impact on these existing connections. These results suggest that network-dependent mechanisms, particularly those mediated by gap junctional coupling, regulate synapse formation within simple neural networks. PMID:17156754

  11. From networks of protein interactions to networks of functional dependencies

    PubMed Central

    2012-01-01

    Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation). However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations), based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud) or biological processes (e.g., cell budding) of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms. PMID:22607727

  12. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself.

  13. Control of coupled oscillator networks with application to microgrid technologies.

    PubMed

    Skardal, Per Sebastian; Arenas, Alex

    2015-08-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions-a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  14. Control of coupled oscillator networks with application to microgrid technologies

    PubMed Central

    Skardal, Per Sebastian; Arenas, Alex

    2015-01-01

    The control of complex systems and network-coupled dynamical systems is a topic of vital theoretical importance in mathematics and physics with a wide range of applications in engineering and various other sciences. Motivated by recent research into smart grid technologies, we study the control of synchronization and consider the important case of networks of coupled phase oscillators with nonlinear interactions—a paradigmatic example that has guided our understanding of self-organization for decades. We develop a method for control based on identifying and stabilizing problematic oscillators, resulting in a stable spectrum of eigenvalues, and in turn a linearly stable synchronized state. The amount of control, that is, number of oscillators, required to stabilize the network is primarily dictated by the coupling strength, dynamical heterogeneity, and mean degree of the network, and depends little on the structural heterogeneity of the network itself. PMID:26601231

  15. Impulsive synchronization of coupled dynamical networks with nonidentical Duffing oscillators and coupling delays.

    PubMed

    Wang, Zhengxin; Duan, Zhisheng; Cao, Jinde

    2012-03-01

    This paper aims to investigate the synchronization problem of coupled dynamical networks with nonidentical Duffing-type oscillators without or with coupling delays. Different from cluster synchronization of nonidentical dynamical networks in the previous literature, this paper focuses on the problem of complete synchronization, which is more challenging than cluster synchronization. By applying an impulsive controller, some sufficient criteria are obtained for complete synchronization of the coupled dynamical networks of nonidentical oscillators. Furthermore, numerical simulations are given to verify the theoretical results. PMID:22463016

  16. Dynamics of a network of phase oscillators with plastic couplings

    NASA Astrophysics Data System (ADS)

    Nekorkin, V. I.; Kasatkin, D. V.

    2016-06-01

    The processes of synchronization and phase cluster formation are investigated in a complex network of dynamically coupled phase oscillators. Coupling weights evolve dynamically depending on the phase relations between the oscillators. It is shown that the network exhibits several types of behavior: the globally synchronized state, two-cluster and multi-cluster states, different synchronous states with a fixed phase relationship between the oscillators and chaotic desynchronized state.

  17. Targeted attack on networks coupled by connectivity and dependency links

    NASA Astrophysics Data System (ADS)

    Du, Ruijin; Dong, Gaogao; Tian, Lixin; Liu, Runran

    2016-05-01

    Coupled systems used to increase capacity were shown beneficial as long as it does not open pathways to cascades. Previous studies on the robustness of coupled networks except for interdependent networks are almost the cases of random attack. Many challenges remain exist in targeted-attack problem of coupled networks. Since nodes within coupled networks show different functions for each network, this paper both analytically and numerically analyzed the robustness of coupled networks under three types of targeted attacking strategies, including attacking on nodes by considering internal and external degree, internal degree only, and external degree only. For coupled network with both interdependent and interconnected links, all degree distributions of intra- and inter-connectivity links are Poissonian, we find the system undergoes from second to first order phase transition as coupling strength q increases. The fraction of nodes in the giant component P∞ at stable state, the critical phase transition threshold pc (first order threshold pcI and second order threshold pcII), and the critical point (pc ,qc) separating the first and second order phase transitions are analytically obtained for three types of attacking strategies with attacking probability parameter α = 0 , 1. We also find the system becomes more vulnerable as the average degree of intra-links k ¯ or inter-links K ¯ decreases. Therefore, the minimum average degrees k¯min and K¯min to maintain the system stable are obtained for the case of α = 0 , 1. Moreover, we discussed three typical cases of coupled networks, interdependent networks (K ¯ = 0), interacting networks (q = 0) and bipartite network (k = 0, q = 0), the analytical expressions of P∞, pc and (pc ,qc) (only for interdependent) are given respectively. Besides, we study the equivalence relations between interdependent networks and coupled networks with connectivity and dependency links for the same pc. The results imply that we can

  18. NAPS: Network Analysis of Protein Structures.

    PubMed

    Chakrabarty, Broto; Parekh, Nita

    2016-07-01

    Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue-residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein-protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

  19. Evolutionarily conserved coupling of adaptive and excitable networks mediates eukaryotic chemotaxis

    NASA Astrophysics Data System (ADS)

    Tang, Ming; Wang, Mingjie; Shi, Changji; Iglesias, Pablo A.; Devreotes, Peter N.; Huang, Chuan-Hsiang

    2014-10-01

    Numerous models explain how cells sense and migrate towards shallow chemoattractant gradients. Studies show that an excitable signal transduction network acts as a pacemaker that controls the cytoskeleton to drive motility. Here we show that this network is required to link stimuli to actin polymerization and chemotactic motility and we distinguish the various models of chemotaxis. First, signalling activity is suppressed towards the low side in a gradient or following removal of uniform chemoattractant. Second, signalling activities display a rapid shut off and a slower adaptation during which responsiveness to subsequent test stimuli decline. Simulations of various models indicate that these properties require coupled adaptive and excitable networks. Adaptation involves a G-protein-independent inhibitor, as stimulation of cells lacking G-protein function suppresses basal activities. The salient features of the coupled networks were observed for different chemoattractants in Dictyostelium and in human neutrophils, suggesting an evolutionarily conserved mechanism for eukaryotic chemotaxis.

  20. Identifying the hub proteins from complicated membrane protein network systems.

    PubMed

    Shen, Yi-Zhen; Ding, Yong-Sheng; Gu, Quan; Chou, Kuo-Chen

    2010-05-01

    The so-called "hub proteins" are those proteins in a protein-protein interaction network system that have remarkably higher interaction relations (or degrees) than the others. Therefore, the information of hub proteins can provide very useful insights for selecting or prioritizing targets during drug development. In this paper, by combining the multi-agent-based method with the graphical spectrum analysis and immune-genetic algorithm, a novel simulator for identifying the hub proteins from membrane protein interaction networks is proposed. As a demonstration of using the simulator, two hub membrane proteins, YPL227C and YIL147C, were identified from a complicated network system consisting of 1500 membrane proteins. Meanwhile, along with the two identified hub proteins, their molecular functions, biological processes, and cellular components were also revealed. It is anticipated that the hub-protein-simulator may become a very useful tool for system biology and drug development, particularly in deciphering unknown protein functions, determining protein complexes, and in identifying the key targets from a complicated disease system. PMID:20507268

  1. Different types of synchronization in coupled network based chaotic circuits

    NASA Astrophysics Data System (ADS)

    Srinivasan, K.; Chandrasekar, V. K.; Gladwin Pradeep, R.; Murali, K.; Lakshmanan, M.

    2016-10-01

    We propose a simple and new unified method to achieve lag, complete and anticipatory synchronizations in coupled nonlinear systems. It can be considered as an alternative to the subsystem and intentional parameter mismatch methods. This novel method is illustrated in a unidirectionally coupled RC phase shift network based Chua's circuit. Employing feedback coupling, different types of chaos synchronization are observed experimentally and numerically in coupled identical chaotic oscillators without using time delay. With a simple switch in the experimental set up we observe different kinds of synchronization. We also analyze the coupled system with numerical simulations.

  2. Delay-induced synchrony in complex networks with conjugate coupling.

    PubMed

    Shrii, M Manju; Senthilkumar, D V; Kurths, J

    2012-05-01

    We demonstrate stable synchronous chaos in a delay coupled network of time continuous dynamical system using the framework of master stability formalism (MSF). It is further shown that conjugate coupling, i.e., coupling using dissimilar variables, can substitute delay coupling of similar variables in retrieving delay-induced phenomena. By exploiting the MSF, we show that delayed conjugate coupling in an arbitrary network is capable of both inducing synchronization where there is no synchronization at all and enhancing synchronization to a large parameter space, which even the conjugate coupling without delay is incapable of. The above results are demonstrated using the paradigmatic Rössler system and Hindmarsh-Rose neuron. PMID:23004910

  3. Embryonic stem cells: protein interaction networks*

    PubMed Central

    Ng, Patricia Miang-Lon; Lufkin, Thomas

    2012-01-01

    Embryonic stem cells have the ability to differentiate into nearly all cell types. However, the molecular mechanism of its pluripotency is still unclear. Oct3/4, Sox2 and Nanog are important factors of pluripotency. Oct3/4 (hereafter referred to as Oct4), in particular, has been an irreplaceable factor in the induction of pluripotency in adult cells. Proteins interacting with Oct4 and Nanog have been identified via affinity purification and mass spectrometry. These data, together with iterative purifications of interacting proteins allowed a protein interaction network to be constructed. The network currently includes 77 transcription factors, all of which are interconnected in one network. In-depth studies of some of these transcription factors show that they all recruit the NuRD complex. Hence, transcription factor clustering and chromosomal remodeling are key mechanism used by embryonic stem cells. Studies using RNA interference suggest that more pluripotency genes are yet to be discovered via protein-protein interactions. More work is required to complete and curate the embryonic stem cell protein interaction network. Analysis of a saturated protein interaction network by system biology tools can greatly aid in the understanding of the embryonic stem cell pluripotency network. PMID:22639699

  4. Stimulus-dependent synchronization in delayed-coupled neuronal networks

    PubMed Central

    Esfahani, Zahra G.; Gollo, Leonardo L.; Valizadeh, Alireza

    2016-01-01

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand. PMID:27001428

  5. Stimulus-dependent synchronization in delayed-coupled neuronal networks.

    PubMed

    Esfahani, Zahra G; Gollo, Leonardo L; Valizadeh, Alireza

    2016-03-22

    Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand.

  6. NAPS: Network Analysis of Protein Structures

    PubMed Central

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

    Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

  7. G Protein-Coupled Receptors in Cancer.

    PubMed

    Bar-Shavit, Rachel; Maoz, Myriam; Kancharla, Arun; Nag, Jeetendra Kumar; Agranovich, Daniel; Grisaru-Granovsky, Sorina; Uziely, Beatrice

    2016-01-01

    Despite the fact that G protein-coupled receptors (GPCRs) are the largest signal-conveying receptor family and mediate many physiological processes, their role in tumor biology is underappreciated. Numerous lines of evidence now associate GPCRs and their downstream signaling targets in cancer growth and development. Indeed, GPCRs control many features of tumorigenesis, including immune cell-mediated functions, proliferation, invasion and survival at the secondary site. Technological advances have further substantiated GPCR modifications in human tumors. Among these are point mutations, gene overexpression, GPCR silencing by promoter methylation and the number of gene copies. At this point, it is imperative to elucidate specific signaling pathways of "cancer driver" GPCRs. Emerging data on GPCR biology point to functional selectivity and "biased agonism"; hence, there is a diminishing enthusiasm for the concept of "one drug per GPCR target" and increasing interest in the identification of several drug options. Therefore, determining the appropriate context-dependent conformation of a functional GPCR as well as the contribution of GPCR alterations to cancer development remain significant challenges for the discovery of dominant cancer genes and the development of targeted therapeutics. PMID:27529230

  8. G Protein-Coupled Receptors in Cancer

    PubMed Central

    Bar-Shavit, Rachel; Maoz, Myriam; Kancharla, Arun; Nag, Jeetendra Kumar; Agranovich, Daniel; Grisaru-Granovsky, Sorina; Uziely, Beatrice

    2016-01-01

    Despite the fact that G protein-coupled receptors (GPCRs) are the largest signal-conveying receptor family and mediate many physiological processes, their role in tumor biology is underappreciated. Numerous lines of evidence now associate GPCRs and their downstream signaling targets in cancer growth and development. Indeed, GPCRs control many features of tumorigenesis, including immune cell-mediated functions, proliferation, invasion and survival at the secondary site. Technological advances have further substantiated GPCR modifications in human tumors. Among these are point mutations, gene overexpression, GPCR silencing by promoter methylation and the number of gene copies. At this point, it is imperative to elucidate specific signaling pathways of “cancer driver” GPCRs. Emerging data on GPCR biology point to functional selectivity and “biased agonism”; hence, there is a diminishing enthusiasm for the concept of “one drug per GPCR target” and increasing interest in the identification of several drug options. Therefore, determining the appropriate context-dependent conformation of a functional GPCR as well as the contribution of GPCR alterations to cancer development remain significant challenges for the discovery of dominant cancer genes and the development of targeted therapeutics. PMID:27529230

  9. Optimal control of coupled PDE networks with automated code generation

    NASA Astrophysics Data System (ADS)

    Papadopoulos, D.

    2012-09-01

    The purpose of this work is to present a framework for the optimal control of coupled PDE networks. A coupled PDE network is a system of partial differential equations coupled together. Such systems can be represented as a directed graph. A domain specific language (DSL)—an extension of the DOT language—is used for the description of such a coupled PDE network. The adjoint equations and the gradient, required for its optimal control, are computed with the help of a computer algebra system (CAS). Automated code generation techniques have been used for the generation of the PDE systems of both the direct and the adjoint equations. Both the direct and adjoint equations are solved with the standard finite element method. Finally, for the numerical optimization of the system standard optimization techniques are used such as BFGS and Newton conjugate gradient.

  10. Facilitated synchronization of complex networks through a discontinuous coupling strategy

    NASA Astrophysics Data System (ADS)

    Chen, L.; Qiu, C.; Huang, H. B.; Qi, G. X.; Wang, H. J.

    2010-08-01

    Synchronization stability in complex networks is a topic of theoretical interest and practical importance. Increasing effort has been devoted to the enhancement of synchronizability of networks, or more specifically, the design of synchronizable networks. However, most previous attempts turn the coupling weight/gradient or change the topological interactions, which sometimes is not manageable. In this paper, by adopting a simple kind of discontinuous coupling strategy: the uniform on-off coupling scheme, with on-off period being comparable to the timescale of node dynamics, the problem is solved within the framework of the master stability function. The results show that, this strategy can greatly increase the stable region of synchronization, which means the size of synchronizable networks can be much larger than the traditional case, without any changes of their connections. Furthermore, the synchronization speed can be accelerated considerably, which is even higher than the previous optimal case. The mechanism of the facilitation is revealed and shows that the continuous coupling in fact is one of the worst choices for synchronization in the view of discontinuous coupling strategy. The coupling cost required for synchronization is also examined, which is approximately the same as the continuous coupling.

  11. Structural dependencies of protein backbone 2JNC' couplings.

    PubMed

    Juranić, Nenad; Dannenberg, J J; Cornilescu, Gabriel; Salvador, Pedro; Atanasova, Elena; Ahn, Hee-Chul; Macura, Slobodan; Markley, John L; Prendergast, Franklyn G

    2008-04-01

    Protein folding can introduce strain in peptide covalent geometry, including deviations from planarity that are difficult to detect, especially for a protein in solution. We have found dependencies in protein backbone (2)J(NC') couplings on the planarity and the relative orientation of the sequential peptide planes. These dependences were observed in experimental (2)J(NC') couplings from seven proteins, and also were supported by DFT calculations for a model tripeptide. Findings indicate that elevated (2)J(NC') couplings may serve as reporters of structural strain in the protein backbone imposed by protein folds. Such information, supplemented with the H-bond strengths derived from (h3)J(NC') couplings, provides useful insight into the overall energy profile of the protein backbone in solution.

  12. Amplitude-phase coupling drives chimera states in globally coupled laser networks.

    PubMed

    Böhm, Fabian; Zakharova, Anna; Schöll, Eckehard; Lüdge, Kathy

    2015-04-01

    For a globally coupled network of semiconductor lasers with delayed optical feedback, we demonstrate the existence of chimera states. The domains of coherence and incoherence that are typical for chimera states are found to exist for the amplitude, phase, and inversion of the coupled lasers. These chimera states defy several of the previously established existence criteria. While chimera states in phase oscillators generally demand nonlocal coupling, large system sizes, and specially prepared initial conditions, we find chimera states that are stable for global coupling in a network of only four coupled lasers for random initial conditions. The existence is linked to a regime of multistability between the synchronous steady state and asynchronous periodic solutions. We show that amplitude-phase coupling, a concept common in different fields, is necessary for the formation of the chimera states.

  13. Regular synchrony lattices for product coupled cell networks.

    PubMed

    Aguiar, Manuela A D; Dias, Ana Paula S

    2015-01-01

    There are several ways for constructing (bigger) networks from smaller networks. We consider here the cartesian and the Kronecker (tensor) product networks. Our main aim is to determine a relation between the lattices of synchrony subspaces for a product network and the component networks of the product. In this sense, we show how to obtain the lattice of regular synchrony subspaces for a product network from the lattices of synchrony subspaces for the component networks. Specifically, we prove that a tensor of subspaces is of synchrony for the product network if and only if the subspaces involved in the tensor are synchrony subspaces for the component networks of the product. We also show that, in general, there are (irregular) synchrony subspaces for the product network that are not described by the synchrony subspaces for the component networks, concluding that, in general, it is not possible to obtain the all synchrony lattice for the product network from the corresponding lattices for the component networks. We also make the following remark concerning the fact that the cartesian and Kronecker products, as graph operations, are quite different, implying that the associated coupled cell systems have distinct structures. Although, the kinds of dynamics expected to occur are difficult to compare, we establish an inclusion relation between the lattices of synchrony subspaces for the cartesian and Kronecker products. PMID:25637919

  14. Regular synchrony lattices for product coupled cell networks.

    PubMed

    Aguiar, Manuela A D; Dias, Ana Paula S

    2015-01-01

    There are several ways for constructing (bigger) networks from smaller networks. We consider here the cartesian and the Kronecker (tensor) product networks. Our main aim is to determine a relation between the lattices of synchrony subspaces for a product network and the component networks of the product. In this sense, we show how to obtain the lattice of regular synchrony subspaces for a product network from the lattices of synchrony subspaces for the component networks. Specifically, we prove that a tensor of subspaces is of synchrony for the product network if and only if the subspaces involved in the tensor are synchrony subspaces for the component networks of the product. We also show that, in general, there are (irregular) synchrony subspaces for the product network that are not described by the synchrony subspaces for the component networks, concluding that, in general, it is not possible to obtain the all synchrony lattice for the product network from the corresponding lattices for the component networks. We also make the following remark concerning the fact that the cartesian and Kronecker products, as graph operations, are quite different, implying that the associated coupled cell systems have distinct structures. Although, the kinds of dynamics expected to occur are difficult to compare, we establish an inclusion relation between the lattices of synchrony subspaces for the cartesian and Kronecker products.

  15. Default and Executive Network Coupling Supports Creative Idea Production

    PubMed Central

    Beaty, Roger E.; Benedek, Mathias; Barry Kaufman, Scott; Silvia, Paul J.

    2015-01-01

    The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

  16. Default and Executive Network Coupling Supports Creative Idea Production.

    PubMed

    Beaty, Roger E; Benedek, Mathias; Kaufman, Scott Barry; Silvia, Paul J

    2015-01-01

    The role of attention in creative cognition remains controversial. Neuroimaging studies have reported activation of brain regions linked to both cognitive control and spontaneous imaginative processes, raising questions about how these regions interact to support creative thought. Using functional magnetic resonance imaging (fMRI), we explored this question by examining dynamic interactions between brain regions during a divergent thinking task. Multivariate pattern analysis revealed a distributed network associated with divergent thinking, including several core hubs of the default (posterior cingulate) and executive (dorsolateral prefrontal cortex) networks. The resting-state network affiliation of these regions was confirmed using data from an independent sample of participants. Graph theory analysis assessed global efficiency of the divergent thinking network, and network efficiency was found to increase as a function of individual differences in divergent thinking ability. Moreover, temporal connectivity analysis revealed increased coupling between default and salience network regions (bilateral insula) at the beginning of the task, followed by increased coupling between default and executive network regions at later stages. Such dynamic coupling suggests that divergent thinking involves cooperation between brain networks linked to cognitive control and spontaneous thought, which may reflect focused internal attention and the top-down control of spontaneous cognition during creative idea production. PMID:26084037

  17. Network motifs in integrated cellular networks of transcription-regulation and protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Yeger-Lotem, Esti; Sattath, Shmuel; Kashtan, Nadav; Itzkovitz, Shalev; Milo, Ron; Pinter, Ron Y.; Alon, Uri; Margalit, Hanah

    2004-04-01

    Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription-regulation or protein-protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription-regulation and protein-protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein-protein interactions and transcription regulation in Saccharomyces cerevisiae. We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.

  18. Delay-induced cluster patterns in coupled Cayley tree networks

    NASA Astrophysics Data System (ADS)

    Singh, A.; Jalan, S.

    2013-07-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

  19. Regulators of G-protein-signaling proteins: negative modulators of G-protein-coupled receptor signaling.

    PubMed

    Woodard, Geoffrey E; Jardín, Isaac; Berna-Erro, A; Salido, Gines M; Rosado, Juan A

    2015-01-01

    Regulators of G-protein-signaling (RGS) proteins are a category of intracellular proteins that have an inhibitory effect on the intracellular signaling produced by G-protein-coupled receptors (GPCRs). RGS along with RGS-like proteins switch on through direct contact G-alpha subunits providing a variety of intracellular functions through intracellular signaling. RGS proteins have a common RGS domain that binds to G alpha. RGS proteins accelerate GTPase and thus enhance guanosine triphosphate hydrolysis through the alpha subunit of heterotrimeric G proteins. As a result, they inactivate the G protein and quickly turn off GPCR signaling thus terminating the resulting downstream signals. Activity and subcellular localization of RGS proteins can be changed through covalent molecular changes to the enzyme, differential gene splicing, and processing of the protein. Other roles of RGS proteins have shown them to not be solely committed to being inhibitors but behave more as modulators and integrators of signaling. RGS proteins modulate the duration and kinetics of slow calcium oscillations and rapid phototransduction and ion signaling events. In other cases, RGS proteins integrate G proteins with signaling pathways linked to such diverse cellular responses as cell growth and differentiation, cell motility, and intracellular trafficking. Human and animal studies have revealed that RGS proteins play a vital role in physiology and can be ideal targets for diseases such as those related to addiction where receptor signaling seems continuously switched on.

  20. Multiplexed imaging of intracellular protein networks.

    PubMed

    Grecco, Hernán E; Imtiaz, Sarah; Zamir, Eli

    2016-08-01

    Cellular functions emerge from the collective action of a large number of different proteins. Understanding how these protein networks operate requires monitoring their components in intact cells. Due to intercellular and intracellular molecular variability, it is important to monitor simultaneously multiple components at high spatiotemporal resolution. However, inherent trade-offs narrow the boundaries of achievable multiplexed imaging. Pushing these boundaries is essential for a better understanding of cellular processes. Here the motivations, challenges and approaches for multiplexed imaging of intracellular protein networks are discussed. © 2016 International Society for Advancement of Cytometry. PMID:27183498

  1. Neural network error correction for solving coupled ordinary differential equations

    NASA Technical Reports Server (NTRS)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  2. Link weight evolution in a network of coupled chemical oscillators.

    PubMed

    Ke, Hua; Tinsley, Mark R; Steele, Aaron; Wang, Fang; Showalter, Kenneth

    2014-05-01

    Link weight evolution is studied in a network of coupled chemical oscillators. Oscillators are perturbed by adjustments in imposed light intensity based on excitatory or inhibitory links to other oscillators undergoing excitation. Experimental and modeling studies demonstrate that the network is capable of producing sustained coordinated activity. The individual nodes of the network exhibit incoherent firing events; however, a dominant frequency can be discerned within the collective signal by Fourier analysis. The introduction of spike-timing-dependent plasticity yields a network that evolves to a stable unimodal link weight distribution.

  3. Protein enriched pasta: structure and digestibility of its protein network.

    PubMed

    Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

    2016-02-01

    Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

  4. Protein enriched pasta: structure and digestibility of its protein network.

    PubMed

    Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

    2016-02-01

    Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest. PMID:26829164

  5. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    PubMed

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  6. Stable and transient multicluster oscillation death in nonlocally coupled networks.

    PubMed

    Schneider, Isabelle; Kapeller, Marie; Loos, Sarah; Zakharova, Anna; Fiedler, Bernold; Schöll, Eckehard

    2015-11-01

    In a network of nonlocally coupled Stuart-Landau oscillators with symmetry-breaking coupling, we study numerically, and explain analytically, a family of inhomogeneous steady states (oscillation death). They exhibit multicluster patterns, depending on the cluster distribution prescribed by the initial conditions. Besides stable oscillation death, we also find a regime of long transients asymptotically approaching synchronized oscillations. To explain these phenomena analytically in dependence on the coupling range and the coupling strength, we first use a mean-field approximation, which works well for large coupling ranges but fails for coupling ranges, which are small compared to the cluster size. Going beyond standard mean-field theory, we predict the boundaries of the different stability regimes as well as the transient times analytically in excellent agreement with numerical results. PMID:26651770

  7. Stable and transient multicluster oscillation death in nonlocally coupled networks

    NASA Astrophysics Data System (ADS)

    Schneider, Isabelle; Kapeller, Marie; Loos, Sarah; Zakharova, Anna; Fiedler, Bernold; Schöll, Eckehard

    2015-11-01

    In a network of nonlocally coupled Stuart-Landau oscillators with symmetry-breaking coupling, we study numerically, and explain analytically, a family of inhomogeneous steady states (oscillation death). They exhibit multicluster patterns, depending on the cluster distribution prescribed by the initial conditions. Besides stable oscillation death, we also find a regime of long transients asymptotically approaching synchronized oscillations. To explain these phenomena analytically in dependence on the coupling range and the coupling strength, we first use a mean-field approximation, which works well for large coupling ranges but fails for coupling ranges, which are small compared to the cluster size. Going beyond standard mean-field theory, we predict the boundaries of the different stability regimes as well as the transient times analytically in excellent agreement with numerical results.

  8. Network Compression as a Quality Measure for Protein Interaction Networks

    PubMed Central

    Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

    2012-01-01

    With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

  9. Pairwise alignment of protein interaction networks.

    PubMed

    Koyutürk, Mehmet; Kim, Yohan; Topkara, Umut; Subramaniam, Shankar; Szpankowski, Wojciech; Grama, Ananth

    2006-03-01

    With an ever-increasing amount of available data on protein-protein interaction (PPI) networks and research revealing that these networks evolve at a modular level, discovery of conserved patterns in these networks becomes an important problem. Although available data on protein-protein interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment of elegant mathematical models. The main challenge in aligning PPI networks is to define a graph theoretical measure of similarity between graph structures that captures underlying biological phenomena accurately. In this respect, modeling of conservation and divergence of interactions, as well as the interpretation of resulting alignments, are important design parameters. In this paper, we develop a framework for comprehensive alignment of PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. We propose a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, the proposed framework facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, our model allows flexibility in adjusting parameters to quantify underlying evolutionary relationships. Based on the proposed model, we formulate PPI network alignment as an optimization problem and present fast algorithms to solve this problem. Detailed experimental results from an implementation of the proposed framework show that our algorithm is able to discover conserved interaction patterns very effectively, in terms of both accuracies and computational

  10. Synchronization-based computation through networks of coupled oscillators

    PubMed Central

    Malagarriga, Daniel; García-Vellisca, Mariano A.; Villa, Alessandro E. P.; Buldú, Javier M.; García-Ojalvo, Jordi; Pons, Antonio J.

    2015-01-01

    The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates. PMID:26300765

  11. Synchronization-based computation through networks of coupled oscillators.

    PubMed

    Malagarriga, Daniel; García-Vellisca, Mariano A; Villa, Alessandro E P; Buldú, Javier M; García-Ojalvo, Jordi; Pons, Antonio J

    2015-01-01

    The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates.

  12. Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

    PubMed

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

    Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

  13. Ocean-atmosphere coupling from a climate network perspective

    NASA Astrophysics Data System (ADS)

    Wiedermann, Marc; Donges, Jonathan F.; Donner, Reik V.; Kurths, Jürgen

    2014-05-01

    In recent years extensive studies on the climate system have been carried out making use of advanced complex network statistics. However, most previous studies have been characterized by two conceptual restrictions: First, in most cases network measures have been computed without taking into account the topology of the discrete grid, regular or irregular, that climate data is typically stored on. To overcome this problem, the concept of node splitting invariant network measures has been introduced considering individual node weights, for example according to the surface area a node represents, when computing network measures [1]. Second, the great majority of recent studies have been focussing on single climatological fields located on surfaces parallel to or directly on the Earth's surface. A recent work introduced a methodology for quantifying interaction structures between geopotential height fields at different isobaric surfaces by proposing general definitions for network measures dealing with a network of networks composed from distinct subnetworks [2]. In this work, we combine both, the node-weighting scheme as well as the interacting network measure approach. For this purpose, we invent new node-weighted cross-network measures that provide a general tool for quantifying interaction structures in multilayer networks that is applicable to many fields beyond the study of the climate system, such as communication, social or trade networks. Our new approach is utilized for studying ocean-atmosphere coupling in the northern hemisphere. Specifically, we construct 18 coupled climate networks based on monthly data from the ERA 40 reanalysis, each consisting of two subnetworks. In all cases, one subnetwork represents sea-surface temperature (SST) anomalies while the other is based on the geopotential height (HGT) of isobaric surfaces at different pressure levels. By investigating the connectivity of the resulting interdependent network structures, we identify a

  14. Phase clustering in complex networks of delay-coupled oscillators

    NASA Astrophysics Data System (ADS)

    Pérez, Toni; Eguíluz, Víctor M.; Arenas, Alex

    2011-06-01

    We study the clusterization of phase oscillators coupled with delay in complex networks. For the case of diffusive oscillators, we formulate the equations relating the topology of the network and the phases and frequencies of the oscillators (functional response). We solve them exactly in directed networks for the case of perfect synchronization. We also compare the reliability of the solution of the linear system for non-linear couplings. Taking advantage of the form of the solution, we propose a frequency adaptation rule to achieve perfect synchronization. We also propose a mean-field theory for uncorrelated random networks that proves to be pretty accurate to predict phase synchronization in real topologies, as for example, the Caenorhabditis elegans or the autonomous systems connectivity.

  15. Interaction Energy Based Protein Structure Networks

    PubMed Central

    Vijayabaskar, M.S.; Vishveshwara, Saraswathi

    2010-01-01

    The three-dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino-acid residues of the polypeptide chain. These interactions can be represented collectively in the form of a network. So far, such networks have been investigated by considering the connections based on distances between the amino-acid residues. Here we present a method of constructing the structure network based on interaction energies among the amino-acid residues in the protein. We have investigated the properties of such protein energy-based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability, formation of secondary and super-secondary structural units. Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information, such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein. Finally, the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis. PMID:21112295

  16. Pinning synchronization of coupled inertial delayed neural networks.

    PubMed

    Hu, Jianqiang; Cao, Jinde; Alofi, Abdulaziz; Al-Mazrooei, Abdullah; Elaiw, Ahmed

    2015-06-01

    The paper is devoted to the investigation of synchronization for an array of linearly and diffusively coupled inertial delayed neural networks (DNNs). By placing feedback control on a small fraction of network nodes, the entire coupled DNNs can be synchronized to a common objective trajectory asymptotically. Two different analysis methods, including matrix measure strategy and Lyapunov-Krasovskii function approach, are employed to provide sufficient criteria for the synchronization control problem. Comparisons of these two techniques are given at the end of the paper. Finally, an illustrative example is provided to show the effectiveness of the obtained theoretical results.

  17. Generalized synchronization in mutually coupled oscillators and complex networks.

    PubMed

    Moskalenko, Olga I; Koronovskii, Alexey A; Hramov, Alexander E; Boccaletti, Stefano

    2012-09-01

    We introduce a concept of generalized synchronization, able to encompass the setting of collective synchronized behavior for mutually coupled systems and networking systems featuring complex topologies in their connections. The onset of the synchronous regime is confirmed by the dependence of the system's Lyapunov exponents on the coupling parameter. The presence of a generalized synchronization regime is verified by means of the nearest neighbor method.

  18. Controlling topological entanglement in engineered protein hydrogels with a variety of thiol coupling chemistries

    PubMed Central

    Tang, Shengchang; Olsen, Bradley D.

    2014-01-01

    Topological entanglements between polymer chains are achieved in associating protein hydrogels through the synthesis of high molecular weight proteins via chain extension using a variety of thiol coupling chemistries, including disulfide formation, thiol-maleimide, thiol-bromomaleimide and thiol-ene. Coupling of cysteines via disulfide formation results in the most pronounced entanglement effect in hydrogels, while other chemistries provide versatile means of changing the extent of entanglement, achieving faster chain extension, and providing a facile method of controlling the network hierarchy and incorporating stimuli responsivities. The addition of trifunctional coupling agents causes incomplete crosslinking and introduces branching architecture to the protein molecules. The high-frequency plateau modulus and the entanglement plateau modulus can be tuned by changing the ratio of difunctional chain extender to the trifunctional branching unit. Therefore, these chain extension reactions show promise in delicately controlling the relaxation and mechanical properties of engineered protein hydrogels in ways that complement their design through genetic engineering. PMID:24860800

  19. Controlling Topological Entanglement in Engineered Protein Hydrogels with a Variety of Thiol Coupling Chemistries

    NASA Astrophysics Data System (ADS)

    Tang, Shengchang; Olsen, Bradley

    2014-05-01

    Topological entanglements between polymer chains are achieved in associating protein hydrogels through the synthesis of high molecular weight proteins via chain extension using a variety of thiol coupling chemistries, including disulfide formation, thiol-maleimide, thiol-bromomaleimide and thiol-ene. Coupling of cysteines via disulfide formation results in the most pronounced entanglement effect in hydrogels, while other chemistries provide versatile means of changing the extent of entanglement, achieving faster chain extension, and providing a facile method of controlling the network hierarchy and incorporating stimuli responsivities. The addition of trifunctional coupling agents causes incomplete crosslinking and introduces branching architecture to the protein molecules. The high-frequency plateau modulus and the entanglement plateau modulus can be tuned by changing the ratio of difunctional chain extender to the trifunctional branching unit. Therefore, these chain extension reactions show promise in delicately controlling the relaxation and mechanical properties of engineered protein hydrogels in ways that complement their design through genetic engineering.

  20. Molecular dynamics study of naturally existing cavity couplings in proteins.

    PubMed

    Barbany, Montserrat; Meyer, Tim; Hospital, Adam; Faustino, Ignacio; D'Abramo, Marco; Morata, Jordi; Orozco, Modesto; de la Cruz, Xavier

    2015-01-01

    Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.

  1. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition.

    PubMed

    Spreng, R Nathan; Stevens, W Dale; Chamberlain, Jon P; Gilmore, Adrian W; Schacter, Daniel L

    2010-10-15

    Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed 'frontoparietal control network' might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by analyzing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Task-related functional connectivity analyses demonstrate that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes.

  2. Erosion of synchronization in networks of coupled oscillators.

    PubMed

    Skardal, Per Sebastian; Taylor, Dane; Sun, Jie; Arenas, Alex

    2015-01-01

    We report erosion of synchronization in networks of coupled phase oscillators, a phenomenon where perfect phase synchronization is unattainable in steady state, even in the limit of infinite coupling. An analysis reveals that the total erosion is separable into the product of terms characterizing coupling frustration and structural heterogeneity, both of which amplify erosion. The latter, however, can differ significantly from degree heterogeneity. Finally, we show that erosion is marked by the reorganization of oscillators according to their node degrees rather than their natural frequencies.

  3. A dynamically coupled allosteric network underlies binding cooperativity in Src kinase

    NASA Astrophysics Data System (ADS)

    Foda, Zachariah H.; Shan, Yibing; Kim, Eric T.; Shaw, David E.; Seeliger, Markus A.

    2015-01-01

    Protein tyrosine kinases are attractive drug targets because many human diseases are associated with the deregulation of kinase activity. However, how the catalytic kinase domain integrates different signals and switches from an active to an inactive conformation remains incompletely understood. Here we identify an allosteric network of dynamically coupled amino acids in Src kinase that connects regulatory sites to the ATP- and substrate-binding sites. Surprisingly, reactants (ATP and peptide substrates) bind with negative cooperativity to Src kinase while products (ADP and phosphopeptide) bind with positive cooperativity. We confirm the molecular details of the signal relay through the allosteric network by biochemical studies. Experiments on two additional protein tyrosine kinases indicate that the allosteric network may be largely conserved among these enzymes. Our work provides new insights into the regulation of protein tyrosine kinases and establishes a potential conduit by which resistance mutations to ATP-competitive kinase inhibitors can affect their activity.

  4. Receptor activity-modifying proteins; multifunctional G protein-coupled receptor accessory proteins.

    PubMed

    Hay, Debbie L; Walker, Christopher S; Gingell, Joseph J; Ladds, Graham; Reynolds, Christopher A; Poyner, David R

    2016-04-15

    Receptor activity-modifying proteins (RAMPs) are single pass membrane proteins initially identified by their ability to determine the pharmacology of the calcitonin receptor-like receptor (CLR), a family B G protein-coupled receptor (GPCR). It is now known that RAMPs can interact with a much wider range of GPCRs. This review considers recent developments on the structure of the complexes formed between the extracellular domains (ECDs) of CLR and RAMP1 or RAMP2 as these provide insights as to how the RAMPs direct ligand binding. The range of RAMP interactions is also considered; RAMPs can interact with numerous family B GPCRs as well as examples of family A and family C GPCRs. They influence receptor expression at the cell surface, trafficking, ligand binding and G protein coupling. The GPCR-RAMP interface offers opportunities for drug targeting, illustrated by examples of drugs developed for migraine. PMID:27068971

  5. The Fragility of Interdependency: Coupled Networks Switching Phenomena

    NASA Astrophysics Data System (ADS)

    Stanley, H. Eugene

    2013-03-01

    Recent disasters ranging from abrupt financial ``flash crashes'' and large-scale power outages to sudden death among the elderly dramatically exemplify the fact that the most dangerous vulnerability is hiding in the many interdependencies among different networks. In the past year, we have quantified failures in model of interconnected networks, and demonstrated the need to consider mutually dependent network properties in designing resilient systems. Specifically, we have uncovered new laws governing the nature of switching phenomena in coupled networks, and found that phenomena that are continuous ``second order'' phase transitions in isolated networks become discontinuous abrupt ``first order'' transitions in interdependent networks [S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, ``Catastrophic Cascade of Failures in Interdependent Networks,'' Nature 464, 1025 (2010); J. Gao, S. V. Buldyrev, H. E. Stanley, and S. Havlin, ``Novel Behavior of Networks Formed from Interdependent Networks,'' Nature Physics 8, 40 (2012). We conclude by discussing the network basis for understanding sudden death in the elderly, and the possibility that financial ``flash crashes'' are not unlike the catastrophic first-order failure incidents occurring in coupled networks. Specifically, we study the coupled networks that are responsible for financial fluctuations. It appears that ``trend switching phenomena'' that we uncover are remarkably independent of the scale over which they are analyzed. For example, we find that the same laws governing the formation and bursting of the largest financial bubbles also govern the tiniest finance bubbles, over a factor of 1,000,000,000 in time scale [T. Preis, J. Schneider, and H. E. Stanley, ``Switching Processes in Financial Markets,'' Proc. Natl. Acad. Sci. USA 108, 7674 (2011); T. Preis and H. E. Stanley, ``Bubble Trouble: Can a Law Describe Bubbles and Crashes in Financial Markets?'' Physics World 24, No. 5, 29 (May 2011

  6. Multifractal characterization of protein contact networks

    NASA Astrophysics Data System (ADS)

    Maiorino, Enrico; Livi, Lorenzo; Giuliani, Alessandro; Sadeghian, Alireza; Rizzi, Antonello

    2015-06-01

    The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic exponents and the multifractal spectrum can be further analyzed to discover important insights into the underlying dynamical process. In this paper, we employ multifractal analysis techniques in the study of protein contact networks. To this end, initially a network is mapped to three different time series, each of which is generated by a stationary unbiased random walk. To capture the peculiarities of the networks at different levels, we accordingly consider three observables at each vertex: the degree, the clustering coefficient, and the closeness centrality. To compare the results with suitable references, we consider also instances of three well-known network models and two typical time series with pure monofractal and multifractal properties. The first result of notable interest is that time series associated to protein contact networks exhibit long-range correlations (strong persistence), which are consistent with signals in-between the typical monofractal and multifractal behavior. Successively, a suitable embedding of the multifractal spectra allows to focus on ensemble properties, which in turn gives us the possibility to make further observations regarding the considered networks. In particular, we highlight the different role that small and large fluctuations of the considered observables play in the characterization of the network topology.

  7. Eukaryotic G Protein Signaling Evolved to Require G Protein–Coupled Receptors for Activation

    PubMed Central

    Bradford, William; Buckholz, Adam; Morton, John; Price, Collin; Jones, Alan M.; Urano, Daisuke

    2016-01-01

    Although bioinformatic analysis of the increasing numbers of diverse genome sequences and amount of functional data has provided insight into the evolution of signaling networks, bioinformatics approaches have limited application for understanding the evolution of highly divergent protein families. We used biochemical analyses to determine the in vitro properties of selected divergent components of the heterotrimeric guanine nucleotide–binding protein (G protein) signaling network to investigate signaling network evolution. In animals, G proteins are activated by cell-surface seven-transmembrane (7TM) receptors, which are named G protein–coupled receptors (GPCRs) and function as guanine nucleotide exchange factors (GEFs). In contrast, the plant G protein is intrinsically active, and a 7TM protein terminates G protein activity by functioning as a guanosine triphosphatase–activating protein (GAP). We showed that ancient regulation of the G protein active state is GPCR-independent and “self-activating,” a property that is maintained in Bikonts, one of the two fundamental evolutionary clades containing eukaryotes, whereas G proteins of the other clade, the Unikonts, evolved from being GEF-independent to being GEF-dependent. Self-activating G proteins near the base of the Eukaryota are controlled by 7TM-GAPs, suggesting that the ancestral regulator of G protein activation was a GAP-functioning receptor, not a GEF-functioning GPCR. Our findings indicate that the GPCR paradigm describes a recently evolved network architecture found in a relatively small group of Eukaryota and suggest that the evolution of signaling network architecture is constrained by the availability of molecules that control the activation state of nexus proteins. PMID:23695163

  8. Extension and limits of the network of coupled motions correlated to hydride transfer in dihydrofolate reductase.

    PubMed

    Singh, Priyanka; Sen, Arundhuti; Francis, Kevin; Kohen, Amnon

    2014-02-12

    Enzyme catalysis has been studied extensively, but the role of enzyme dynamics in the catalyzed chemical conversion is still an enigma. The enzyme dihydrofolate reductase (DHFR) is often used as a model system to assess a network of coupled motions across the protein that may affect the catalyzed chemical transformation. Molecular dynamics simulations, quantum mechanical/molecular mechanical studies, and bioinformatics studies have suggested the presence of a "global dynamic network" of residues in DHFR. Earlier studies of two DHFR distal mutants, G121V and M42W, indicated that these residues affect the chemical step synergistically. While this finding was in accordance with the concept of a network of functional motions across the protein, two residues do not constitute a network. To better define the extent and limits of the proposed network, the current work studied two remote residues predicted to be part of the same network: W133 and F125. The effect of mutations in these residues on the nature of the chemical step was examined via measurements of the temperature-dependence of the intrinsic kinetic isotope effects (KIEs) and other kinetic parameters, and double mutants were used to tie the findings to G121 and M42. The findings indicate that residue F125, which was implicated by both calculations and bioinformatic methods, is a part of the same global dynamic network as G121 and M42, while W133, implicated only by bioinformatics, is not. These findings extend our understanding of the proposed network and the relations between functional and genomic couplings. Delineating that network illuminates the need to consider remote residues and protein structural dynamics in the rational design of drugs and of biomimetic catalysts. PMID:24450297

  9. Characterization of Seed Storage Proteins from Chickpea Using 2D Electrophoresis Coupled with Mass Spectrometry.

    PubMed

    Singh, Pramod Kumar; Shrivastava, Nidhi; Chaturvedi, Krishna; Sharma, Bechan; Bhagyawant, Sameer S

    2016-01-01

    Proteomic analysis was employed to map the seed storage protein network in landrace and cultivated chickpea accessions. Protein extracts were separated by two-dimensional gel electrophoresis (2D-GE) across a broad range 3.0-10.0 immobilized pH gradient (IPG) strips. Comparative elucidation of differentially expressed proteins between two diverse geographically originated chickpea accessions was carried out using 2D-GE coupled with mass spectrometry. A total of 600 protein spots were detected in these accessions. In-gel protein expression patterns revealed three protein spots as upregulated and three other as downregulated. Using trypsin in-gel digestion, these differentially expressed proteins were identified by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) which showed 45% amino acid homology of chickpea seed storage proteins with Arabidopsis thaliana. PMID:27144024

  10. Phase-lag synchronization in networks of coupled chemical oscillators.

    PubMed

    Totz, Jan F; Snari, Razan; Yengi, Desmond; Tinsley, Mark R; Engel, Harald; Showalter, Kenneth

    2015-08-01

    Chemical oscillators with a broad frequency distribution are photochemically coupled in network topologies. Experiments and simulations show that the network synchronization occurs by phase-lag synchronization of clusters of oscillators with zero- or nearly zero-lag synchronization. Symmetry also plays a role in the synchronization, the extent of which is explored as a function of coupling strength, frequency distribution, and the highest frequency oscillator location. The phase-lag synchronization occurs through connected synchronized clusters, with the highest frequency node or nodes setting the frequency of the entire network. The synchronized clusters successively "fire," with a constant phase difference between them. For low heterogeneity and high coupling strength, the synchronized clusters are made up of one or more clusters of nodes with the same permutation symmetries. As heterogeneity is increased or coupling strength decreased, the phase-lag synchronization occurs partially through clusters of nodes sharing the same permutation symmetries. As heterogeneity is further increased or coupling strength decreased, partial synchronization and, finally, independent unsynchronized oscillations are observed. The relationships between these classes of behavior are explored with numerical simulations, which agree well with the experimentally observed behavior.

  11. Finite time thermodynamic coupling in a biochemical network.

    PubMed

    Dasgupta, Anjan Kr

    2014-03-01

    The paper describes some thermodynamic constrains and relations in biochemical or metabolic network and provides a basis for entropy enthalpy compensation. Conventional definition of macroscopic forces and fluxes leads to a paradox namely, non-existence of positive efficiency of a chemically driven process. This paradox is resolved by deriving an appropriate definition of macroscopic force using the local balance equations. Entropy enthalpy compensation, whose thermodynamic basis is so far unclear, also follows. The method provides an account of how reactive pathways are coupled, the strength of coupling between a pathway pair depending on the product of their respective enthalpies. The obligatory role of the presence of a common chemical intermediate in defining coupling becomes unnecessary; such intermediate-free coupling being a key feature of metabolic energy transduction. The redefined flux and force can also be exploited to explain surface to volume ratio dependence of coupled networks. Lastly, the thermodynamic rationale for the Bergman's eco-geographic rule, namely the reduced ability of larger animals to avoid stress follows from the generalized expression for coupling coefficients. Higher surface to volume ratio is shown to make the organism resistant to external perturbations.

  12. Network measures for protein folding state discrimination

    PubMed Central

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

    Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis. PMID:27464796

  13. Network measures for protein folding state discrimination.

    PubMed

    Menichetti, Giulia; Fariselli, Piero; Remondini, Daniel

    2016-01-01

    Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis. PMID:27464796

  14. Network-scale effect on synchronizability of fully coupled network with connection delay.

    PubMed

    Zheng, Y G; Wang, Z H

    2016-04-01

    Network-scale effect on synchronizability of fully coupled network with connection delay is investigated in this paper. The master stability function, which governs the stability of synchronization manifold, is first obtained by separating the synchronization manifold direction from other transverse directions. Then, by introducing a new time variable in the master stability function, it is shown the effect of connection delay can be weakened with the increase of network scale, and thus, in contrast to the situation without connection delay, large network scale is more positive to the synchronizability of fully coupled network with connection delay. Those findings are confirmed by the studies on two specific networks with nodes of typical nonlinear dynamical systems. PMID:27131482

  15. Protein-protein interaction network analysis of cirrhosis liver disease

    PubMed Central

    Safaei, Akram; Rezaei Tavirani, Mostafa; Arefi Oskouei, Afsaneh; Zamanian Azodi, Mona; Mohebbi, Seyed Reza; Nikzamir, Abdol Rahim

    2016-01-01

    Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease. PMID:27099671

  16. Network-Based Protein Biomarker Discovery Platforms

    PubMed Central

    Kim, Minhyung

    2016-01-01

    The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885

  17. FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

    PubMed

    Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

    2014-10-01

    A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

  18. Predicting the fission yeast protein interaction network.

    PubMed

    Pancaldi, Vera; Saraç, Omer S; Rallis, Charalampos; McLean, Janel R; Převorovský, Martin; Gould, Kathleen; Beyer, Andreas; Bähler, Jürg

    2012-04-01

    A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

  19. Designed Proteins To Modulate Cellular Networks

    PubMed Central

    Cortajarena, Aitziber L.; Liu, Tina Y.; Hochstrasser, Mark; Regan, Lynne

    2012-01-01

    A major challenge of protein design is to create useful new proteins that interact specifically with biological targets in living cells. Such binding modules have many potential applications, including the targeted perturbation of protein networks. As a general approach to create such modules, we designed a library with approximately 109 different binding specificities based on a small 3-tetratricopeptide repeat (TPR) motif framework. We employed a novel strategy, based on split GFP reassembly, to screen the library for modules with the desired binding specificity. Using this approach, we identified modules that bind tightly and specifically to Dss1, a small human protein that interacts with the tumor suppressor protein BRCA2. We showed that these modules also bind the yeast homologue of Dss1, Sem1. Furthermore, we demonstrated that these modules inhibit Sem1 activity in yeast. This strategy will be generally applicable to make novel genetically encoded tools for systems/synthetic biology applications. PMID:20020775

  20. Biased imitation in coupled evolutionary games in interdependent networks

    NASA Astrophysics Data System (ADS)

    Santos, M. D.; Dorogovtsev, S. N.; Mendes, J. F. F.

    2014-03-01

    We explore the evolutionary dynamics of two games--the Prisoner's Dilemma and the Snowdrift Game--played within distinct networks (layers) of interdependent networks. In these networks imitation and interaction between individuals of opposite layers is established through interlinks. We explore an update rule in which revision of strategies is a biased imitation process: individuals imitate neighbors from the same layer with probability p, and neighbors from the second layer with complementary probability 1 - p. We demonstrate that a small decrease of p from p = 1 (which corresponds to forbidding strategy transfer between layers) is sufficient to promote cooperation in the Prisoner's Dilemma subpopulation. This, on the other hand, is detrimental for cooperation in the Snowdrift Game subpopulation. We provide results of extensive computer simulations for the case in which layers are modelled as regular random networks, and support this study with analytical results for coupled well-mixed populations.

  1. Pclust: protein network visualization highlighting experimental data

    PubMed Central

    Li, Wenlin; Kinch, Lisa N.; Grishin, Nick V.

    2013-01-01

    Summary: One approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. ‘reference proteins’. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such ‘reference proteins’ and providing access to their full information in source databases, e.g. articles in PubMed. The identification of ‘reference proteins’ and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest. Availability: The Pclust server is freely available at http://prodata.swmed.edu/pclust Contact: grishin@chop.swmed.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:23918248

  2. Correlated loss of ecosystem services in coupled mutualistic networks.

    PubMed

    Albrecht, Jörg; Berens, Dana Gertrud; Jaroszewicz, Bogdan; Selva, Nuria; Brandl, Roland; Farwig, Nina

    2014-05-08

    Networks of species interactions promote biodiversity and provide important ecosystem services. These networks have traditionally been studied in isolation, but species are commonly involved in multiple, diverse types of interaction. Therefore, whether different types of species interaction networks coupled through shared species show idiosyncratic or correlated responses to habitat degradation is unresolved. Here we study the collective response of coupled mutualistic networks of plants and their pollinators and seed dispersers to the degradation of Europe's last relict of old-growth lowland forest (Białowieża, Poland). We show that logging of old-growth forests has correlated effects on the number of partners and interactions of plants in both mutualisms, and that these effects are mediated by shifts in plant densities on logged sites. These results suggest bottom-up-controlled effects of habitat degradation on plant-animal mutualistic networks, and predict that the conversion of primary old-growth forests to secondary habitats may cause a parallel loss of multiple animal-mediated ecosystem services.

  3. Rescue of endemic states in interconnected networks with adaptive coupling

    NASA Astrophysics Data System (ADS)

    Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

    2016-07-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.

  4. Rescue of endemic states in interconnected networks with adaptive coupling

    NASA Astrophysics Data System (ADS)

    Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

    2016-07-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.

  5. Rescue of endemic states in interconnected networks with adaptive coupling

    PubMed Central

    Vazquez, F.; Serrano, M. Ángeles; Miguel, M. San

    2016-01-01

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network –and therefore on the interconnected system– the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime. PMID:27380771

  6. Rescue of endemic states in interconnected networks with adaptive coupling.

    PubMed

    Vazquez, F; Serrano, M Ángeles; Miguel, M San

    2016-07-06

    We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads when the two layers are interconnected but not in each layer separately, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network -and therefore on the interconnected system- the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that rewiring amplifies finite-size effects, preventing the disease transmission between finite networks, as there is a non zero probability that the epidemics stays confined in only one network during its lifetime.

  7. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    PubMed

    Yen, Eric A; Tsay, Aaron; Waldispuhl, Jerome; Vogel, Jackie

    2014-05-01

    Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden

  8. Localized patterns in homogeneous networks of diffusively coupled reactors

    NASA Astrophysics Data System (ADS)

    Moore, Peter K.; Horsthemke, Werner

    2005-06-01

    We study the influence of network topology on instabilities of the homogeneous steady state of diffusively coupled, monostable nonlinear cells. A particular focus are diffusion-induced instabilities, i.e., Turing instabilities. We present various theorems that make it possible to determine analytically the stability properties of networks with arbitrary topologies and general monostable dynamics of the individual cells. This work aims in particular to determine those topologies that will give rise to localized stationary patterns. Specific examples focus on well-stirred chemical reactors. The reactors are coupled by diffusion-like mass transfer, and the kinetics is given by the Lengyel-Epstein model, a two-variable scheme for the chlorine dioxide-iodine-malonic acid reaction.

  9. Module organization and variance in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Lin, Chun-Yu; Lee, Tsai-Ling; Chiu, Yi-Yuan; Lin, Yi-Wei; Lo, Yu-Shu; Lin, Chih-Ta; Yang, Jinn-Moon

    2015-03-01

    A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

  10. Pinning synchronization of discrete dynamical networks with delay coupling

    NASA Astrophysics Data System (ADS)

    Cheng, Ranran; Peng, Mingshu; Zuo, Jun

    2016-05-01

    The purpose of this paper is to investigate the pinning synchronization analysis for nonlinear coupled delayed discrete dynamical networks with the identical or nonidentical topological structure. Based on the Lyapunov stability theory, pinning control method and linear matrix inequalities, several adaptive synchronization criteria via two kinds of pinning control method are obtained. Two examples based on Rulkov chaotic system are included to illustrate the effectiveness and verification of theoretical analysis.

  11. MHD generator with improved network coupling electrodes to a load

    DOEpatents

    Rosa, Richard J.

    1977-01-01

    An MHD generator has a plurality of segmented electrodes extending longitudinally of a duct, whereby progressively increasing high DC voltages are derived from a set of cathode electrodes and progressively increasing low DC voltages are derived from a set of anode electrodes. First and second load terminals are respectively connected to the cathode and anode electrodes by separate coupling networks, each of which includes a number of SCR's and a number of diode rectifiers.

  12. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures.

    PubMed

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715

  13. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures

    PubMed Central

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715

  14. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures.

    PubMed

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids.

  15. WD40 proteins propel cellular networks.

    PubMed

    Stirnimann, Christian U; Petsalaki, Evangelia; Russell, Robert B; Müller, Christoph W

    2010-10-01

    Recent findings indicate that WD40 domains play central roles in biological processes by acting as hubs in cellular networks; however, they have been studied less intensely than other common domains, such as the kinase, PDZ or SH3 domains. As suggested by various interactome studies, they are among the most promiscuous interactors. Structural studies suggest that this property stems from their ability, as scaffolds, to interact with diverse proteins, peptides or nucleic acids using multiple surfaces or modes of interaction. A general scaffolding role is supported by the fact that no WD40 domain has been found with intrinsic enzymatic activity despite often being part of large molecular machines. We discuss the WD40 domain distributions in protein networks and structures of WD40-containing assemblies to demonstrate their versatility in mediating critical cellular functions.

  16. Pattern Selection in Network of Coupled Multi-Scroll Attractors

    PubMed Central

    Li, Fan

    2016-01-01

    Multi-scroll chaotic attractor makes the oscillator become more complex in dynamic behaviors. The collective behaviors of coupled oscillators with multi-scroll attractors are investigated in the regular network in two-dimensional array, which the local kinetics is described by an improved Chua circuit. A feasible scheme of negative feedback with diversity is imposed on the network to stabilize the spatial patterns. Firstly, the Chua circuit is improved by replacing the nonlinear term with Sine function to generate infinite aquariums so that multi-scroll chaotic attractors could be generated under appropriate parameters, which could be detected by calculating the Lyapunov exponent in the parameter region. Furthermore, negative feedback with different gains (D1, D2) is imposed on the local square center area A2 and outer area A1 of the network, it is found that spiral wave, target wave could be developed in the network under appropriate feedback gain with diversity and size of controlled area. Particularly, homogeneous state could be reached after synchronization by selecting appropriate feedback gain and controlled size in the network. Finally, the distribution for statistical factors of synchronization is calculated in the two-parameter space to understand the transition of pattern region. It is found that developed spiral waves, target waves often are associated with smaller factor of synchronization. These results show that emergence of sustained spiral wave and continuous target wave could be effective for further suppression of spatiotemporal chaos in network by generating stable pacemaker completely. PMID:27119986

  17. Failure tolerance of spike phase synchronization in coupled neural networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2011-09-01

    Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

  18. Pattern Selection in Network of Coupled Multi-Scroll Attractors.

    PubMed

    Li, Fan; Ma, Jun

    2016-01-01

    Multi-scroll chaotic attractor makes the oscillator become more complex in dynamic behaviors. The collective behaviors of coupled oscillators with multi-scroll attractors are investigated in the regular network in two-dimensional array, which the local kinetics is described by an improved Chua circuit. A feasible scheme of negative feedback with diversity is imposed on the network to stabilize the spatial patterns. Firstly, the Chua circuit is improved by replacing the nonlinear term with Sine function to generate infinite aquariums so that multi-scroll chaotic attractors could be generated under appropriate parameters, which could be detected by calculating the Lyapunov exponent in the parameter region. Furthermore, negative feedback with different gains (D1, D2) is imposed on the local square center area A2 and outer area A1 of the network, it is found that spiral wave, target wave could be developed in the network under appropriate feedback gain with diversity and size of controlled area. Particularly, homogeneous state could be reached after synchronization by selecting appropriate feedback gain and controlled size in the network. Finally, the distribution for statistical factors of synchronization is calculated in the two-parameter space to understand the transition of pattern region. It is found that developed spiral waves, target waves often are associated with smaller factor of synchronization. These results show that emergence of sustained spiral wave and continuous target wave could be effective for further suppression of spatiotemporal chaos in network by generating stable pacemaker completely.

  19. Pattern Selection in Network of Coupled Multi-Scroll Attractors.

    PubMed

    Li, Fan; Ma, Jun

    2016-01-01

    Multi-scroll chaotic attractor makes the oscillator become more complex in dynamic behaviors. The collective behaviors of coupled oscillators with multi-scroll attractors are investigated in the regular network in two-dimensional array, which the local kinetics is described by an improved Chua circuit. A feasible scheme of negative feedback with diversity is imposed on the network to stabilize the spatial patterns. Firstly, the Chua circuit is improved by replacing the nonlinear term with Sine function to generate infinite aquariums so that multi-scroll chaotic attractors could be generated under appropriate parameters, which could be detected by calculating the Lyapunov exponent in the parameter region. Furthermore, negative feedback with different gains (D1, D2) is imposed on the local square center area A2 and outer area A1 of the network, it is found that spiral wave, target wave could be developed in the network under appropriate feedback gain with diversity and size of controlled area. Particularly, homogeneous state could be reached after synchronization by selecting appropriate feedback gain and controlled size in the network. Finally, the distribution for statistical factors of synchronization is calculated in the two-parameter space to understand the transition of pattern region. It is found that developed spiral waves, target waves often are associated with smaller factor of synchronization. These results show that emergence of sustained spiral wave and continuous target wave could be effective for further suppression of spatiotemporal chaos in network by generating stable pacemaker completely. PMID:27119986

  20. Chaos in generically coupled phase oscillator networks with nonpairwise interactions

    NASA Astrophysics Data System (ADS)

    Bick, Christian; Ashwin, Peter; Rodrigues, Ana

    2016-09-01

    The Kuramoto-Sakaguchi system of coupled phase oscillators, where interaction between oscillators is determined by a single harmonic of phase differences of pairs of oscillators, has very simple emergent dynamics in the case of identical oscillators that are globally coupled: there is a variational structure that means the only attractors are full synchrony (in-phase) or splay phase (rotating wave/full asynchrony) oscillations and the bifurcation between these states is highly degenerate. Here we show that nonpairwise coupling—including three and four-way interactions of the oscillator phases—that appears generically at the next order in normal-form based calculations can give rise to complex emergent dynamics in symmetric phase oscillator networks. In particular, we show that chaos can appear in the smallest possible dimension of four coupled phase oscillators for a range of parameter values.

  1. Coupled disease-behavior dynamics on complex networks: A review

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  2. Coupled disease-behavior dynamics on complex networks: A review.

    PubMed

    Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  3. Emerging dynamics in neuronal networks of diffusively coupled hard oscillators.

    PubMed

    Ponta, L; Lanza, V; Bonnin, M; Corinto, F

    2011-06-01

    Oscillatory networks are a special class of neural networks where each neuron exhibits time periodic behavior. They represent bio-inspired architectures which can be exploited to model biological processes such as the binding problem and selective attention. In this paper we investigate the dynamics of networks whose neurons are hard oscillators, namely they exhibit the coexistence of different stable attractors. We consider a constant external stimulus applied to each neuron, which influences the neuron's own natural frequency. We show that, due to the interaction between different kinds of attractors, as well as between attractors and repellors, new interesting dynamics arises, in the form of synchronous oscillations of various amplitudes. We also show that neurons subject to different stimuli are able to synchronize if their couplings are strong enough.

  4. G-protein-coupled receptors in intestinal chemosensation.

    PubMed

    Reimann, Frank; Tolhurst, Gwen; Gribble, Fiona M

    2012-04-01

    Food intake is detected by the chemical senses of taste and smell and subsequently by chemosensory cells in the gastrointestinal tract that link the composition of ingested foods to feedback circuits controlling gut motility/secretion, appetite, and peripheral nutrient disposal. G-protein-coupled receptors responsive to a range of nutrients and other food components have been identified, and many are localized to intestinal chemosensory cells, eliciting hormonal and neuronal signaling to the brain and periphery. This review examines the role of G-protein-coupled receptors as signaling molecules in the gut, with a particular focus on pathways relevant to appetite and glucose homeostasis. PMID:22482725

  5. Molecular pharmacology of G protein-coupled receptors.

    PubMed

    Summers, R J

    2016-10-01

    This themed issue of the British Journal of Pharmacology stems from the eighth in the series of meetings on the Molecular Pharmacology of G protein coupled receptors (MPGPCR) held as part of a joint meeting with the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists (ASCEPT) in Melbourne Australia from 7 to 11 December 2014. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc. PMID:27682321

  6. Molecular pharmacology of G protein-coupled receptors.

    PubMed

    Summers, R J

    2016-10-01

    This themed issue of the British Journal of Pharmacology stems from the eighth in the series of meetings on the Molecular Pharmacology of G protein coupled receptors (MPGPCR) held as part of a joint meeting with the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists (ASCEPT) in Melbourne Australia from 7 to 11 December 2014. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.

  7. From biological and social network metaphors to coupled bio-social wireless networks

    PubMed Central

    Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

    2010-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  8. Tetramer formation in Arabidopsis MADS domain proteins: analysis of a protein-protein interaction network

    PubMed Central

    2014-01-01

    Background MADS domain proteins are transcription factors that coordinate several important developmental processes in plants. These proteins interact with other MADS domain proteins to form dimers, and it has been proposed that they are able to associate as tetrameric complexes that regulate transcription of target genes. Whether the formation of functional tetramers is a widespread property of plant MADS domain proteins, or it is specific to few of these transcriptional regulators remains unclear. Results We analyzed the structure of the network of physical interactions among MADS domain proteins in Arabidopsis thaliana. We determined the abundance of subgraphs that represent the connection pattern expected for a MADS domain protein heterotetramer. These subgraphs were significantly more abundant in the MADS domain protein interaction network than in randomized analogous networks. Importantly, these subgraphs are not significantly frequent in a protein interaction network of TCP plant transcription factors, when compared to expectation by chance. In addition, we found that MADS domain proteins in tetramer-like subgraphs are more likely to be expressed jointly than proteins in other subgraphs. This effect is mainly due to proteins in the monophyletic MIKC clade, as there is no association between tetramer-like subgraphs and co-expression for proteins outside this clade. Conclusions Our results support that the tendency to form functional tetramers is widespread in the MADS domain protein-protein interaction network. Our observations also suggest that this trend is prevalent, or perhaps exclusive, for proteins in the MIKC clade. Because it is possible to retrodict several experimental results from our analyses, our work can be an important aid to make new predictions and facilitates experimental research on plant MADS domain proteins. PMID:24468197

  9. Applications of molecular replacement to G protein-coupled receptors

    SciTech Connect

    Kruse, Andrew C.; Manglik, Aashish; Kobilka, Brian K.; Weis, William I.

    2013-11-01

    The use of molecular replacement in solving the structures of G protein-coupled receptors is discussed, with specific examples being described in detail. G protein-coupled receptors (GPCRs) are a large class of integral membrane proteins involved in regulating virtually every aspect of human physiology. Despite their profound importance in human health and disease, structural information regarding GPCRs has been extremely limited until recently. With the advent of a variety of new biochemical and crystallographic techniques, the structural biology of GPCRs has advanced rapidly, offering key molecular insights into GPCR activation and signal transduction. To date, almost all GPCR structures have been solved using molecular-replacement techniques. Here, the unique aspects of molecular replacement as applied to individual GPCRs and to signaling complexes of these important proteins are discussed.

  10. Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions

    PubMed Central

    Burgard, Anthony P.; Nikolaev, Evgeni V.; Schilling, Christophe H.; Maranas, Costas D.

    2004-01-01

    In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v1 and v2, are (1) directionally coupled, if a non-zero flux for v1 implies a non-zero flux for v2 but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v1 implies a non-zero, though variable, flux for v2 and vice versa; or (3) fully coupled, if a non-zero flux for v1 implies not only a non-zero but also a fixed flux for v2 and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations. PMID:14718379

  11. Epidemic spreading on one-way-coupled networks

    NASA Astrophysics Data System (ADS)

    Wang, Lingna; Sun, Mengfeng; Chen, Shanshan; Fu, Xinchu

    2016-09-01

    Numerous real-world networks (e.g., social, communicational, and biological networks) have been observed to depend on each other, and this results in interconnected networks with different topology structures and dynamics functions. In this paper, we focus on the scenario of epidemic spreading on one-way-coupled networks comprised of two subnetworks, which can manifest the transmission of some zoonotic diseases. By proposing a mathematical model through mean-field approximation approach, we prove the global stability of the disease-free and endemic equilibria of this model. Through the theoretical and numerical analysis, we obtain interesting results: the basic reproduction number R0 of the whole network is the maximum of the basic reproduction numbers of the two subnetworks; R0 is independent of the cross-infection rate and cross contact pattern; R0 increases rapidly with the growth of inner infection rate if the inner contact pattern is scale-free; in order to eradicate zoonotic diseases from human beings, we must simultaneously eradicate them from animals; bird-to-bird infection rate has bigger impact on the human's average infected density than bird-to-human infection rate.

  12. New roles of G protein-coupled receptor kinase 2 (GRK2) in cell migration

    PubMed Central

    Penela, Petronila; Ribas, Catalina; Aymerich, Ivette

    2009-01-01

    G protein-coupled receptor kinase 2 (GRK2) was initially identified as a key player, together with β-arrestins, in the regulation of multiple G protein-coupled receptors (GPCR). Further research has revealed a complex GRK2 interactome, that includes a variety of proteins related to cell motility, and a role for GRK2 kinase activity in inhibiting chemokine-induced immune cell migration. In addition, we have recently reported that GRK2 positively regulates integrin and sphingosine-1-phosphate-dependent motility in epithelial cell types and fibroblasts, acting as a scaffold molecule. We suggest that the positive or negative correlation of GRK2 levels with cell migration would depend on the cell type, specific stimuli acting through plasma membrane receptors, or on the signalling context, leading to differential networks of interaction of GRK2 with cell migration-related signalosomes. PMID:19372742

  13. Stability of the splay state in pulse-coupled networks

    SciTech Connect

    Zillmer, Ruediger; Livi, Roberto; Politi, Antonio; Torcini, Alessandro

    2007-10-15

    The stability of the dynamical states characterized by a uniform firing rate (splay states) is analyzed in a network of globally coupled leaky integrate-and-fire neurons. This is done by reducing the set of differential equations to a map that is investigated in the limit of large network size. We show that the stability of the splay state depends crucially on the ratio between the pulse width and the interspike interval. More precisely, the spectrum of Floquet exponents turns out to consist of three components: (i) one that coincides with the predictions of the mean-field analysis [Abbott and van Vreesvijk, Phys. Rev. E 48, 1483 (1993)], (ii) a component measuring the instability of 'finite-frequency' modes, (iii) a number of 'isolated' eigenvalues that are connected to the characteristics of the single pulse and may give rise to strong instabilities (the Floquet exponent being proportional to the network size). Finally, as a side result, we find that the splay state can be stable even for inhibitory coupling.

  14. Changes in G protein-coupled receptor sorting protein affinity regulate postendocytic targeting of G protein-coupled receptors.

    PubMed

    Thompson, Dawn; Pusch, Margareta; Whistler, Jennifer L

    2007-10-01

    After activation, most G protein-coupled receptors (GPCRs) are regulated by a cascade of events involving desensitization and endocytosis. Internalized receptors can then be recycled to the plasma membrane, retained in an endosomal compartment, or targeted for degradation. The GPCR-associated sorting protein, GASP, has been shown to preferentially sort a number of native GPCRs to the lysosome for degradation after endocytosis. Here we show that a mutant beta(2) adrenergic receptor and a mutant mu opioid receptor that have previously been described as lacking "recycling signals" due to mutations in their C termini in fact bind to GASP and are targeted for degradation. We also show that a mutant dopamine D1 receptor, which has likewise been described as lacking a recycling signal, does not bind to GASP and is therefore not targeted for degradation. Together, these results indicate that alteration of receptors in their C termini can expose determinants with affinity for GASP binding and consequently target receptors for degradation.

  15. Metal ion coupled protein folding and allosteric motions

    NASA Astrophysics Data System (ADS)

    Wang, Wei

    2014-03-01

    Many proteins need the help of cofactors for their successful folding and functioning. Metal ions, i.e., Zn2+, Ca2+, and Mg2+ etc., are typical biological cofactors. Binding of metal ions can reshape the energy landscapes of proteins, thereby modifying the folding and allosteric motions. For example, such binding may make the intrinsically disordered proteins have funneled energy landscapes, consequently, ensures their spontaneous folding. In addition, the binding may activate certain biological processes by inducing related conformational changes of regulation proteins. However, how the local interactions involving the metal ion binding can induce the global conformational motions of proteins remains elusive. Investigating such question requires multiple models with different details, including quantum mechanics, atomistic models, and coarse grained models. In our recent work, we have been developing such multiscale methods which can reasonably model the metal ion binding induced charge transfer, protonation/deprotonation, and large conformational motions of proteins. With such multiscale model, we elucidated the zinc-binding induced folding mechanism of classical zinc finger and the calcium-binding induced dynamic symmetry breaking in the allosteric motions of calmodulin. In addition, we studied the coupling of folding, calcium binding and allosteric motions of calmodulin domains. In this talk, I will introduce the above progresses on the metal ion coupled protein folding and allosteric motions. We thank the finacial support from NSFC and the 973 project.

  16. Experimental multistable states for small network of coupled pendula.

    PubMed

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-01-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton's laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems. PMID:27445038

  17. Experimental multistable states for small network of coupled pendula

    PubMed Central

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-01-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton’s laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems. PMID:27445038

  18. Experimental multistable states for small network of coupled pendula.

    PubMed

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-07-21

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton's laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems.

  19. Experimental multistable states for small network of coupled pendula

    NASA Astrophysics Data System (ADS)

    Dudkowski, Dawid; Grabski, Juliusz; Wojewoda, Jerzy; Perlikowski, Przemyslaw; Maistrenko, Yuri; Kapitaniak, Tomasz

    2016-07-01

    Chimera states are dynamical patterns emerging in populations of coupled identical oscillators where different groups of oscillators exhibit coexisting synchronous and incoherent behaviors despite homogeneous coupling. Although these states are typically observed in the large ensembles of oscillators, recently it has been shown that so-called weak chimera states may occur in the systems with small numbers of oscillators. Here, we show that similar multistable states demonstrating partial frequency synchronization, can be observed in simple experiments with identical mechanical oscillators, namely pendula. The mathematical model of our experiment shows that the observed multistable states are controlled by elementary dynamical equations, derived from Newton’s laws that are ubiquitous in many physical and engineering systems. Our finding suggests that multistable chimera-like states are observable in small networks relevant to various real-world systems.

  20. Discriminating lysosomal membrane protein types using dynamic neural network.

    PubMed

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

    This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.

  1. Multifractal analysis of the coupling space of feedforward neural networks

    NASA Astrophysics Data System (ADS)

    Engel, A.; Weigt, M.

    1996-03-01

    Random input patterns induce a partition of the coupling space of feedforward neural networks into different cells according to the generated output sequence. For the perceptron this partition forms a random multifractal for which the spectrum f(α) can be calculated analytically using the replica trick. A phase transition in the multifractal spectrum corresponds to the crossover from percolating to nonpercolating cell sizes. Instabilities of negative moments are related to the Vapnik-Chervonenkis (VC) dimension

    [Theor. Prob. Appl. 16, 264 (1971)]
    .

  2. Data Synchronization in a Network of Coupled Phase Oscillators

    NASA Astrophysics Data System (ADS)

    Miyano, Takaya; Tsutsui, Takako

    2007-01-01

    We devised a new method of data mining for a large-scale database. In the method, a network of locally coupled phase oscillators subject to Kuramoto’s model substitutes for given multivariate data to generate major features through phase locking of the oscillators, i.e., phase transition of the data set. We applied the method to the national database of care needs certification for the Japanese public long-term care insurance program, and found three major patterns in the aging process of the frail elderly. This work revealed the latent utility of Kuramoto’s model for data processing.

  3. Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

    SciTech Connect

    Tang, Longkun E-mail: xqwu@whu.edu.cn; Wu, Xiaoqun E-mail: xqwu@whu.edu.cn; Lu, Jun-an; Lü, Jinhu

    2015-03-15

    Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) The coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.

  4. Comparing protein interaction networks via a graph match-and-split algorithm.

    PubMed

    Narayanan, Manikandan; Karp, Richard M

    2007-09-01

    We present a method that compares the protein interaction networks of two species to detect functionally similar (conserved) protein modules between them. The method is based on an algorithm we developed to identify matching subgraphs between two graphs. Unlike previous network comparison methods, our algorithm has provable guarantees on correctness and efficiency. Our algorithm framework also admits quite general criteria that define when two subgraphs match and constitute a conserved module. We apply our method to pairwise comparisons of the yeast protein network with the human, fruit fly and nematode worm protein networks, using a lenient criterion based on connectedness and matching edges, coupled with a clustering heuristic. In evaluations of the detected conserved modules against reference yeast protein complexes, our method performs competitively with and sometimes better than two previous network comparison methods. Further, under some conditions (proper homolog and species selection), our method performs better than a popular single-species clustering method. Beyond these evaluations, we discuss the biology of a couple of conserved modules detected by our method. We demonstrate the utility of network comparison for transferring annotations from yeast proteins to human ones, and validate the predicted annotations. Supplemental text is available at www.cs.berkeley.edu/ approximately nmani/M-and-S/supplement.pdf. PMID:17803369

  5. Synchronization in slowly switching networks of coupled oscillators

    PubMed Central

    Zhou, Jie; Zou, Yong; Guan, Shuguang; Liu, Zonghua; Boccaletti, S.

    2016-01-01

    Networks whose structure of connections evolves in time constitute a big challenge in the study of synchronization, in particular when the time scales for the evolution of the graph topology are comparable with (or even longer than) those pertinent to the units’ dynamics. We here focus on networks with a slow-switching structure, and show that the necessary conditions for synchronization, i.e. the conditions for which synchronization is locally stable, are determined by the time average of the largest Lyapunov exponents of transverse modes of the switching topologies. Comparison between fast- and slow-switching networks allows elucidating that slow-switching processes prompt synchronization in the cases where the Master Stability Function is concave, whereas fast-switching schemes facilitate synchronization for convex curves. Moreover, the condition of slow-switching enables the introduction of a control strategy for inducing synchronization in networks with arbitrary structure and coupling strength, which is of evident relevance for broad applications in real world systems. PMID:27779253

  6. Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

    PubMed Central

    Abrams, Jennifer L.; Morano, Kevin A.

    2013-01-01

    Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must be maintained to avoid deleterious consequences 1. External or internal factors that disrupt this balance can lead to protein aggregation, toxicity and cell death. In humans this is a major contributing factor to the symptoms associated with neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases 10. It is therefore essential that the proteins involved in maintenance of proteostasis be identified in order to develop treatments for these debilitating diseases. This article describes techniques for monitoring in vivo protein folding at near-real time resolution using the model protein firefly luciferase fused to green fluorescent protein (FFL-GFP). FFL-GFP is a unique model chimeric protein as the FFL moiety is extremely sensitive to stress-induced misfolding and aggregation, which inactivates the enzyme 12. Luciferase activity is monitored using an enzymatic assay, and the GFP moiety provides a method of visualizing soluble or aggregated FFL using automated microscopy. These coupled methods incorporate two parallel and technically independent approaches to analyze both refolding and functional reactivation of an enzyme after stress. Activity recovery can be directly correlated with kinetics of disaggregation and re-solubilization to better understand how protein quality control factors such as protein chaperones collaborate to perform these functions. In addition, gene deletions or mutations can be used to test contributions of specific proteins or protein subunits to this process. In this article we examine the contributions of the protein disaggregase Hsp104 13, known to partner with the Hsp40/70/nucleotide exchange factor (NEF) refolding system 5, to protein refolding to validate this approach. PMID:23892247

  7. Dynamical network of residue–residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation

    PubMed Central

    Doshi, Urmi; Holliday, Michael J.; Eisenmesser, Elan Z.; Hamelberg, Donald

    2016-01-01

    Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue–residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general. PMID:27071107

  8. Modeling of price and profit in coupled-ring networks

    NASA Astrophysics Data System (ADS)

    Tangmongkollert, Kittiwat; Suwanna, Sujin

    2016-06-01

    We study the behaviors of magnetization, price, and profit profiles in ring networks in the presence of the external magnetic field. The Ising model is used to determine the state of each node, which is mapped to the buy-or-sell state in a financial market, where +1 is identified as the buying state, and -1 as the selling state. Price and profit mechanisms are modeled based on the assumption that price should increase if demand is larger than supply, and it should decrease otherwise. We find that the magnetization can be induced between two rings via coupling links, where the induced magnetization strength depends on the number of the coupling links. Consequently, the price behaves linearly with time, where its rate of change depends on the magnetization. The profit grows like a quadratic polynomial with coefficients dependent on the magnetization. If two rings have opposite direction of net spins, the price flows in the direction of the majority spins, and the network with the minority spins gets a loss in profit.

  9. Evolution of biomolecular networks: lessons from metabolic and protein interactions.

    PubMed

    Yamada, Takuji; Bork, Peer

    2009-11-01

    Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

  10. Protein function prediction using neighbor relativity in protein-protein interaction network.

    PubMed

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network.

  11. FunCoup 3.0: database of genome-wide functional coupling networks.

    PubMed

    Schmitt, Thomas; Ogris, Christoph; Sonnhammer, Erik L L

    2014-01-01

    We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.

  12. Coupled protein diffusion and folding in the cell.

    PubMed

    Guo, Minghao; Gelman, Hannah; Gruebele, Martin

    2014-01-01

    When a protein unfolds in the cell, its diffusion coefficient is affected by its increased hydrodynamic radius and by interactions of exposed hydrophobic residues with the cytoplasmic matrix, including chaperones. We characterize protein diffusion by photobleaching whole cells at a single point, and imaging the concentration change of fluorescent-labeled protein throughout the cell as a function of time. As a folded reference protein we use green fluorescent protein. The resulting region-dependent anomalous diffusion is well characterized by 2-D or 3-D diffusion equations coupled to a clustering algorithm that accounts for position-dependent diffusion. Then we study diffusion of a destabilized mutant of the enzyme phosphoglycerate kinase (PGK) and of its stable control inside the cell. Unlike the green fluorescent protein control's diffusion coefficient, PGK's diffusion coefficient is a non-monotonic function of temperature, signaling 'sticking' of the protein in the cytosol as it begins to unfold. The temperature-dependent increase and subsequent decrease of the PGK diffusion coefficient in the cytosol is greater than a simple size-scaling model suggests. Chaperone binding of the unfolding protein inside the cell is one plausible candidate for even slower diffusion of PGK, and we test the plausibility of this hypothesis experimentally, although we do not rule out other candidates.

  13. Cross-frequency coupling in real and virtual brain networks

    PubMed Central

    Jirsa, Viktor; Müller, Viktor

    2013-01-01

    Information processing in the brain is thought to rely on the convergence and divergence of oscillatory behaviors of widely distributed brain areas. This information flow is captured in its simplest form via the concepts of synchronization and desynchronization and related metrics. More complex forms of information flow are transient synchronizations and multi-frequency behaviors with metrics related to cross-frequency coupling (CFC). It is supposed that CFC plays a crucial role in the organization of large-scale networks and functional integration across large distances. In this study, we describe different CFC measures and test their applicability in simulated and real electroencephalographic (EEG) data obtained during resting state. For these purposes, we derive generic oscillator equations from full brain network models. We systematically model and simulate the various scenarios of CFC under the influence of noise to obtain biologically realistic oscillator dynamics. We find that (i) specific CFC-measures detect correctly in most cases the nature of CFC under noise conditions, (ii) bispectrum (BIS) and bicoherence (BIC) correctly detect the CFCs in simulated data, (iii) empirical resting state EEG show a prominent delta-alpha CFC as identified by specific CFC measures and the more classic BIS and BIC. This coupling was mostly asymmetric (directed) and generally higher in the eyes closed (EC) than in the eyes open (EO) condition. In conjunction, these two sets of measures provide a powerful toolbox to reveal the nature of couplings from experimental data and as such allow inference on the brain state dependent information processing. Methodological advantages of using CFC measures and theoretical significance of delta and alpha interactions during resting and other brain states are discussed. PMID:23840188

  14. Protein-protein interaction network-based detection of functionally similar proteins within species.

    PubMed

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent.

  15. Protein-induced bilayer perturbations: Lipid ordering and hydrophobic coupling.

    PubMed

    Petersen, Frederic N R; Laursen, Ib; Bohr, Henrik; Nielsen, Claus Hélix

    2009-10-01

    The host lipid bilayer is increasingly being recognized as an important non-specific regulator of membrane protein function. Despite considerable progress the interplay between hydrophobic coupling and lipid ordering is still elusive. We use electron spin resonance (ESR) to study the interaction between the model protein gramicidin and lipid bilayers of varying thickness. The free energy of the interaction is up to -6kJ/mol; thus not strongly favored over lipid-lipid interactions. Incorporation of gramicidin results in increased order parameters with increased protein concentration and hydrophobic mismatch. Our findings also show that at high protein:lipid ratios the lipids are motionally restricted but not completely immobilized. Both exchange on and off rate values for the lipid<-->gramicidin interaction are lowest at optimal hydrophobic matching. Hydrophobic mismatch of few A results in up to 10-fold increased exchange rates as compared to the 'optimal' match situation pointing to the regulatory role of hydrophobic coupling in lipid-protein interactions.

  16. Protein-induced bilayer perturbations: Lipid ordering and hydrophobic coupling

    SciTech Connect

    Petersen, Frederic N.R.; Laursen, Ib; Bohr, Henrik; Nielsen, Claus Helix

    2009-10-02

    The host lipid bilayer is increasingly being recognized as an important non-specific regulator of membrane protein function. Despite considerable progress the interplay between hydrophobic coupling and lipid ordering is still elusive. We use electron spin resonance (ESR) to study the interaction between the model protein gramicidin and lipid bilayers of varying thickness. The free energy of the interaction is up to -6 kJ/mol; thus not strongly favored over lipid-lipid interactions. Incorporation of gramicidin results in increased order parameters with increased protein concentration and hydrophobic mismatch. Our findings also show that at high protein:lipid ratios the lipids are motionally restricted but not completely immobilized. Both exchange on and off rate values for the lipid {r_reversible} gramicidin interaction are lowest at optimal hydrophobic matching. Hydrophobic mismatch of few A results in up to 10-fold increased exchange rates as compared to the 'optimal' match situation pointing to the regulatory role of hydrophobic coupling in lipid-protein interactions.

  17. Novel Allosteric Modulators of G Protein-coupled Receptors*

    PubMed Central

    Gentry, Patrick R.; Sexton, Patrick M.; Christopoulos, Arthur

    2015-01-01

    G protein-coupled receptors (GPCRs) are allosteric proteins, because their signal transduction relies on interactions between topographically distinct, yet conformationally linked, domains. Much of the focus on GPCR allostery in the new millennium, however, has been on modes of targeting GPCR allosteric sites with chemical probes due to the potential for novel therapeutics. It is now apparent that some GPCRs possess more than one targetable allosteric site, in addition to a growing list of putative endogenous modulators. Advances in structural biology are also shedding new insights into mechanisms of allostery, although the complexities of candidate allosteric drugs necessitate rigorous biological characterization. PMID:26100627

  18. Global multiple protein-protein interaction network alignment by combining pairwise network alignments

    PubMed Central

    2015-01-01

    Background A wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment. Results We propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments. Conclusions A key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster

  19. Dynamic interactions of proteins in complex networks

    SciTech Connect

    Appella, E.; Anderson, C.

    2009-10-01

    Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact with the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

  20. Dynamic optimization of metabolic networks coupled with gene expression.

    PubMed

    Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

    2015-01-21

    The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.

  1. Detecting overlapping protein complexes by rough-fuzzy clustering in protein-protein interaction networks.

    PubMed

    Wu, Hao; Gao, Lin; Dong, Jihua; Yang, Xiaofei

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks.

  2. Hybrid dynamics in delay-coupled swarms with ``mothership'' networks

    NASA Astrophysics Data System (ADS)

    Hindes, Jason; Schwartz, Ira

    Swarming behavior continues to be a subject of immense interest because of its centrality in many naturally occurring systems in biology and physics. Moreover, the development of autonomous mobile agents that can mimic the behavior of swarms and can be engineered to perform complex tasks without constant intervention is a very active field of practical research. Here we examine the effects on delay-coupled swarm pattern formation from the inclusion of a small fraction of highly connected nodes, ``motherships'', in the swarm interaction network. We find a variety of new behaviors and bifurcations, including new hybrid motions of previously analyzed patterns. Both numerical and analytic techniques are used to classify the dynamics and construct the phase diagram. The implications for swarm control and robustness from topological heterogeneity are also discussed. This research was funded by the office of Naval Research (ONR), and was performed while JH held a National Research Council Research Associateship Award.

  3. Information Filtering via Biased Random Walk on Coupled Social Network

    PubMed Central

    Dong, Qiang; Fu, Yan

    2014-01-01

    The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. PMID:25147867

  4. A Neural Network Reconstruction of the Coupled Inner Magnetospheric Environment

    NASA Astrophysics Data System (ADS)

    Bortnik, J.; Li, W.; Thorne, R. M.; Yue, C.; Chu, X.; Angelopoulos, V.; Blum, L. W.; Ma, Q.; Kletzing, C.; Reeves, G. D.; Spence, H. E.

    2015-12-01

    The Earth's inner magnetosphere represents a dynamic and highly coupled system that is often challenging for physical models to reproduce accurately, but knowledge of this system and its spatiotemporal evolution is crucial for both practical applications in the form of space weather, and scientific insight. In this presentation, we demonstrate the use of a deep neural network in predicting and specifying the global, time-varying distributions of a number of key wave and particle populations including chorus, hiss and magnetosonic wave, and electrion distributions from cold plasma to plasmasheet energies and right through the relativistic and ultra-relativistic populations. We show the temporal and spatial relationships between these waves and particle populations that ultimately lead to the dynamics of the ultra-relativistic particles and discuss implications.

  5. Application of BRET for studying G protein-coupled receptors.

    PubMed

    Kaczor, Agnieszka A; Makarska-Bialokoz, Magdalena; Selent, Jana; de la Fuente, Rocío A; Martí-Solano, Maria; Castro, Marián

    2014-05-01

    G protein-coupled receptors (GPCRs) constitute one of the largest classes of cell surface receptors. GPCR biology has been a subject of widespread interest owing to the functional relevance of these receptors and their potential importance in the development of new drugs. At present, over 30% of all launched drugs target these receptors. GPCRs have been considered for a long time to function as monomeric entities and the idea of GPCR dimerization and oligomerization was initially accepted with disbelief. However, a significant amount of experimental and molecular modeling evidence accumulated during the last several years suggests that the process of GPCRs dimer or oligomer formation is a general phenomenon, in some cases even essential for receptor function. Among the many methods to study GPCR dimerization and oligomerization, modern biophysical techniques such as those based on resonance energy transfer (RET) and particularly bioluminescence resonance energy transfer (BRET) have played a leading role. RET methods are commonly applied as non-destructive indicators of specific protein-protein interactions (PPIs) in living cells. Data from numerous BRET experiments support the idea that the process of GPCR oligomerization may be relevant in many physiological and pathological conditions. The application of BRET to the study of GPCRs is not only limited to the assessment of receptor oligomerization but also expands to the investigation of the interactions of GPCRs with other proteins, including G proteins, G protein-coupled receptor kinases, β-arrestins or receptor tyrosine kinases, as well as to the characterization of GPCR activation and signaling. In this review, we briefly summarize the fundaments of BRET, discuss new trends in this technology and describe the wide range of applications of BRET to study GPCRs.

  6. Surface plasmon resonance applied to G protein-coupled receptors

    PubMed Central

    Locatelli-Hoops, Silvia; Yeliseev, Alexei A.; Gawrisch, Klaus; Gorshkova, Inna

    2013-01-01

    G protein-coupled receptors (GPCR) are integral membrane proteins that transmit signals from external stimuli to the cell interior via activation of GTP-binding proteins (G proteins) thereby mediating key sensorial, hormonal, metabolic, immunological, and neurotransmission processes. Elucidating their structure and mechanism of interaction with extracellular and intracellular binding partners is of fundamental importance and highly relevant to rational design of new effective drugs. Surface plasmon resonance (SPR) has become a method of choice for studying biomolecular interactions at interfaces because measurements take place in real-time and do not require labeling of any of the interactants. However, due to the particular challenges imposed by the high hydrophobicity of membrane proteins and the great diversity of receptor-stimulating ligands, the application of this technique to characterize interactions of GPCR is still in the developmental phase. Here we give an overview of the principle of SPR and analyze current approaches for the preparation of the sensor chip surface, capture and stabilization of GPCR, and experimental design to characterize their interaction with ligands, G proteins and specific antibodies. PMID:24466506

  7. Cell-Free Expression of G Protein-Coupled Receptors.

    PubMed

    Segers, Kenneth; Masure, Stefan

    2015-01-01

    The large-scale production of recombinant G protein-coupled receptors (GPCRs) is one of the major bottlenecks that hamper functional and structural studies of this important class of integral membrane proteins. Heterologous overexpression of GPCRs often results in low yields of active protein, usually due to a combination of several factors, such as low expression levels, protein insolubility, host cell toxicity, and the need to use harsh and often denaturing detergents (e.g., SDS, LDAO, OG, and DDM, among others) to extract the recombinant receptor from the host cell membrane. Many of these problematic issues are inherently linked to cell-based expression systems and can therefore be circumvented by the use of cell-free systems. In this unit, we provide a range of protocols for the production of GPCRs in a cell-free expression system. Using this system, we typically obtain GPCR expression levels of ∼1 mg per ml of reaction mixture in the continuous-exchange configuration. Although the protocols in this unit have been optimized for the cell-free expression of GPCRs, they should provide a good starting point for the production of other classes of membrane proteins, such as ion channels, aquaporins, carrier proteins, membrane-bound enzymes, and even large molecular complexes.

  8. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  9. PDZ Protein Regulation of G Protein-Coupled Receptor Trafficking and Signaling Pathways.

    PubMed

    Dunn, Henry A; Ferguson, Stephen S G

    2015-10-01

    G protein-coupled receptors (GPCRs) contribute to the regulation of every aspect of human physiology and are therapeutic targets for the treatment of numerous diseases. As a consequence, understanding the myriad of mechanisms controlling GPCR signaling and trafficking is essential for the development of new pharmacological strategies for the treatment of human pathologies. Of the many GPCR-interacting proteins, postsynaptic density protein of 95 kilodaltons, disc large, zona occludens-1 (PDZ) domain-containing proteins appear most abundant and have similarly been implicated in disease mechanisms. PDZ proteins play an important role in regulating receptor and channel protein localization within synapses and tight junctions and function to scaffold intracellular signaling protein complexes. In the current study, we review the known functional interactions between PDZ domain-containing proteins and GPCRs and provide insight into the potential mechanisms of action. These PDZ domain-containing proteins include the membrane-associated guanylate-like kinases [postsynaptic density protein of 95 kilodaltons; synapse-associated protein of 97 kilodaltons; postsynaptic density protein of 93 kilodaltons; synapse-associated protein of 102 kilodaltons; discs, large homolog 5; caspase activation and recruitment domain and membrane-associated guanylate-like kinase domain-containing protein 3; membrane protein, palmitoylated 3; calcium/calmodulin-dependent serine protein kinase; membrane-associated guanylate kinase protein (MAGI)-1, MAGI-2, and MAGI-3], Na(+)/H(+) exchanger regulatory factor proteins (NHERFs) (NHERF1, NHERF2, PDZ domain-containing kidney protein 1, and PDZ domain-containing kidney protein 2), Golgi-associated PDZ proteins (Gα-binding protein interacting protein, C-terminus and CFTR-associated ligand), PDZ domain-containing guanine nucleotide exchange factors (GEFs) 1 and 2, regulator of G protein signaling (RGS)-homology-RhoGEFs (PDZ domain-containing RhoGEF and

  10. Quantifying phase-amplitude coupling in neuronal network oscillations.

    PubMed

    Onslow, Angela C E; Bogacz, Rafal; Jones, Matthew W

    2011-03-01

    Neuroscience time series data from a range of techniques and species reveal complex, non-linear interactions between different frequencies of neuronal network oscillations within and across brain regions. Here, we briefly review the evidence that these nested, cross-frequency interactions act in concert with linearly covariant (within-frequency) activity to dynamically coordinate functionally related neuronal ensembles during behaviour. Such studies depend upon reliable quantification of cross-frequency coordination, and we compare the properties of three techniques used to measure phase-amplitude coupling (PAC)--Envelope-to-Signal Correlation (ESC), the Modulation Index (MI) and Cross-Frequency Coherence (CFC)--by standardizing their filtering algorithms and systematically assessing their robustness to noise and signal amplitude using artificial signals. Importantly, we also introduce a freely-downloadable method for estimating statistical significance of PAC, a step overlooked in the majority of published studies. We find that varying data length and noise levels leads to the three measures differentially detecting false positives or correctly identifying frequency bands of interaction; these conditions should therefore be taken into careful consideration when selecting PAC analyses. Finally, we demonstrate the utility of the three measures in quantifying PAC in local field potential data simultaneously recorded from rat hippocampus and prefrontal cortex, revealing a novel finding of prefrontal cortical theta phase modulating hippocampal gamma power. Future adaptations that allow detection of time-variant PAC should prove essential in deciphering the roles of cross-frequency coupling in mediating or reflecting nervous system function.

  11. Protein Co-Expression Network Analysis (ProCoNA)

    SciTech Connect

    Gibbs, David L.; Baratt, Arie; Baric, Ralph; Kawaoka, Yoshihiro; Smith, Richard D.; Orwoll, Eric S.; Katze, Michael G.; Mcweeney, Shannon K.

    2013-06-01

    Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

  12. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor

    SciTech Connect

    Burg, John S.; Ingram, Jessica R.; Venkatakrishnan, A.J.; Jude, Kevin M.; Dukkipati, Abhiram; Feinberg, Evan N.; Angelini, Alessandro; Waghray, Deepa; Dror, Ron O.; Ploegh, Hidde L.; Garcia, K. Christopher

    2015-03-05

    Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor’s inactive state.

  13. Self-organized criticality in proteins: Hydropathic roughening profiles of G-protein-coupled receptors

    NASA Astrophysics Data System (ADS)

    Phillips, J. C.

    2013-03-01

    Proteins appear to be the most dramatic natural example of self-organized criticality (SOC), a concept that explains many otherwise apparently unlikely phenomena. Protein conformational functionality is often dominated by long-range hydrophobic or hydrophilic interactions which both drive protein compaction and mediate protein-protein interactions. Superfamily transmembrane G-protein-coupled receptors (GPCRs) are the largest family of proteins in the human genome; their amino acid sequences form the largest database for protein-membrane interactions. While there are now structural data on the heptad transmembrane structures of representatives of several heptad families, here we show how fresh insights into global and some local chemical trends in GPCR properties can be obtained accurately from sequences alone, especially by algebraically separating the extracellular and cytoplasmic loops from transmembrane segments. The global mediation of long-range water-protein interactions occurs in conjunction with modulation of these interactions by roughened interfaces. Hydropathic roughening profiles are defined here solely in terms of amino acid sequences, and knowledge of protein coordinates is not required. Roughening profiles both for GPCR and some simpler protein families display accurate and transparent connections to protein functionality, and identify natural length scales for protein functionality.

  14. Decoherence in a system of strongly coupled quantum oscillators. I. Symmetric network

    SciTech Connect

    Ponte, M.A. de; Oliveira, M.C. de; Moussa, M.H.Y.

    2004-08-01

    In this work we analyze the coherence dynamics and estimate decoherence times of quantum states in a network composed of N coupled dissipative quantum oscillators. We assume a symmetric network where all oscillators are coupled to each other with the same coupling strength. Master equations are derived for regimes of both weak and strong coupling between the oscillators. The strong coupling regime is characterized by the coupling strength between the oscillators or by the number of oscillators in the network. The decoherence times of particular states of the network are computed and the results are clarified by analyzing the processes of state swap and recurrence of reduced states of the network together with the linear entropies of the joint and reduced systems.

  15. Desensitization of G protein-coupled receptors and neuronal functions.

    PubMed

    Gainetdinov, Raul R; Premont, Richard T; Bohn, Laura M; Lefkowitz, Robert J; Caron, Marc G

    2004-01-01

    G protein-coupled receptors (GPCRs) have proven to be the most highly favorable class of drug targets in modern pharmacology. Over 90% of nonsensory GPCRs are expressed in the brain, where they play important roles in numerous neuronal functions. GPCRs can be desensitized following activation by agonists by becoming phosphorylated by members of the family of G protein-coupled receptor kinases (GRKs). Phosphorylated receptors are then bound by arrestins, which prevent further stimulation of G proteins and downstream signaling pathways. Discussed in this review are recent progress in understanding basics of GPCR desensitization, novel functional roles, patterns of brain expression, and receptor specificity of GRKs and beta arrestins in major brain functions. In particular, screening of genetically modified mice lacking individual GRKs or beta arrestins for alterations in behavioral and biochemical responses to cocaine and morphine has revealed a functional specificity in dopamine and mu-opioid receptor regulation of locomotion and analgesia. An important and specific role of GRKs and beta arrestins in regulating physiological responsiveness to psychostimulants and morphine suggests potential involvement of these molecules in certain brain disorders, such as addiction, Parkinson's disease, mood disorders, and schizophrenia. Furthermore, the utility of a pharmacological strategy aimed at targeting this GPCR desensitization machinery to regulate brain functions can be envisaged. PMID:15217328

  16. Topological Analyses of Protein-Ligand Binding: a Network Approach.

    PubMed

    Costanzi, Stefano

    2016-01-01

    Proteins can be conveniently represented as networks of interacting residues, thus allowing the study of several network parameters that can shed light onto several of their structural and functional aspects. With respect to the binding of ligands, which are central for the function of many proteins, network analysis may constitute a possible route to assist the identification of binding sites. As the bulk of this review illustrates, this has generally been easier for enzymes than for non-enzyme proteins, perhaps due to the different topological nature of the binding sites of the former over those of the latter. The article also illustrates how network representations of binding sites can be used to search PDB structures in order to identify proteins that bind similar molecules and, lastly, how codifying proteins as networks can assist the analysis of the conformational changes consequent to ligand binding.

  17. The architectural design of networks of protein domain architectures.

    PubMed

    Hsu, Chia-Hsin; Chen, Chien-Kuo; Hwang, Ming-Jing

    2013-08-23

    Protein domain architectures (PDAs), in which single domains are linked to form multiple-domain proteins, are a major molecular form used by evolution for the diversification of protein functions. However, the design principles of PDAs remain largely uninvestigated. In this study, we constructed networks to connect domain architectures that had grown out from the same single domain for every single domain in the Pfam-A database and found that there are three main distinctive types of these networks, which suggests that evolution can exploit PDAs in three different ways. Further analysis showed that these three different types of PDA networks are each adopted by different types of protein domains, although many networks exhibit the characteristics of more than one of the three types. Our results shed light on nature's blueprint for protein architecture and provide a framework for understanding architectural design from a network perspective.

  18. Heterodimerization and Surface Localization of G Protein Coupled Receptors

    PubMed Central

    Minneman, Kenneth P.

    2007-01-01

    G protein coupled receptors (GPCRs) are one of the largest human gene families, and are targets for many important therapeutic drugs. Over the last few years, there has been a major paradigm shift in our understanding of how these receptors function. Formerly, GPCRs were thought to exist as monomers that, upon agonist occupation, activated a heterotrimeric G protein to alter the concentrations of specific second messengers. Until recently, this relatively linear cascade has been the standard paradigm for signaling by these molecules. However, it is now clear that this model is not adequate to explain many aspects of GPCR function. We now know that many, if not most, GPCRs form homo- and/or hetero-oligomeric complexes and interact directly with intracellular proteins in addition to G proteins. It now appears that many GPCRs may not function independently, but might more accurately be described as subunits of large multi-protein signaling complexes. These observations raise many important new questions; some of which include: 1) How many functionally and pharmacologically distinct receptor subtypes exist in vivo? 2) Which GPCRs physically associate, and in what stochiometries? 3) What are the roles of individual subunits in binding ligand and activating responses? 4) Are the pharmacological or signaling properties of GPCR heterodimers different from monomers? Since these receptors are the targets for a large number of clinically useful compounds, such information is likely to be of direct therapeutic importance, both in understanding how existing drugs work, but also in discovering novel compounds to treat disease. PMID:17011524

  19. Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function: hypotheses and a comprehensive review.

    PubMed

    Csermely, Peter; Sandhu, Kuljeet Singh; Hazai, Eszter; Hoksza, Zsolt; Kiss, Huba J M; Miozzo, Federico; Veres, Dániel V; Piazza, Francesco; Nussinov, Ruth

    2012-02-01

    During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into 'cumulus-type', i.e., those similar to puffy (white) clouds, and 'stratus-type', i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an 'energy transfer' mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by 'multi-trajectories'; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach 'rarely visited' but functionally-related states. We also show the role of disorder in 'spatial games' of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.

  20. Phylogenetic analysis of modularity in protein interaction networks

    PubMed Central

    Erten, Sinan; Li, Xin; Bebek, Gurkan; Li, Jing; Koyutürk, Mehmet

    2009-01-01

    Background In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity. Results In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology. Conclusion These results establish

  1. Equilibrium fluctuation relations for voltage coupling in membrane proteins.

    PubMed

    Kim, Ilsoo; Warshel, Arieh

    2015-11-01

    A general theoretical framework is developed to account for the effects of an external potential on the energetics of membrane proteins. The framework is based on the free energy relation between two (forward/backward) probability densities, which was recently generalized to non-equilibrium processes, culminating in the work-fluctuation theorem. Starting from the probability densities of the conformational states along the "voltage coupling" reaction coordinate, we investigate several interconnected free energy relations between these two conformational states, considering voltage activation of ion channels. The free energy difference between the two conformational states at zero (depolarization) membrane potential (i.e., known as the chemical component of free energy change in ion channels) is shown to be equivalent to the free energy difference between the two "equilibrium" (resting and activated) conformational states along the one-dimensional voltage couplin reaction coordinate. Furthermore, the requirement that the application of linear response approximation to the free energy functionals of voltage coupling should satisfy the general free energy relations, yields a novel closed-form expression for the gating charge in terms of other basic properties of ion channels. This connection is familiar in statistical mechanics, known as the equilibrium fluctuation-response relation. The theory is illustrated by considering the coupling of a unit charge to the external voltage in the two sites near the surface of membrane, representing the activated and resting states. This is done using a coarse-graining (CG) model of membrane proteins, which includes the membrane, the electrolytes and the electrodes. The CG model yields Marcus-type voltage dependent free energy parabolas for the response of the electrostatic environment (electrolytes etc.) to the transition from the initial to the final configuratinal states, leading to equilibrium free energy difference and free

  2. Synchronization transmission of target signal within the coupling network with quantum chaos effect

    NASA Astrophysics Data System (ADS)

    Li, Wenlin; Li, Chong; Song, Heshan

    2016-11-01

    In this work, we propose a novel technology to investigate the synchronization transmission of target signal within the coupling network. In this new technology, the network synchronization transmission is realized through the coupling between the network nodes, and the controller is not required to add in the network. Especially, as long as the target signal can be input to an arbitrary node in the network, so all the network nodes are synchronized to the target signal, that is, the target signal has got synchronization transmission.

  3. Exact coupling threshold for structural transition reveals diversified behaviors in interconnected networks

    NASA Astrophysics Data System (ADS)

    Darabi Sahneh, Faryad; Scoglio, Caterina; Van Mieghem, Piet

    2015-10-01

    An interconnected network features a structural transition between two regimes [F. Radicchi and A. Arenas, Nat. Phys. 9, 717 (2013), 10.1038/nphys2761]: one where the network components are structurally distinguishable and one where the interconnected network functions as a whole. Our exact solution for the coupling threshold uncovers network topologies with unexpected behaviors. Specifically, we show conditions that superdiffusion, introduced by Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013), 10.1103/PhysRevLett.110.028701], can occur despite the network components functioning distinctly. Moreover, we find that components of certain interconnected network topologies are indistinguishable despite very weak coupling between them.

  4. G Protein-Coupled Receptors in Anopheles gambiae

    NASA Astrophysics Data System (ADS)

    Hill, Catherine A.; Fox, A. Nicole; Pitts, R. Jason; Kent, Lauren B.; Tan, Perciliz L.; Chrystal, Mathew A.; Cravchik, Anibal; Collins, Frank H.; Robertson, Hugh M.; Zwiebel, Laurence J.

    2002-10-01

    We used bioinformatic approaches to identify a total of 276 G protein-coupled receptors (GPCRs) from the Anopheles gambiae genome. These include GPCRs that are likely to play roles in pathways affecting almost every aspect of the mosquito's life cycle. Seventy-nine candidate odorant receptors were characterized for tissue expression and, along with 76 putative gustatory receptors, for their molecular evolution relative to Drosophila melanogaster. Examples of lineage-specific gene expansions were observed as well as a single instance of unusually high sequence conservation.

  5. Morphisms of reaction networks that couple structure to function

    PubMed Central

    2014-01-01

    Background The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures. Results We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm. Conclusions Structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks. PMID:25128194

  6. G protein-coupled receptors and the regulation of autophagy

    PubMed Central

    Wauson, Eric M.; Dbouk, Hashem A.; Ghosh, Anwesha B.; Cobb, Melanie H.

    2014-01-01

    Autophagy is an important catabolic cellular process that eliminates damaged and unnecessary cytoplasmic proteins and organelles. Basal autophagy occurs during normal physiological conditions, but the activity of this process can be significantly altered in human diseases. Thus, defining the regulatory inputs and signals that control autophagy is essential. Nutrients are key modulators of autophagy. While autophagy is generally accepted to be regulated in a cell autonomous fashion, recent studies suggest nutrients can modulate autophagy in a systemic manner by inducing the secretion of hormones and neurotransmitters that regulate G protein-coupled receptors (GPCRs). Emerging studies show that GPCRs also regulate autophagy by directly detecting extracellular nutrients. We review the role of GPCRs in autophagy regulation, highlighting their potential as therapeutic drug targets. PMID:24751357

  7. Serial femtosecond crystallography of G protein-coupled receptors.

    PubMed

    Liu, Wei; Wacker, Daniel; Gati, Cornelius; Han, Gye Won; James, Daniel; Wang, Dingjie; Nelson, Garrett; Weierstall, Uwe; Katritch, Vsevolod; Barty, Anton; Zatsepin, Nadia A; Li, Dianfan; Messerschmidt, Marc; Boutet, Sébastien; Williams, Garth J; Koglin, Jason E; Seibert, M Marvin; Wang, Chong; Shah, Syed T A; Basu, Shibom; Fromme, Raimund; Kupitz, Christopher; Rendek, Kimberley N; Grotjohann, Ingo; Fromme, Petra; Kirian, Richard A; Beyerlein, Kenneth R; White, Thomas A; Chapman, Henry N; Caffrey, Martin; Spence, John C H; Stevens, Raymond C; Cherezov, Vadim

    2013-12-20

    X-ray crystallography of G protein-coupled receptors and other membrane proteins is hampered by difficulties associated with growing sufficiently large crystals that withstand radiation damage and yield high-resolution data at synchrotron sources. We used an x-ray free-electron laser (XFEL) with individual 50-femtosecond-duration x-ray pulses to minimize radiation damage and obtained a high-resolution room-temperature structure of a human serotonin receptor using sub-10-micrometer microcrystals grown in a membrane mimetic matrix known as lipidic cubic phase. Compared with the structure solved by using traditional microcrystallography from cryo-cooled crystals of about two orders of magnitude larger volume, the room-temperature XFEL structure displays a distinct distribution of thermal motions and conformations of residues that likely more accurately represent the receptor structure and dynamics in a cellular environment.

  8. Serial Femtosecond Crystallography of G Protein-Coupled Receptors

    PubMed Central

    Liu, Wei; Wacker, Daniel; Gati, Cornelius; Han, Gye Won; James, Daniel; Wang, Dingjie; Nelson, Garrett; Weierstall, Uwe; Katritch, Vsevolod; Barty, Anton; Zatsepin, Nadia A.; Li, Dianfan; Messerschmidt, Marc; Boutet, Sébastien; Williams, Garth J.; Koglin, Jason E.; Seibert, M. Marvin; Wang, Chong; Shah, Syed T.A.; Basu, Shibom; Fromme, Raimund; Kupitz, Christopher; Rendek, Kimberley N.; Grotjohann, Ingo; Fromme, Petra; Kirian, Richard A.; Beyerlein, Kenneth R.; White, Thomas A.; Chapman, Henry N.; Caffrey, Martin; Spence, John C.H.; Stevens, Raymond C.; Cherezov, Vadim

    2014-01-01

    X-ray crystallography of G protein-coupled receptors and other membrane proteins is hampered by difficulties associated with growing sufficiently large crystals that withstand radiation damage and yield high-resolution data at synchrotron sources. Here we used an x-ray free-electron laser (XFEL) with individual 50-fs duration x-ray pulses to minimize radiation damage and obtained a high-resolution room temperature structure of a human serotonin receptor using sub-10 µm microcrystals grown in a membrane mimetic matrix known as lipidic cubic phase. Compared to the structure solved by traditional microcrystallography from cryo-cooled crystals of about two orders of magnitude larger volume, the room temperature XFEL structure displays a distinct distribution of thermal motions and conformations of residues that likely more accurately represent the receptor structure and dynamics in a cellular environment. PMID:24357322

  9. Serial femtosecond crystallography of G protein-coupled receptors.

    PubMed

    Liu, Wei; Wacker, Daniel; Gati, Cornelius; Han, Gye Won; James, Daniel; Wang, Dingjie; Nelson, Garrett; Weierstall, Uwe; Katritch, Vsevolod; Barty, Anton; Zatsepin, Nadia A; Li, Dianfan; Messerschmidt, Marc; Boutet, Sébastien; Williams, Garth J; Koglin, Jason E; Seibert, M Marvin; Wang, Chong; Shah, Syed T A; Basu, Shibom; Fromme, Raimund; Kupitz, Christopher; Rendek, Kimberley N; Grotjohann, Ingo; Fromme, Petra; Kirian, Richard A; Beyerlein, Kenneth R; White, Thomas A; Chapman, Henry N; Caffrey, Martin; Spence, John C H; Stevens, Raymond C; Cherezov, Vadim

    2013-12-20

    X-ray crystallography of G protein-coupled receptors and other membrane proteins is hampered by difficulties associated with growing sufficiently large crystals that withstand radiation damage and yield high-resolution data at synchrotron sources. We used an x-ray free-electron laser (XFEL) with individual 50-femtosecond-duration x-ray pulses to minimize radiation damage and obtained a high-resolution room-temperature structure of a human serotonin receptor using sub-10-micrometer microcrystals grown in a membrane mimetic matrix known as lipidic cubic phase. Compared with the structure solved by using traditional microcrystallography from cryo-cooled crystals of about two orders of magnitude larger volume, the room-temperature XFEL structure displays a distinct distribution of thermal motions and conformations of residues that likely more accurately represent the receptor structure and dynamics in a cellular environment. PMID:24357322

  10. Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression

    PubMed Central

    Koo, Hyun-Jung; Park, Wook-Ha; Yang, Jae-Seong; Yu, Myeong-Hee; Kim, Sanguk; Pak, Youngmi Kim

    2011-01-01

    The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA (mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins (mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted (ρ0) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ0 cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions. PMID:21738461

  11. Cluster synchronization of starlike networks with normalized Laplacian coupling: master stability function approach

    NASA Astrophysics Data System (ADS)

    Kuptsov, Pavel V.; Kuptsova, Anna V.

    2016-04-01

    A generalized model of star-like network is suggested that takes into account non-additive coupling and nonlinear transformation of coupling variables. For this model a method of analysis of synchronized cluster stability is developed. Using this method three star-like networks based on Ikeda, predator-prey and Hénon maps are studied.

  12. Ultrafast Hydration Dynamics and Coupled Water-Protein Fluctuations in Apomyoglobin

    NASA Astrophysics Data System (ADS)

    Yang, Yi; Zhang, Luyuan; Wang, Lijuan; Zhong, Dongping

    2009-06-01

    Protein hydration dynamics are of fundamental importance to its structure and function. Here, we characterize the global solvation dynamics and anisotropy dynamics around the apomyoglobin surface in different conformational states (native and molten globule) by measuring the Stokes shift and anisotropy decay of tryptophan with femtosecond-resolved fluorescence upconversion. With site-directed mutagenesis, we designed sixteen mutants with one tryptophan in each, and placed the probe at a desirable position ranging from buried in the protein core to fully solvent-exposed on the protein surface. In all protein sites studied, two distinct solvation relaxations (1-8 ps and 20-200 ps) were observed, reflecting the initial collective water relaxation and subsequent hydrogen-bond network restructuring, respectively, and both are strongly correlated with protein's local structures and chemical properties. The hydration dynamics of the mutants in molten globule state are faster than those observed in native state, indicating that the protein becomes more flexible and less structured when its conformation is converted from fully-folded native state to partially-folded molten globule state. Complementary, fluorescence anisotropy dynamics of all mutants in native state show an increasing trend of wobbling times (40-260 ps) when the location of the probe is changed from a loop, to a lateral helix, and then, to the compact protein core. Such an increase in wobbling times is related to the local protein structural rigidity, which relates the interaction of water with side chains. The ultrafast hydration dynamics and related side-chain motion around the protein surface unravel the coupled water-protein fluctuations on the picosecond time scales and indicate that the local protein motions are slaved by hydrating water fluctuations.

  13. Alpha-Bulges in G Protein-Coupled Receptors

    PubMed Central

    van der Kant, Rob; Vriend, Gert

    2014-01-01

    Agonist binding is related to a series of motions in G protein-coupled receptors (GPCRs) that result in the separation of transmembrane helices III and VI at their cytosolic ends and subsequent G protein binding. A large number of smaller motions also seem to be associated with activation. Most helices in GPCRs are highly irregular and often contain kinks, with extensive literature already available about the role of prolines in kink formation and the precise function of these kinks. GPCR transmembrane helices also contain many α-bulges. In this article we aim to draw attention to the role of these α-bulges in ligand and G-protein binding, as well as their role in several aspects of the mobility associated with GPCR activation. This mobility includes regularization and translation of helix III in the extracellular direction, a rotation of the entire helix VI, an inward movement of the helices near the extracellular side, and a concerted motion of the cytosolic ends of the helices that makes their orientation appear more circular and that opens up space for the G protein to bind. In several cases, α-bulges either appear or disappear as part of the activation process. PMID:24806342

  14. Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay.

    PubMed

    Bao, Haibo; Park, Ju H; Cao, Jinde

    2016-01-01

    This paper deals with the exponential synchronization of coupled stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli stochastic variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results.

  15. Defining the Protein-Protein Interaction Network of the Human Protein Tyrosine Phosphatase Family.

    PubMed

    Li, Xu; Tran, Kim My; Aziz, Kathryn E; Sorokin, Alexey V; Chen, Junjie; Wang, Wenqi

    2016-09-01

    Protein tyrosine phosphorylation, which plays a vital role in a variety of human cellular processes, is coordinated by protein tyrosine kinases and protein tyrosine phosphatases (PTPs). Genomic studies provide compelling evidence that PTPs are frequently mutated in various human cancers, suggesting that they have important roles in tumor suppression. However, the cellular functions and regulatory machineries of most PTPs are still largely unknown. To gain a comprehensive understanding of the protein-protein interaction network of the human PTP family, we performed a global proteomic study. Using a Minkowski distance-based unified scoring environment (MUSE) for the data analysis, we identified 940 high confidence candidate-interacting proteins that comprise the interaction landscape of the human PTP family. Through a gene ontology analysis and functional validations, we connected the PTP family with several key signaling pathways or cellular functions whose associations were previously unclear, such as the RAS-RAF-MEK pathway, the Hippo-YAP pathway, and cytokinesis. Our study provides the first glimpse of a protein interaction network for the human PTP family, linking it to a number of crucial signaling events, and generating a useful resource for future studies of PTPs.

  16. Model Organisms in G Protein-Coupled Receptor Research.

    PubMed

    Langenhan, Tobias; Barr, Maureen M; Bruchas, Michael R; Ewer, John; Griffith, Leslie C; Maiellaro, Isabella; Taghert, Paul H; White, Benjamin H; Monk, Kelly R

    2015-09-01

    The study of G protein-coupled receptors (GPCRs) has benefited greatly from experimental approaches that interrogate their functions in controlled, artificial environments. Working in vitro, GPCR receptorologists discovered the basic biologic mechanisms by which GPCRs operate, including their eponymous capacity to couple to G proteins; their molecular makeup, including the famed serpentine transmembrane unit; and ultimately, their three-dimensional structure. Although the insights gained from working outside the native environments of GPCRs have allowed for the collection of low-noise data, such approaches cannot directly address a receptor's native (in vivo) functions. An in vivo approach can complement the rigor of in vitro approaches: as studied in model organisms, it imposes physiologic constraints on receptor action and thus allows investigators to deduce the most salient features of receptor function. Here, we briefly discuss specific examples in which model organisms have successfully contributed to the elucidation of signals controlled through GPCRs and other surface receptor systems. We list recent examples that have served either in the initial discovery of GPCR signaling concepts or in their fuller definition. Furthermore, we selectively highlight experimental advantages, shortcomings, and tools of each model organism. PMID:25979002

  17. Protein contact network topology: a natural language for allostery.

    PubMed

    Di Paola, Luisa; Giuliani, Alessandro

    2015-04-01

    Protein molecules work as a whole, so that any local perturbation may resonate on the entire structure: allostery deals with this general property of protein molecules. It is worth noting a perturbation does not necessarily involve a conformational change but, more generally, it travels across the structure as an 'energy signal'. The atomic interactions within the network provide the structural support for this 'signaling highway'. Network descriptors, capturing network signaling efficiency, explain allostery in terms of signal transmission. In this review we will survey the key applications of graph theory to protein allostery. The complex network approach introduces a new perspective in biochemistry; as for applications, the development of new drugs relying on allosteric effects (allo-network drugs) represents a promising avenue of contact network formalism.

  18. Large-scale production and protein engineering of G protein-coupled receptors for structural studies

    PubMed Central

    Milić, Dalibor; Veprintsev, Dmitry B.

    2015-01-01

    Structural studies of G protein-coupled receptors (GPCRs) gave insights into molecular mechanisms of their action and contributed significantly to molecular pharmacology. This is primarily due to technical advances in protein engineering, production and crystallization of these important receptor targets. On the other hand, NMR spectroscopy of GPCRs, which can provide information about their dynamics, still remains challenging due to difficulties in preparation of isotopically labeled receptors and their low long-term stabilities. In this review, we discuss methods used for expression and purification of GPCRs for crystallographic and NMR studies. We also summarize protein engineering methods that played a crucial role in obtaining GPCR crystal structures. PMID:25873898

  19. Large-scale production and protein engineering of G protein-coupled receptors for structural studies.

    PubMed

    Milić, Dalibor; Veprintsev, Dmitry B

    2015-01-01

    Structural studies of G protein-coupled receptors (GPCRs) gave insights into molecular mechanisms of their action and contributed significantly to molecular pharmacology. This is primarily due to technical advances in protein engineering, production and crystallization of these important receptor targets. On the other hand, NMR spectroscopy of GPCRs, which can provide information about their dynamics, still remains challenging due to difficulties in preparation of isotopically labeled receptors and their low long-term stabilities. In this review, we discuss methods used for expression and purification of GPCRs for crystallographic and NMR studies. We also summarize protein engineering methods that played a crucial role in obtaining GPCR crystal structures.

  20. Image fusion by pulse couple neural network with shearlet

    NASA Astrophysics Data System (ADS)

    Geng, Peng; Wang, Zhengyou; Zhang, Zhigang; Xiao, Zhong

    2012-06-01

    The shearlet representation forms a tight frame which decomposes a function into scales and directions, and is optimally sparse in representing images with edges. An image fusion method is proposed based on the shearlet transform. Firstly, transform the image A and image B by the shearlets. Secondly, a pulse couple neural network (PCNN) is used for the frequency subbands, which uses the number of output pulses from the PCNN's neurons to select fusion coefficients. Finally, an inverse shearlet transform is applied on the new fused coefficients to reconstruct the fused image. Some experiments are performed in images such as multi-focus images, multi-sensor images, medical images and multispectral images comparing the proposed algorithm with the wavelet, contourlet and nonsubsampled contourlet method based on the PCNN. The experimental results show that the proposed algorithm can not only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. It significantly outperforms the traditional multiscale transform image fusion methods in terms of both visual quality and objective evaluation criteria such as MI and QAB/F.

  1. Prioritizing protein complexes implicated in human diseases by network optimization

    PubMed Central

    2014-01-01

    Background The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes. Results We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. Conclusions MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins. PMID:24565064

  2. Synchronization and spatiotemporal patterns in coupled phase oscillators on a weighted planar network

    NASA Astrophysics Data System (ADS)

    Kagawa, Yuki; Takamatsu, Atsuko

    2009-04-01

    To reveal the relation between network structures found in two-dimensional biological systems, such as protoplasmic tube networks in the plasmodium of true slime mold, and spatiotemporal oscillation patterns emerged on the networks, we constructed coupled phase oscillators on weighted planar networks and investigated their dynamics. Results showed that the distribution of edge weights in the networks strongly affects (i) the propensity for global synchronization and (ii) emerging ratios of oscillation patterns, such as traveling and concentric waves, even if the total weight is fixed. In-phase locking, traveling wave, and concentric wave patterns were, respectively, observed most frequently in uniformly weighted, center weighted treelike, and periphery weighted ring-shaped networks. Controlling the global spatiotemporal patterns with the weight distribution given by the local weighting (coupling) rules might be useful in biological network systems including the plasmodial networks and neural networks in the brain.

  3. Simulated evolution of protein-protein interaction networks with realistic topology.

    PubMed

    Peterson, G Jack; Pressé, Steve; Peterson, Kristin S; Dill, Ken A

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.

  4. Simulated evolution of protein-protein interaction networks with realistic topology.

    PubMed

    Peterson, G Jack; Pressé, Steve; Peterson, Kristin S; Dill, Ken A

    2012-01-01

    We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution. PMID:22768057

  5. Identification and evolution of structurally dominant nodes in protein-protein interaction networks.

    PubMed

    Wang, Pei; Yu, Xinghuo; Lü, Jinhu

    2014-02-01

    It is well known that protein-protein interaction (PPI) networks are typical evolving complex networks. Identification of important nodes has been an emerging popular topic in complex networks. Many indexes have been proposed to measure the importance of nodes in complex networks, such as degree, closeness, betweenness, k-shell, clustering coefficient, semi-local centrality, eigenvector centrality. Based on multivariate statistical analysis, through integrating the above indexes and further considering the appearances of nodes in network motifs, this paper aims at developing a new measure to characterize the structurally dominant proteins (SDP) in PPI networks. Moreover, we will further investigate the evolution of the defined dominant nodes in temporal evolving real-world and artificial PPI networks. Our results indicate that the constructed artificial networks have some similar statistical properties as those of the real-world evolving networks. In this case, the artificial PPI networks can be used to further investigate the above evolution characteristics of the real-world evolving networks. Simulation results reveal that SDP in the yeast PPI networks are evolutionary conserved, however, the undominant nodes evolve rapidly. Furthermore, PPI networks are very robust against random mutations, while fragile yet with certain robustness to targeted mutations on SDP. Our investigations shed some light on the future applications of the evolving characteristics of bio-molecular networks, such as reengineering of particular networks for technological, synthetic or pharmacological purposes. PMID:24681922

  6. FunMod: a Cytoscape plugin for identifying functional modules in undirected protein-protein networks.

    PubMed

    Natale, Massimo; Benso, Alfredo; Di Carlo, Stefano; Ficarra, Elisa

    2014-08-01

    The characterization of the interacting behaviors of complex biological systems is a primary objective in protein-protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected protein-protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in diseases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

  7. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.

  8. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network.

    PubMed

    Hu, Yang; Zhang, Ying; Ren, Jun; Wang, Yadong; Wang, Zhenzhen; Zhang, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  9. Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

    PubMed Central

    Hu, Yang; Zhang, Ying; Ren, Jun

    2016-01-01

    The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence. PMID:27648447

  10. Structural and Supportive Changes in Couples' Family and Friendship Networks across the Transition to Parenthood.

    ERIC Educational Resources Information Center

    Bost, Kelly K.; Cox, Martha J.; Burchinal, Margaret R.; Payne, Chris

    2002-01-01

    Examines patterns of change in family and friend network with parenthood in 137 couples surveyed before the birth of their first child. Husbands and wives who reported larger network sizes and support prior to their first child's birth were more likely to report larger networks after birth. Changes in parents' social systems were related to…

  11. G-Protein Coupled Receptors: Surface Display and Biosensor Technology

    NASA Astrophysics Data System (ADS)

    McMurchie, Edward; Leifert, Wayne

    Signal transduction by G-protein coupled receptors (GPCRs) underpins a multitude of physiological processes. Ligand recognition by the receptor leads to the activation of a generic molecular switch involving heterotrimeric G-proteins and guanine nucleotides. With growing interest and commercial investment in GPCRs in areas such as drug targets, orphan receptors, high-throughput screening of drugs and biosensors, greater attention will focus on assay development to allow for miniaturization, ultrahigh-throughput and, eventually, microarray/biochip assay formats that will require nanotechnology-based approaches. Stable, robust, cell-free signaling assemblies comprising receptor and appropriate molecular switching components will form the basis of future GPCR/G-protein platforms, which should be able to be adapted to such applications as microarrays and biosensors. This chapter focuses on cell-free GPCR assay nanotechnologies and describes some molecular biological approaches for the construction of more sophisticated, surface-immobilized, homogeneous, functional GPCR sensors. The latter points should greatly extend the range of applications to which technologies based on GPCRs could be applied.

  12. Stabilization of G protein-coupled receptors by point mutations

    PubMed Central

    Heydenreich, Franziska M.; Vuckovic, Ziva; Matkovic, Milos; Veprintsev, Dmitry B.

    2015-01-01

    G protein-coupled receptors (GPCRs) are flexible integral membrane proteins involved in transmembrane signaling. Their involvement in many physiological processes makes them interesting targets for drug development. Determination of the structure of these receptors will help to design more specific drugs, however, their structural characterization has so far been hampered by the low expression and their inherent instability in detergents which made protein engineering indispensable for structural and biophysical characterization. Several approaches to stabilize the receptors in a particular conformation have led to breakthroughs in GPCR structure determination. These include truncations of the flexible regions, stabilization by antibodies and nanobodies, fusion partners, high affinity and covalently bound ligands as well as conformational stabilization by mutagenesis. In this review we focus on stabilization of GPCRs by insertion of point mutations, which lead to increased conformational and thermal stability as well as improved expression levels. We summarize existing mutagenesis strategies with different coverage of GPCR sequence space and depth of information, design and transferability of mutations and the molecular basis for stabilization. We also discuss whether mutations alter the structure and pharmacological properties of GPCRs. PMID:25941489

  13. PANADA: Protein Association Network Annotation, Determination and Analysis

    PubMed Central

    Martin, Alberto J. M.; Walsh, Ian; Domenico, Tomás Di; Mičetić, Ivan; Tosatto, Silvio C. E.

    2013-01-01

    Increasingly large numbers of proteins require methods for functional annotation. This is typically based on pairwise inference from the homology of either protein sequence or structure. Recently, similarity networks have been presented to leverage both the ability to visualize relationships between proteins and assess the transferability of functional inference. Here we present PANADA, a novel toolkit for the visualization and analysis of protein similarity networks in Cytoscape. Networks can be constructed based on pairwise sequence or structural alignments either on a set of proteins or, alternatively, by database search from a single sequence. The Panada web server, executable for download and examples and extensive help files are available at URL: http://protein.bio.unipd.it/panada/. PMID:24265686

  14. Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms.

    PubMed

    Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan

    2016-04-01

    Two types of coupled neural networks with reaction-diffusion terms are considered in this paper. In the first one, the nodes are coupled through their states. In the second one, the nodes are coupled through the spatial diffusion terms. For the former, utilizing Lyapunov functional method and pinning control technique, we obtain some sufficient conditions to guarantee that network can realize synchronization. In addition, considering that the theoretical coupling strength required for synchronization may be much larger than the needed value, we propose an adaptive strategy to adjust the coupling strength for achieving a suitable value. For the latter, we establish a criterion for synchronization using the designed pinning controllers. It is found that the coupled reaction-diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled reaction-diffusion neural networks with spatial diffusion coupling. Moreover, a general criterion for ensuring network synchronization is derived by pinning a small fraction of nodes with adaptive feedback controllers. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

  15. G protein activation by G protein coupled receptors: ternary complex formation or catalyzed reaction?

    PubMed

    Roberts, David J; Waelbroeck, Magali

    2004-09-01

    G protein coupled receptors catalyze the GDP/GTP exchange on G proteins, thereby activating them. The ternary complex model, designed to describe agonist binding in the absence of GTP, is often extended to G protein activation. This is logically unsatisfactory as the ternary complex does not accumulate when G proteins are activated by GTP. Extended models taking into account nucleotide binding exist, but fail to explain catalytic G protein activation. This review puts forward an enzymatic model of G protein activation and compares its predictions with the ternary complex model and with observed receptor phenomenon. This alternative model does not merely provide a new set of formulae but leads to a new philosophical outlook and more readily accommodates experimental observations. The ternary complex model implies that, HRG being responsible for efficient G protein activation, it should be as stable as possible. In contrast, the enzyme model suggests that although a limited stabilization of HRG facilitates GDP release, HRG should not be "too stable" as this might trap the G protein in an inactive state and actually hinder G protein activation. The two models also differ completely in the definition of the receptor "active state": the ternary complex model implies that the active state corresponds to a single active receptor conformation (HRG); in contrast, the catalytic model predicts that the active receptor state is mobile, switching smoothly through various conformations with high and low affinities for agonists (HR, HRG, HRGGDP, HRGGTP, etc.).

  16. Do heterotrimeric G proteins redistribute upon G protein-coupled receptor stimulation in platelets?

    PubMed

    Kahner, Bryan N; Quinton, Todd M; Langan, Sarah; Kunapuli, Satya P

    2006-09-01

    Previous studies have proposed that stimulation of G protein-coupled receptors can cause a redistribution of G proteins to other receptors. The redistribution would cause a greater functional sensitivity of unsensitized 'secondary' receptors toward their agonists. Using platelets as a model system, we utilized a proximal signaling event, intracellular calcium mobilization, to determine if agonist stimulation of particular Gq-coupled receptors would result in increased sensitivity for stimulation of other Gq-coupled receptors. Platelets express three Gq-coupled receptors for thrombin, thromboxane A2, and ADP with different potencies. Varying concentrations of a primary agonist (PAR-1 agonist SFLLRN, or the TXA2 agonist U46619) was followed by a constant submaximal concentration of a secondary agonist (U46619, or the P2Y1 agonist ADP). We observed that initial stimulation by SFLLRN was followed by a decrease in the extent of secondary U46619 or ADP-mediated calcium mobilization in comparison to control responses (i.e. without primary stimulation). To extend these studies we examined calcium mobilization in platelets from mice that were either wild-type or homozygous null for the PAR-4 or P2Y1 receptors, hypothesizing that the loss of PAR-4 or P2Y1 receptors would cause redistribution of its Galphaq proteins to other receptors, and elicit a greater response when stimulated with other agonists than in platelets from a wild-type mouse. However, our results showed almost identical levels of peak calcium between wild-type or PAR-4 null mice when stimulated with either ADP or U46619. Similar results were obtained for the P2Y1 null mice stimulated with AYPGKF or U46619. We conclude that stimulation of one Gq coupled receptor does not result in redistribution of Gq to other Gq-coupled receptors. PMID:16973501

  17. Distinct Lysosomal Network Protein Profiles in Parkinsonian Syndrome Cerebrospinal Fluid

    PubMed Central

    Boman, Andrea; Svensson, Samuel; Boxer, Adam; Rojas, Julio C.; Seeley, William W.; Karydas, Anna; Miller, Bruce; Kågedal, Katarina; Svenningsson, Per

    2016-01-01

    Background: Clinical diagnosis of parkinsonian syndromes like Parkinson’s disease (PD), corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP) is hampered by overlapping symptomatology and lack of diagnostic biomarkers, and definitive diagnosis is only possible post-mortem. Objective: Since impaired protein degradation plays an important role in many neurodegenerative disorders, we hypothesized that profiles of select lysosomal network proteins in cerebrospinal fluid could be differentially expressed in these parkinsonian syndromes. Methods: Cerebrospinal fluid samples were collected from PD patients (n = 18), clinically diagnosed 4-repeat tauopathy patients; corticobasal syndrome (CBS) (n = 3) and PSP (n = 8); and pathologically diagnosed PSP (n = 8) and CBD patients (n = 7). Each patient set was compared to its appropriate control group consisting of age and gender matched individuals. Select lysosomal network protein levels were detected via Western blotting. Factor analysis was used to test the diagnostic sensitivity, specificity and accuracy of the select lysosomal network protein expression profiles. Results: PD, CBD and PSP were markedly different in their cerebrospinal fluid lysosomal network protein profiles. Lysosomal-associated membrane proteins 1 and 2 were significantly decreased in PD; early endosomal antigen 1 was decreased and lysozyme increased in PSP; and lysosomal-associated membrane proteins 1 and 2, microtubule-associated protein 1 light chain 3 and lysozyme were increased in CBD. A panel of lysosomal-associated membrane protein 2, lysozyme and microtubule-associated protein 1 light chain discriminated between controls, PD and 4-repeat tauopathies. Conclusions: This study offers proof of concept that select lysosomal network proteins are differentially expressed in cerebrospinal fluid of Parkinson’s disease, corticobasal syndrome and progressive supranuclear palsy. Lysosomal network protein analysis

  18. Signalling-dependent interactions between the kinase-coupling protein CheW and chemoreceptors in living cells.

    PubMed

    Pedetta, Andrea; Parkinson, John S; Studdert, Claudia A

    2014-09-01

    Chemical signals sensed on the periplasmic side of bacterial cells by transmembrane chemoreceptors are transmitted to the flagellar motors via the histidine kinase CheA, which controls the phosphorylation level of the effector protein CheY. Chemoreceptor arrays comprise remarkably stable supramolecular structures in which thousands of chemoreceptors are networked through interactions between their cytoplasmic tips, CheA, and the small coupling protein CheW. To explore the conformational changes that occur within this protein assembly during signalling, we used in vivo cross-linking methods to detect close interactions between the coupling protein CheW and the serine receptor Tsr in intact Escherichia coli cells. We identified two signal-sensitive contacts between CheW and the cytoplasmic tip of Tsr. Our results suggest that ligand binding triggers changes in the receptor that alter its signalling contacts with CheW (and/or CheA).

  19. Conformational Fluctuations in G-Protein-Coupled Receptors

    NASA Astrophysics Data System (ADS)

    Brown, Michael F.

    2014-03-01

    G-protein-coupled receptors (GPCRs) comprise almost 50% of pharmaceutical drug targets, where rhodopsin is an important prototype and occurs naturally in a lipid membrane. Rhodopsin photoactivation entails 11-cis to all-trans isomerization of the retinal cofactor, yielding an equilibrium between inactive Meta-I and active Meta-II states. Two important questions are: (1) Is rhodopsin is a simple two-state switch? Or (2) does isomerization of retinal unlock an activated conformational ensemble? For an ensemble-based activation mechanism (EAM) a role for conformational fluctuations is clearly indicated. Solid-state NMR data together with theoretical molecular dynamics (MD) simulations detect increased local mobility of retinal after light activation. Resultant changes in local dynamics of the cofactor initiate large-scale fluctuations of transmembrane helices that expose recognition sites for the signal-transducing G-protein. Time-resolved FTIR studies and electronic spectroscopy further show the conformational ensemble is strongly biased by the membrane lipid composition, as well as pH and osmotic pressure. A new flexible surface model (FSM) describes how the curvature stress field of the membrane governs the energetics of active rhodopsin, due to the spontaneous monolayer curvature of the lipids. Furthermore, influences of osmotic pressure dictate that a large number of bulk water molecules are implicated in rhodopsin activation. Around 60 bulk water molecules activate rhodopsin, which is much larger than the number of structural waters seen in X-ray crystallography, or inferred from studies of bulk hydrostatic pressure. Conformational selection and promoting vibrational motions of rhodopsin lead to activation of the G-protein (transducin). Our biophysical data give a paradigm shift in understanding GPCR activation. The new view is: dynamics and conformational fluctuations involve an ensemble of substates that activate the cognate G-protein in the amplified visual

  20. A protein trisulfide couples dissimilatory sulfate reduction to energy conservation

    NASA Astrophysics Data System (ADS)

    Santos, André A.; Venceslau, Sofia S.; Grein, Fabian; Leavitt, William D.; Dahl, Christiane; Johnston, David T.; Pereira, Inês A. C.

    2015-12-01

    Microbial sulfate reduction has governed Earth’s biogeochemical sulfur cycle for at least 2.5 billion years. However, the enzymatic mechanisms behind this pathway are incompletely understood, particularly for the reduction of sulfite—a key intermediate in the pathway. This critical reaction is performed by DsrAB, a widespread enzyme also involved in other dissimilatory sulfur metabolisms. Using in vitro assays with an archaeal DsrAB, supported with genetic experiments in a bacterial system, we show that the product of sulfite reduction by DsrAB is a protein-based trisulfide, in which a sulfite-derived sulfur is bridging two conserved cysteines of DsrC. Physiological studies also reveal that sulfate reduction rates are determined by cellular levels of DsrC. Dissimilatory sulfate reduction couples the four-electron reduction of the DsrC trisulfide to energy conservation.

  1. Crystal Structure of a Lipid G Protein-Coupled Receptor

    SciTech Connect

    Hanson, Michael A; Roth, Christopher B; Jo, Euijung; Griffith, Mark T; Scott, Fiona L; Reinhart, Greg; Desale, Hans; Clemons, Bryan; Cahalan, Stuart M; Schuerer, Stephan C; Sanna, M Germana; Han, Gye Won; Kuhn, Peter; Rosen, Hugh; Stevens, Raymond C

    2012-03-01

    The lyso-phospholipid sphingosine 1-phosphate modulates lymphocyte trafficking, endothelial development and integrity, heart rate, and vascular tone and maturation by activating G protein-coupled sphingosine 1-phosphate receptors. Here, we present the crystal structure of the sphingosine 1-phosphate receptor 1 fused to T4-lysozyme (S1P1-T4L) in complex with an antagonist sphingolipid mimic. Extracellular access to the binding pocket is occluded by the amino terminus and extracellular loops of the receptor. Access is gained by ligands entering laterally between helices I and VII within the transmembrane region of the receptor. This structure, along with mutagenesis, agonist structure-activity relationship data, and modeling, provides a detailed view of the molecular recognition and requirement for hydrophobic volume that activates S1P1, resulting in the modulation of immune and stromal cell responses.

  2. Genetic approaches to unraveling G protein-coupled receptor biology.

    PubMed

    Aparicio, Samuel A J R; Powell, Justin

    2004-09-01

    Genetic approaches to validating G protein-coupled receptors (GPCRs) have proven to be a powerful research tool, especially knockout studies in rodents. To date, data related to in vivo function have been published on approximately half of the human rhodopsin-like family-1 GPCRs, which can be attributed to the use of mouse knockouts. It is likely that many currently unknown yet important therapeutic mechanisms will be uncovered through knockout screens in mice. One such recent discovery is the elucidation of the in vivo function of the GPCR GPR54 through mouse genetics, and its subsequent validation in human populations. Although previously unsuspected, GPR54 has been found to be a master-regulator of the hypothalamic-pituitary-gonadal axis.

  3. Regulation of G Protein-Coupled Receptors by Allosteric Ligands

    PubMed Central

    2013-01-01

    Topographically distinct, druggable, allosteric sites may be present on all G protein-coupled receptors (GPCRs). As such, targeting these sites with synthetic small molecules offers an attractive approach to develop receptor-subtype selective chemical leads for the development of novel therapies. A crucial part of drug development is to understand the acute and chronic effects of such allosteric modulators at their corresponding GPCR target. Key regulatory processes including cell-surface delivery, endocytosis, recycling, and down-regulation tightly control the number of receptors at the surface of the cell. As many GPCR therapeutics will be administered chronically, understanding how such ligands modulate these regulatory pathways forms an essential part of the characterization of novel GPCR ligands. This is true for both orthosteric and allosteric ligands. In this Review, we summarize our current understanding of GPCR regulatory processes with a particular focus on the effects and implications of allosteric targeting of GPCRs. PMID:23398684

  4. Serial femtosecond crystallography datasets from G protein-coupled receptors.

    PubMed

    White, Thomas A; Barty, Anton; Liu, Wei; Ishchenko, Andrii; Zhang, Haitao; Gati, Cornelius; Zatsepin, Nadia A; Basu, Shibom; Oberthür, Dominik; Metz, Markus; Beyerlein, Kenneth R; Yoon, Chun Hong; Yefanov, Oleksandr M; James, Daniel; Wang, Dingjie; Messerschmidt, Marc; Koglin, Jason E; Boutet, Sébastien; Weierstall, Uwe; Cherezov, Vadim

    2016-08-01

    We describe the deposition of four datasets consisting of X-ray diffraction images acquired using serial femtosecond crystallography experiments on microcrystals of human G protein-coupled receptors, grown and delivered in lipidic cubic phase, at the Linac Coherent Light Source. The receptors are: the human serotonin receptor 2B in complex with an agonist ergotamine, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2, the human smoothened receptor in complex with an antagonist cyclopamine, and finally the human angiotensin II type 1 receptor in complex with the selective antagonist ZD7155. All four datasets have been deposited, with minimal processing, in an HDF5-based file format, which can be used directly for crystallographic processing with CrystFEL or other software. We have provided processing scripts and supporting files for recent versions of CrystFEL, which can be used to validate the data.

  5. Biacore analysis with stabilized G-protein-coupled receptors.

    PubMed

    Rich, Rebecca L; Errey, James; Marshall, Fiona; Myszka, David G

    2011-02-15

    Using stabilized forms of β₁ adrenergic and A₂(A) adenosine G-protein-coupled receptors, we applied Biacore to monitor receptor activity and characterize binding constants of small-molecule antagonists spanning more than 20,000-fold in affinity. We also illustrate an improved method for tethering His-tagged receptors on NTA (carboxymethylated dextran preimmobilized with nitrilotriacetic acid) chips to yield stable, high-capacity, high-activity surfaces as well as a novel approach to regenerate receptor binding sites. Based on our success with this approach, we expect that the combination of stabilized receptors with biosensor technology will become a common method for characterizing members of this receptor family.

  6. Lysophospholipids and their G protein-coupled receptors in atherosclerosis.

    PubMed

    Li, Ya-Feng; Li, Rong-Shan; Samuel, Sonia B; Cueto, Ramon; Li, Xin-Yuan; Wang, Hong; Yang, Xiao-Feng

    2016-01-01

    Lysophospholipids (LPLs) are bioactive lipid-derived signaling molecules generated by the enzymatic and chemical processes of regiospecific phospholipases on substrates such as membrane phospholipids (PLs) and sphingolipids (SLs). They play a major role as extracellular mediators by activating G-protein coupled receptors (GPCRs) and stimulating diverse cellular responses from their signaling pathways. LPLs are involved in various pathologies of the vasculature system including coronary heart disease and hypertension. Many studies suggest the importance of LPLs in their association with the development of atherosclerosis, a chronic and severe vascular disease. This paper focuses on the pathophysiological effects of different lysophospholipids on atherosclerosis, which may promote the pathogenesis of myocardial infarction and strokes. Their atherogenic biological activities take place in vascular endothelial cells, vascular smooth muscle cells, fibroblasts, monocytes and macrophages, dendritic cells, T-lymphocytes, platelets, etc. PMID:26709762

  7. Serial femtosecond crystallography datasets from G protein-coupled receptors

    PubMed Central

    White, Thomas A.; Barty, Anton; Liu, Wei; Ishchenko, Andrii; Zhang, Haitao; Gati, Cornelius; Zatsepin, Nadia A.; Basu, Shibom; Oberthür, Dominik; Metz, Markus; Beyerlein, Kenneth R.; Yoon, Chun Hong; Yefanov, Oleksandr M.; James, Daniel; Wang, Dingjie; Messerschmidt, Marc; Koglin, Jason E.; Boutet, Sébastien; Weierstall, Uwe; Cherezov, Vadim

    2016-01-01

    We describe the deposition of four datasets consisting of X-ray diffraction images acquired using serial femtosecond crystallography experiments on microcrystals of human G protein-coupled receptors, grown and delivered in lipidic cubic phase, at the Linac Coherent Light Source. The receptors are: the human serotonin receptor 2B in complex with an agonist ergotamine, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2, the human smoothened receptor in complex with an antagonist cyclopamine, and finally the human angiotensin II type 1 receptor in complex with the selective antagonist ZD7155. All four datasets have been deposited, with minimal processing, in an HDF5-based file format, which can be used directly for crystallographic processing with CrystFEL or other software. We have provided processing scripts and supporting files for recent versions of CrystFEL, which can be used to validate the data. PMID:27479354

  8. Serial femtosecond crystallography datasets from G protein-coupled receptors.

    PubMed

    White, Thomas A; Barty, Anton; Liu, Wei; Ishchenko, Andrii; Zhang, Haitao; Gati, Cornelius; Zatsepin, Nadia A; Basu, Shibom; Oberthür, Dominik; Metz, Markus; Beyerlein, Kenneth R; Yoon, Chun Hong; Yefanov, Oleksandr M; James, Daniel; Wang, Dingjie; Messerschmidt, Marc; Koglin, Jason E; Boutet, Sébastien; Weierstall, Uwe; Cherezov, Vadim

    2016-01-01

    We describe the deposition of four datasets consisting of X-ray diffraction images acquired using serial femtosecond crystallography experiments on microcrystals of human G protein-coupled receptors, grown and delivered in lipidic cubic phase, at the Linac Coherent Light Source. The receptors are: the human serotonin receptor 2B in complex with an agonist ergotamine, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2, the human smoothened receptor in complex with an antagonist cyclopamine, and finally the human angiotensin II type 1 receptor in complex with the selective antagonist ZD7155. All four datasets have been deposited, with minimal processing, in an HDF5-based file format, which can be used directly for crystallographic processing with CrystFEL or other software. We have provided processing scripts and supporting files for recent versions of CrystFEL, which can be used to validate the data. PMID:27479354

  9. Fluctuations in Mass-Action Equilibrium of Protein Binding Networks

    NASA Astrophysics Data System (ADS)

    Yan, Koon-Kiu; Walker, Dylan; Maslov, Sergei

    2008-12-01

    We consider two types of fluctuations in the mass-action equilibrium in protein binding networks. The first type is driven by slow changes in total concentrations of interacting proteins. The second type (spontaneous) is caused by quickly decaying thermodynamic deviations away from equilibrium. We investigate the effects of network connectivity on fluctuations by comparing them to scenarios in which the interacting pair is isolated from the network and analytically derives bounds on fluctuations. Collective effects are shown to sometimes lead to large amplification of spontaneous fluctuations. The strength of both types of fluctuations is positively correlated with the complex connectivity and negatively correlated with complex concentration. Our general findings are illustrated using a curated network of protein interactions and multiprotein complexes in baker’s yeast, with empirical protein concentrations.

  10. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

    PubMed Central

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

    2016-01-01

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

  11. Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family.

    PubMed

    Uhart, Marina; Flores, Gabriel; Bustos, Diego M

    2016-05-19

    Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems.

  12. A Social Network Comparison of Low-Income Black and White Newlywed Couples.

    PubMed

    Jackson, Grace L; Kennedy, David; Bradbury, Thomas N; Karney, Benjamin R

    2014-10-01

    Relative to White families, Black families have been described as relying on extended social networks to compensate for other social and economic disadvantages. The presence or absence of supportive social networks should be especially relevant to young couples entering marriage, but to date there has been little effort to describe the social networks of comparable Black and White newlyweds. The current study addressed this gap by drawing on interviews with 57 first-married newlyweds from low-income communities to compare the composition and structure of Black and White couples' duocentric social networks. The results indicated that low-income Black couples entered marriage at a social disadvantage relative to White couples, with more family relationships but fewer positive relationships and fewer sources of emotional support (for wives), fewer connections to married individuals, and fewer shared relationships between spouses. Black couples' relative social disadvantages persisted even when various economic and demographic variables were controlled. PMID:25214673

  13. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment. PMID:23889048

  14. Predict drug-protein interaction in cellular networking.

    PubMed

    Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

    2013-01-01

    Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs". To develop GPCR-targeting or ion-channel-targeting drugs, the first important step is to identify the interactions between potential drug compounds with the two kinds of protein receptors in the cellular networking. In this minireview, we are to introduce two predictors. One is called iGPCR-Drug accessible at http://www.jci-bioinfo.cn/iGPCR-Drug/; the other called iCDI-PseFpt at http://www.jci-bioinfo.cn/iCDI-PseFpt. The former is for identifying the interactions of drug compounds with GPCRs; while the latter for that with ion channels. In both predictors, the drug compound was formulated by the two-dimensional molecular fingerprint, and the protein receptor by the pseudo amino acid composition generated with the grey model theory, while the operation engine was the fuzzy K-nearest neighbor algorithm. For the convenience of most experimental pharmaceutical and medical scientists, a step-bystep guide is provided on how to use each of the two web-servers to get the desired results without the need to follow the complicated mathematics involved originally for their establishment.

  15. Synchronized states and multistability in a random network of coupled discontinuous maps

    SciTech Connect

    Nag, Mayurakshi; Poria, Swarup

    2015-08-15

    The synchronization behavior of coupled chaotic discontinuous maps over a ring network with dynamic random connections is reported in this paper. It is observed that random rewiring stabilizes one of the two strongly unstable fixed points of the local map. Depending on initial conditions, the network synchronizes to different unstable fixed points, which signifies the existence of synchronized multistability in the complex network. Moreover, the length of discontinuity of the local map has an important role in generating windows of different synchronized fixed points. Synchronized fixed point and synchronized periodic orbits are found in the network depending on coupling strength and different parameter values of the local map. We have identified the existence of period subtracting bifurcation with respect to coupling strength in the network. The range of coupling strength for the occurrence of synchronized multistable spatiotemporal fixed points is determined. This range strongly depends upon the dynamic rewiring probability and also on the local map.

  16. A fast approach to global alignment of protein-protein interaction networks

    PubMed Central

    2013-01-01

    edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms. Conclusions We have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure. Our heuristic matching techniques maintain comparable – if not better – quality in resulting alignments. A consequence of our work is that network-alignment based orthologies can be computed within minutes (as compared to hours) on typical protein interaction networks, enabling a more comprehensive tuning of alignment parameters for refined orthologies. PMID:23363457

  17. Classification of G-protein coupled receptors at four levels.

    PubMed

    Gao, Qing-Bin; Wang, Zheng-Zhi

    2006-11-01

    G-protein coupled receptors (GPCRs) are transmembrane proteins which via G-proteins initiate some of the important signaling pathways in a cell and are involved in various physiological processes. Thus, computational prediction and classification of GPCRs can supply significant information for the development of novel drugs in pharmaceutical industry. In this paper, a nearest neighbor method has been introduced to discriminate GPCRs from non-GPCRs and subsequently classify GPCRs at four levels on the basis of amino acid composition and dipeptide composition of proteins. Its performance is evaluated on a non-redundant dataset consisted of 1406 GPCRs for six families and 1406 globular proteins using the jackknife test. The present method based on amino acid composition achieved an overall accuracy of 96.4% and Matthew's correlation coefficient (MCC) of 0.930 for correctly picking out the GPCRs from globular proteins. The overall accuracy and MCC were further enhanced to 99.8% and 0.996 by dipeptide composition-based method. On the other hand, the present method has successfully classified 1406 GPCRs into six families with an overall accuracy of 89.6 and 98.8% using amino acid composition and dipeptide composition, respectively. For the subfamily prediction of 1181 GPCRs of rhodopsin-like family, the present method achieved an overall accuracy of 76.7 and 94.5% based on the amino acid composition and dipeptide composition, respectively. Finally, GPCRs belonging to the amine subfamily and olfactory subfamily of rhodopsin-like family were further analyzed at the type level. The overall accuracy of dipeptide composition-based method for the classification of amine type and olfactory type of GPCRs reached 94.5 and 86.9%, respectively, while the overall accuracy of amino acid composition-based method was very low for both subfamilies. In comparison with existing methods in the literature, the present method also displayed great competitiveness. These results demonstrate

  18. Structural mechanism of G protein activation by G protein-coupled receptor.

    PubMed

    Duc, Nguyen Minh; Kim, Hee Ryung; Chung, Ka Young

    2015-09-15

    G protein-coupled receptors (GPCRs) are a family of membrane receptors that regulate physiology and pathology of various organs. Consequently, about 40% of drugs in the market targets GPCRs. Heterotrimeric G proteins are composed of α, β, and γ subunits, and act as the key downstream signaling molecules of GPCRs. The structural mechanism of G protein activation by GPCRs has been of a great interest, and a number of biochemical and biophysical studies have been performed since the late 80's. These studies investigated the interface between GPCR and G proteins and the structural mechanism of GPCR-induced G protein activation. Recently, arrestins are also reported to be important molecular switches in GPCR-mediated signal transduction, and the physiological output of arrestin-mediated signal transduction is different from that of G protein-mediated signal transduction. Understanding the structural mechanism of the activation of G proteins and arrestins would provide fundamental information for the downstream signaling-selective GPCR-targeting drug development. This review will discuss the structural mechanism of GPCR-induced G protein activation by comparing previous biochemical and biophysical studies.

  19. G-protein-coupled receptor kinase 2 terminates G-protein-coupled receptor function in steroid hormone 20-hydroxyecdysone signaling.

    PubMed

    Zhao, Wen-Li; Wang, Di; Liu, Chun-Yan; Zhao, Xiao-Fan

    2016-01-01

    G-protein-coupled receptors (GPCRs) transmit extracellular signals across the cell membrane. GPCR kinases (GRKs) desensitize GPCR signals in the cell membrane. However, the role and mechanism of GRKs in the desensitization of steroid hormone signaling are unclear. In this study, we propose that GRK2 is phosphorylated by protein kinase C (PKC) in response to induction by the steroid hormone 20-hydroxyecdysone (20E), which determines its translocation to the cell membrane of the lepidopteran Helicoverpa armigera. GRK2 protein expression is increased during the metamorphic stage because of induction by 20E. Knockdown of GRK2 in larvae causes accelerated pupation, an increase in 20E-response gene expression, and advanced apoptosis and metamorphosis. 20E induces translocation of GRK2 from the cytoplasm to the cell membrane via steroid hormone ecdysone-responsive GPCR (ErGPCR-2). GRK2 is phosphorylated by PKC on serine 680 after induction by 20E, which leads to the translocation of GRK2 to the cell membrane. GRK2 interacts with ErGPCR-2. These data indicate that GRK2 terminates the ErGPCR-2 function in 20E signaling in the cell membrane by a negative feedback mechanism. PMID:27412951

  20. G-protein-coupled receptor kinase 2 terminates G-protein-coupled receptor function in steroid hormone 20-hydroxyecdysone signaling

    PubMed Central

    Zhao, Wen-Li; Wang, Di; Liu, Chun-Yan; Zhao, Xiao-Fan

    2016-01-01

    G-protein-coupled receptors (GPCRs) transmit extracellular signals across the cell membrane. GPCR kinases (GRKs) desensitize GPCR signals in the cell membrane. However, the role and mechanism of GRKs in the desensitization of steroid hormone signaling are unclear. In this study, we propose that GRK2 is phosphorylated by protein kinase C (PKC) in response to induction by the steroid hormone 20-hydroxyecdysone (20E), which determines its translocation to the cell membrane of the lepidopteran Helicoverpa armigera. GRK2 protein expression is increased during the metamorphic stage because of induction by 20E. Knockdown of GRK2 in larvae causes accelerated pupation, an increase in 20E-response gene expression, and advanced apoptosis and metamorphosis. 20E induces translocation of GRK2 from the cytoplasm to the cell membrane via steroid hormone ecdysone-responsive GPCR (ErGPCR-2). GRK2 is phosphorylated by PKC on serine 680 after induction by 20E, which leads to the translocation of GRK2 to the cell membrane. GRK2 interacts with ErGPCR-2. These data indicate that GRK2 terminates the ErGPCR-2 function in 20E signaling in the cell membrane by a negative feedback mechanism. PMID:27412951

  1. Synchronization of coupled large-scale Boolean networks

    SciTech Connect

    Li, Fangfei

    2014-03-15

    This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.

  2. Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks

    PubMed Central

    2014-01-01

    To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups. PMID:25435900

  3. Network based approaches reveal clustering in protein point patterns

    NASA Astrophysics Data System (ADS)

    Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

    2014-03-01

    Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

  4. Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach.

    PubMed

    Di Paola, Luisa; Platania, Chiara Bianca Maria; Oliva, Gabriele; Setola, Roberto; Pascucci, Federica; Giuliani, Alessandro

    2015-01-01

    Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein-protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node's ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein-protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.

  5. Synchronization of Memristor-Based Coupling Recurrent Neural Networks With Time-Varying Delays and Impulses.

    PubMed

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; He, Xing

    2015-12-01

    Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.

  6. Olfactory receptors: G protein-coupled receptors and beyond.

    PubMed

    Spehr, Marc; Munger, Steven D

    2009-06-01

    Sensing the chemical environment is critical for all organisms. Diverse animals from insects to mammals utilize highly organized olfactory system to detect, encode, and process chemostimuli that may carry important information critical for health, survival, social interactions and reproduction. Therefore, for animals to properly interpret and react to their environment it is imperative that the olfactory system recognizes chemical stimuli with appropriate selectivity and sensitivity. Because olfactory receptor proteins play such an essential role in the specific recognition of diverse stimuli, understanding how they interact with and transduce their cognate ligands is a high priority. In the nearly two decades since the discovery that the mammalian odorant receptor gene family constitutes the largest group of G protein-coupled receptor (GPCR) genes, much attention has been focused on the roles of GPCRs in vertebrate and invertebrate olfaction. However, is has become clear that the 'family' of olfactory receptors is highly diverse, with roles for enzymes and ligand-gated ion channels as well as GPCRs in the primary detection of olfactory stimuli. PMID:19383089

  7. Proton-coupled electron transfer in solution, proteins, and electrochemistry.

    PubMed

    Hammes-Schiffer, Sharon; Soudackov, Alexander V

    2008-11-13

    Recent advances in the theoretical treatment of proton-coupled electron transfer (PCET) reactions are reviewed. These reactions play an important role in a wide range of biological processes, as well as in fuel cells, solar cells, chemical sensors, and electrochemical devices. A unified theoretical framework has been developed to describe both sequential and concerted PCET, as well as hydrogen atom transfer (HAT). A quantitative diagnostic has been proposed to differentiate between HAT and PCET in terms of the degree of electronic nonadiabaticity, where HAT corresponds to electronically adiabatic proton transfer and PCET corresponds to electronically nonadiabatic proton transfer. In both cases, the overall reaction is typically vibronically nonadiabatic. A series of rate constant expressions have been derived in various limits by describing the PCET reactions in terms of nonadiabatic transitions between electron-proton vibronic states. These expressions account for the solvent response to both electron and proton transfer and the effects of the proton donor-acceptor vibrational motion. The solvent and protein environment can be represented by a dielectric continuum or described with explicit molecular dynamics. These theoretical treatments have been applied to numerous PCET reactions in solution and proteins. Expressions for heterogeneous rate constants and current densities for electrochemical PCET have also been derived and applied to model systems.

  8. Allosteric Coupling in the Bacterial Adhesive Protein FimH*

    PubMed Central

    Rodriguez, Victoria B.; Kidd, Brian A.; Interlandi, Gianluca; Tchesnokova, Veronika; Sokurenko, Evgeni V.; Thomas, Wendy E.

    2013-01-01

    The protein FimH is expressed by the majority of commensal and uropathogenic strains of Escherichia coli on the tips of type 1 fimbriae and mediates adhesion via a catch bond to its ligand mannose. Crystal structures of FimH show an allosteric conformational change, but it remains unclear whether all of the observed structural differences are part of the allosteric mechanism. Here we use the protein structural analysis tool RosettaDesign combined with human insight to identify and synthesize 10 mutations in four regions that we predicted would stabilize one of the conformations of that region. The function of each variant was characterized by measuring binding to the ligand mannose, whereas the allosteric state was determined using a conformation-specific monoclonal antibody. These studies demonstrated that each region investigated was indeed part of the FimH allosteric mechanism. However, the studies strongly suggested that some regions were more tightly coupled to mannose binding and others to antibody binding. In addition, we identified many FimH variants that appear locked in the low affinity state. Knowledge of regulatory sites outside the active and effector sites as well as the ability to make FimH variants locked in the low affinity state may be crucial to the future development of novel antiadhesive and antimicrobial therapies using allosteric regulation to inhibit FimH. PMID:23821547

  9. Heterologous expression of G-protein-coupled receptors in yeast.

    PubMed

    Bertheleme, Nicolas; Singh, Shweta; Dowell, Simon; Byrne, Bernadette

    2015-01-01

    Heterologous yeast expression systems have been successfully used for the production of G-protein-coupled receptors (GPCRs) for both structural and functional studies. Yeast combine comparatively low cost and short culture times with straightforward generation of expression clones. They also perform some key posttranslational modifications not possible in bacterial systems. There are two major yeast expression systems, Pichia pastoris and Saccharomyces cerevisiae, both of which have been used for the production of GPCRs. P. pastoris has a proven track record for the production of large amounts of GPCR for structural studies. High-resolution crystal structures of both the adenosine A2A and the histamine H1 receptors have been obtained using protein expressed in this system. S. cerevisiae is relatively easy to engineer and this has resulted in the development of sophisticated tools for the functional characterization of GPCRs. In this chapter, we provide protocols for both large-scale receptor expression in P. pastoris for structural studies and small-scale receptor expression in S. cerevisiae for functional characterization. In both cases, the receptor used is the human adenosine A2A receptor. The results that both we and others have obtained using these protocols show the wide utility of the yeast expression systems for the production of GPCRs.

  10. Protein complexes and functional modules in molecular networks

    NASA Astrophysics Data System (ADS)

    Spirin, Victor; Mirny, Leonid A.

    2003-10-01

    Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

  11. The complex G protein-coupled receptor kinase 2 (GRK2) interactome unveils new physiopathological targets

    PubMed Central

    Penela, Petronila; Murga, Cristina; Ribas, Catalina; Lafarga, Vanesa; Mayor, Federico

    2010-01-01

    GRK2 is a ubiquitous member of the G protein-coupled receptor kinase (GRK) family that appears to play a central, integrative role in signal transduction cascades. GRKs participate together with arrestins in the regulation of G protein-coupled receptors (GPCR), a family of hundreds of membrane proteins of key physiological and pharmacological importance, by triggering receptor desensitization from G proteins and GPCR internalization, and also by helping assemble macromolecular signalosomes in the receptor environment acting as agonist-regulated adaptor scaffolds, thus contributing to signal propagation. In addition, emerging evidence indicates that GRK2 can phosphorylate a growing number of non-GPCR substrates and associate with a variety of proteins related to signal transduction, thus suggesting that this kinase could also have diverse ‘effector’ functions. We discuss herein the increasing complexity of such GRK2 ‘interactome’, with emphasis on the recently reported roles of this kinase in cell migration and cell cycle progression and on the functional impact of the altered GRK2 levels observed in several relevant cardiovascular, inflammatory or tumour pathologies. Deciphering how the different networks of potential GRK2 functional interactions are orchestrated in a stimulus, cell type or context-specific way is critical to unveil the contribution of GRK2 to basic cellular processes, to understand how alterations in GRK2 levels or functionality may participate in the onset or development of several cardiovascular, tumour or inflammatory diseases, and to assess the feasibility of new therapeutic strategies based on the modulation of the activity, levels or specific interactions of GRK2. PMID:20590581

  12. Programming Molecular Association and Viscoelastic Behavior in Protein Networks.

    PubMed

    Dooling, Lawrence J; Buck, Maren E; Zhang, Wen-Bin; Tirrell, David A

    2016-06-01

    A set of recombinant artificial proteins that can be cross-linked, by either covalent bonds or association of helical domains or both, is described. The designed proteins can be used to construct molecular networks in which the mechanism of crosslinking determines the time-dependent responses to mechanical deformation. PMID:27061171

  13. Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

    PubMed Central

    Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

    2015-01-01

    It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

  14. Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis.

    PubMed

    Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

    2015-07-28

    It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant-wild-type and 16 matched SNP--wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation.

  15. MANET: tracing evolution of protein architecture in metabolic networks

    PubMed Central

    Kim, Hee Shin; Mittenthal, Jay E; Caetano-Anollés, Gustavo

    2006-01-01

    Background Cellular metabolism can be characterized by networks of enzymatic reactions and transport processes capable of supporting cellular life. Our aim is to find evolutionary patterns and processes embedded in the architecture and function of modern metabolism, using information derived from structural genomics. Description The Molecular Ancestry Network (MANET) project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism. Conclusion MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks. PMID:16854231

  16. Strong effects of network architecture in the entrainment of coupled oscillator systems

    NASA Astrophysics Data System (ADS)

    Kori, Hiroshi; Mikhailov, Alexander S.

    2006-12-01

    Random networks of coupled phase oscillators, representing an approximation for systems of coupled limit-cycle oscillators, are considered. Entrainment of such networks by periodic external forcing applied to a subset of their elements is numerically and analytically investigated. For a large class of interaction functions, we find that the entrainment window with a tongue shape becomes exponentially narrow for networks with higher hierarchical organization. However, the entrainment is significantly facilitated if the networks are directionally biased—i.e., closer to the feedforward networks. Furthermore, we show that the networks with high entrainment ability can be constructed by evolutionary optimization processes. The neural network structure of the master clock of the circadian rhythm in mammals is discussed from the viewpoint of our results.

  17. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    PubMed Central

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  18. Metallochelate Coupling of Phosphorescent Pt-Porphyrins to Peptides, Proteins, and Self-Assembling Protein Nanoparticles.

    PubMed

    Dmitriev, Ruslan I; O'Donnell, Neil; Papkovsky, Dmitri B

    2016-02-17

    Specific and reversible metallochelate coupling via nitrilotriacetate (NTA) moiety is widely used for immobilization, purification, and labeling of oligo(histidine)-tagged proteins. Here, we evaluated this strategy to label various peptides and proteins with phosphorescent Pt-porphyrin derivatives bearing NTA group(s). Zn(2+) complexes were shown to have minimal effect on the photophysics of the porphyrin moiety, allowing quenched-phosphorescence sensing of O2. We complexed the PtTFPP-NTA conjugate with His-containing peptide that can facilitate intracellular loading, and observed efficient accumulation and phosphorescent staining of MEF cells. The more hydrophilic PtCP-NTA conjugate was also seen to form stable complexes with larger polypeptide constructs based on fluorescent proteins, and with subunits of protein nanoparticles, which retained their ability to self-assemble. Testing in phosphorescence lifetime based O2 sensing assays on a fluorescence reader and PLIM microscope revealed that phosphorescent metallochelate complexes perform similarly to the existing O2 probes. Thus, metallochelate coupling allows simple preparation of different types of biomaterials labeled with phosphorescent Pt-porphyrins. PMID:26704593

  19. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses (SAOSA) of samples containing protein are useful for determining the nature of the protein matrix without damaging it. The Dairy Processing and Products Research Unit of the Agricultural Research Service, USDA has pioneered the use of SAOSA in understandin...

  20. Dynamic rheology of food protein networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Small amplitude oscillatory shear analyses of samples containing protein are useful for determining the nature of the protein matrix without damaging it. Elastic modulus, viscous modulus, and loss tangent (the ratio of viscous modulus to elastic modulus) give information on the strength of the netw...

  1. Passivity and robust synchronisation of switched interval coupled neural networks with time delay

    NASA Astrophysics Data System (ADS)

    Li, Ning; Cao, Jinde

    2016-09-01

    This paper is concerned with passivity and robust synchronisation of switched coupled neural networks with uncertain parameters. First, the mathematical model of switched coupled neural networks with interval uncertain parameters is established, which consists of L modes and switches from one mode to another according to the switching rule. Second, by employing passivity theory and linear matrix inequality techniques, delay-independent and delay-dependent conditions are derived to guarantee the passivity of switched interval coupled neural networks. Moreover, based on the proposed passivity results, global synchronisation criteria can be obtained for switched coupled neural networks with or without uncertain parameters. Finally, an illustrative example is provided to demonstrate the effectiveness of the obtained results.

  2. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

    PubMed

    Qu, Hong; Yi, Zhang; Yang, Simon X

    2013-06-01

    Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach. PMID:23144039

  3. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

    PubMed

    Qu, Hong; Yi, Zhang; Yang, Simon X

    2013-06-01

    Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

  4. Transport of organelles by elastically coupled motor proteins.

    PubMed

    Bhat, Deepak; Gopalakrishnan, Manoj

    2016-07-01

    Motor-driven intracellular transport is a complex phenomenon where multiple motor proteins simultaneously attached on to a cargo engage in pulling activity, often leading to tug-of-war, displaying bidirectional motion. However, most mathematical and computational models ignore the details of the motor-cargo interaction. A few studies have focused on more realistic models of cargo transport by including elastic motor-cargo coupling, but either restrict the number of motors and/or use purely phenomenological forms for force-dependent hopping rates. Here, we study a generic model in which N motors are elastically coupled to a cargo, which itself is subjected to thermal noise in the cytoplasm and to an additional external applied force. The motor-hopping rates are chosen to satisfy detailed balance with respect to the energy of elastic stretching. With these assumptions, an (N + 1) -variable master equation is constructed for dynamics of the motor-cargo complex. By expanding the hopping rates to linear order in fluctuations in motor positions, we obtain a linear Fokker-Planck equation. The deterministic equations governing the average quantities are separated out and explicit analytical expressions are obtained for the mean velocity and diffusion coefficient of the cargo. We also study the statistical features of the force experienced by an individual motor and quantitatively characterize the load-sharing among the cargo-bound motors. The mean cargo velocity and the effective diffusion coefficient are found to be decreasing functions of the stiffness. While the increase in the number of motors N does not increase the velocity substantially, it decreases the effective diffusion coefficient which falls as 1/N asymptotically. We further show that the cargo-bound motors share the force exerted on the cargo equally only in the limit of vanishing elastic stiffness; as stiffness is increased, deviations from equal load sharing are observed. Numerical simulations agree with

  5. GGA3 Interacts with a G Protein-Coupled Receptor and Modulates Its Cell Surface Export

    PubMed Central

    Zhang, Maoxiang; Davis, Jason E.; Li, Chunman; Gao, Jie; Huang, Wei; Lambert, Nevin A.; Terry, Alvin V.

    2016-01-01

    Molecular mechanisms governing the anterograde trafficking of nascent G protein-coupled receptors (GPCRs) are poorly understood. Here, we have studied the regulation of cell surface transport of α2-adrenergic receptors (α2-ARs) by GGA3 (Golgi-localized, γ-adaptin ear domain homology, ADP ribosylation factor-binding protein 3), a multidomain clathrin adaptor protein that sorts cargo proteins at the trans-Golgi network (TGN) to the endosome/lysosome pathway. By using an inducible system, we demonstrated that GGA3 knockdown significantly inhibited the cell surface expression of newly synthesized α2B-AR without altering overall receptor synthesis and internalization. The receptors were arrested in the TGN. Furthermore, GGA3 knockdown attenuated α2B-AR-mediated signaling, including extracellular signal-regulated kinase 1/2 (ERK1/2) activation and cyclic AMP (cAMP) inhibition. More interestingly, GGA3 physically interacted with α2B-AR, and the interaction sites were identified as the triple Arg motif in the third intracellular loop of the receptor and the acidic motif EDWE in the VHS domain of GGA3. In contrast, α2A-AR did not interact with GGA3 and its cell surface export and signaling were not affected by GGA3 knockdown. These data reveal a novel function of GGA3 in export trafficking of a GPCR that is mediated via a specific interaction with the receptor. PMID:26811329

  6. Synchronization and Partial Synchronization Experiments with Networks of Time-Delay Coupled Hindmarsh-Rose Neurons

    NASA Astrophysics Data System (ADS)

    Steur, Erik; Murguia, Carlos; Fey, Rob H. B.; Nijmeijer, Henk

    2016-06-01

    We study experimentally synchronization and partial synchronization in networks of Hindmarsh-Rose model neurons that interact through linear time-delay couplings. Our experimental setup consists of electric circuit board realizations of the Hindmarsh-Rose model neuron and a coupling interface in which the interaction between the circuits is defined. With this experimental setup we test the predictive value of theoretical results about synchronization and partial synchronization in networks.

  7. Reconstruction of Protein Networks Using Reverse-Phase Protein Array Data.

    PubMed

    von der Heyde, Silvia; Sonntag, Johanna; Kramer, Frank; Bender, Christian; Korf, Ulrike; Beißbarth, Tim

    2016-01-01

    In this chapter, we describe an approach to reconstruct cellular signaling networks based on measurements of protein activation after different stimulation experiments. As experimental platform reverse-phase protein arrays (RPPA) are used. RPPA allow the measurement of proteins and phosphoproteins across many samples in parallel with minimal sample consumption using a panel of highly target protein-specific antibodies. Functional interactions of proteins are modeled using a Boolean network. We describe the Boolean network reconstruction approach ddepn (dynamic deterministic effects propagation networks), which uses time course data to derive protein interactions based on perturbation experiments. We explain how the method works, give a practical application example, and describe how the results can be interpreted. Furthermore prior knowledge on signaling pathways is essential for network reconstruction. Here we describe the use of our software rBiopaxParser to integrate prior knowledge on protein signaling available in public databases. All applied methods are freely available as open-source R software packages. We describe the preparation of RPPA data as well as all relevant programming steps to format the RPPA data, to infer the prior knowledge, and to reconstruct and analyze the protein signaling networks. PMID:26519181

  8. An analysis pipeline for the inference of protein-protein interaction networks

    SciTech Connect

    Taylor, Ronald C.; Singhal, Mudita; Daly, Don S.; Gilmore, Jason M.; Cannon, William R.; Domico, Kelly O.; White, Amanda M.; Auberry, Deanna L.; Auberry, Kenneth J.; Hooker, Brian S.; Hurst, G. B.; McDermott, Jason E.; McDonald, W. H.; Pelletier, Dale A.; Schmoyer, Denise A.; Wiley, H. S.

    2009-12-01

    An analysis pipeline has been created for deployment of a novel algorithm, the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro), for use in the reconstruction of protein-protein interaction networks. We have combined the Software Environment for BIological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data, and the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources, to allow interactions computed by BEPro to be stored, visualized, and further analyzed. Incorporating BEPro into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of mass spectrometry bait-prey experiment data. SEBINI demo site: https://www.emsl.pnl.gov /SEBINI/ Contact: ronald.taylor@pnl.gov. BEPro is available at http://www.pnl.gov/statistics/BEPro3/index.htm. Contact: ds.daly@pnl.gov. CABIN is available at http://www.sysbio.org/dataresources/cabin.stm. Contact: mudita.singhal@pnl.gov.

  9. G-protein coupled receptor expression patterns delineate medulloblastoma subgroups

    PubMed Central

    2013-01-01

    Background Medulloblastoma is the most common malignant brain tumor in children. Genetic profiling has identified four principle tumor subgroups; each subgroup is characterized by different initiating mutations, genetic and clinical profiles, and prognoses. The two most well-defined subgroups are caused by overactive signaling in the WNT and SHH mitogenic pathways; less is understood about Groups 3 and 4 medulloblastoma. Identification of tumor subgroup using molecular classification is set to become an important component of medulloblastoma diagnosis and staging, and will likely guide therapeutic options. However, thus far, few druggable targets have emerged. G-protein coupled receptors (GPCRs) possess characteristics that make them ideal targets for molecular imaging and therapeutics; drugs targeting GPCRs account for 30-40% of all current pharmaceuticals. While expression patterns of many proteins in human medulloblastoma subgroups have been discerned, the expression pattern of GPCRs in medulloblastoma has not been investigated. We hypothesized that analysis of GPCR expression would identify clear subsets of medulloblastoma and suggest distinct GPCRs that might serve as molecular targets for both imaging and therapy. Results Our study found that medulloblastoma tumors fall into distinct clusters based solely on GPCR expression patterns. Normal cerebellum clustered separately from the tumor samples. Further, two of the tumor clusters correspond with high fidelity to the WNT and SHH subgroups of medulloblastoma. Distinct over-expressed GPCRs emerge; for example, LGR5 and GPR64 are significantly and uniquely over-expressed in the WNT subgroup of tumors, while PTGER4 is over-expressed in the SHH subgroup. Uniquely under-expressed GPCRs were also observed. Our key findings were independently validated using a large international dataset. Conclusions Our results identify GPCRs with potential to act as imaging and therapeutic targets. Elucidating tumorigenic pathways

  10. Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

    PubMed Central

    Tan, Dan; Li, Qiang; Zhang, Mei-Jun; Liu, Chao; Ma, Chengying; Zhang, Pan; Ding, Yue-He; Fan, Sheng-Bo; Tao, Li; Yang, Bing; Li, Xiangke; Ma, Shoucai; Liu, Junjie; Feng, Boya; Liu, Xiaohui; Wang, Hong-Wei; He, Si-Min; Gao, Ning; Ye, Keqiong; Dong, Meng-Qiu; Lei, Xiaoguang

    2016-01-01

    To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. DOI: http://dx.doi.org/10.7554/eLife.12509.001 PMID:26952210

  11. Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn

    PubMed Central

    Du, Chunguang; Xiong, Wenwei; Chen, Xinjian; Deng, Fei; Ma, Zhiyan; Qiao, Dahe; Hu, Chunhui; Ren, Yangliu; Li, Yuling

    2015-01-01

    The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated. A total of 6,876 proteins were identified, of which 1,396 were nonredundant. Specific proteins and different expression patterns were observed across developmental stages and tissues. The functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the development of the tissues. The whole, endosperm-specific and pericarp-specific protein networks integrated 125, 9 and 77 proteins, respectively, which were involved in 54 KEGG pathways and reflected their complex metabolic interactions. Confirmation for the iTRAQ endosperm proteins by two-dimensional gel electrophoresis showed that 44.44% proteins were commonly found. However, the concordance between mRNA level and the protein abundance varied across different proteins, stages, tissues and inbred lines, according to the gene cloning and expression analyses of four relevant proteins with important functions and different expression levels. But the result by western blot showed their same expression tendency for the four proteins as by iTRAQ. These results could provide new insights into the developmental mechanisms of endosperm and pericarp, and grain formation in maize. PMID:26587848

  12. Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn.

    PubMed

    Dong, Yongbin; Wang, Qilei; Zhang, Long; Du, Chunguang; Xiong, Wenwei; Chen, Xinjian; Deng, Fei; Ma, Zhiyan; Qiao, Dahe; Hu, Chunhui; Ren, Yangliu; Li, Yuling

    2015-01-01

    The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated. A total of 6,876 proteins were identified, of which 1,396 were nonredundant. Specific proteins and different expression patterns were observed across developmental stages and tissues. The functional annotation of the identified proteins revealed the importance of metabolic and cellular processes, and binding and catalytic activities for the development of the tissues. The whole, endosperm-specific and pericarp-specific protein networks integrated 125, 9 and 77 proteins, respectively, which were involved in 54 KEGG pathways and reflected their complex metabolic interactions. Confirmation for the iTRAQ endosperm proteins by two-dimensional gel electrophoresis showed that 44.44% proteins were commonly found. However, the concordance between mRNA level and the protein abundance varied across different proteins, stages, tissues and inbred lines, according to the gene cloning and expression analyses of four relevant proteins with important functions and different expression levels. But the result by western blot showed their same expression tendency for the four proteins as by iTRAQ. These results could provide new insights into the developmental mechanisms of endosperm and pericarp, and grain formation in maize. PMID:26587848

  13. GATA Factor-G-Protein-Coupled Receptor Circuit Suppresses Hematopoiesis

    PubMed Central

    Gao, Xin; Wu, Tongyu; Johnson, Kirby D.; Lahvic, Jamie L.; Ranheim, Erik A.; Zon, Leonard I.; Bresnick, Emery H.

    2016-01-01

    Summary Hematopoietic stem cells (HSCs) originate from hemogenic endothelium within the aorta-gonad-mesonephros (AGM) region of the mammalian embryo. The relationship between genetic circuits controlling stem cell genesis and multi-potency is not understood. A Gata2 cis element (+9.5) enhances Gata2 expression in the AGM and induces the endothelial to HSC transition. We demonstrated that GATA-2 rescued hematopoiesis in +9.5−/− AGMs. As G-protein-coupled receptors (GPCRs) are the most common targets for FDA-approved drugs, we analyzed the GPCR gene ensemble to identify GATA-2-regulated GPCRs. Of the 20 GATA-2-activated GPCR genes, four were GATA-1-activated, and only Gpr65 expression resembled Gata2. Contrasting with the paradigm in which GATA-2-activated genes promote hematopoietic stem and progenitor cell genesis/function, our mouse and zebrafish studies indicated that GPR65 suppressed hematopoiesis. GPR65 established repressive chromatin at the +9.5 site, restricted occupancy by the activator Scl/TAL1, and repressed Gata2 transcription. Thus, a Gata2 cis element creates a GATA-2-GPCR circuit that limits positive regulators that promote hematopoiesis. PMID:26905203

  14. G protein-coupled receptors in regulation of body weight.

    PubMed

    Schiöth, Helgi B

    2006-06-01

    In this issue of CNS & Neurological Disorders-Drug Targets, we focus on G protein-coupled receptors (GPCRs) that are involved in regulating body weight. In six reviews, the melanocortins system (including MC4 and MC3 receptors, Agrp, MSH), the NPY receptors (including NPY-Y1, NPY-Y2, and NPY-Y5, PYY3-36), the cannabinoid system (including the development of rimonabant), the ghrelin (GHS, growth hormone secretagogue) system, the monoamine GPCRs (including serotonin, adrenergic and histamine receptors), orexin (hypocretin) system and the galanin receptors are covered. In this overview, an introduction to the GPCRs and the field of central regulation of food intake is provided together with brief mentioning of some other GPCRs that are also implicated in regulation of body weight, such as the melanin-concentrating hormone (MCH), neuromedin U, prolactin-releasing peptide (PrRP), bombesin, cholecystokinin (CCK), Glucagon-like peptide-1 (GLP-1) (and oxyntomodulin), neuropeptide B (NPB) and neuropeptide W (NPW), opioids peptides, free fatty acid (FFA) receptors (GPR40, GPR41). In total over 40 GPCRs are listed that have been implicated to affect regulation of body weight.

  15. Snapin interacts with G-protein coupled receptor PKR2.

    PubMed

    Song, Jian; Li, Jie; Liu, Hua-die; Liu, Wei; Feng, Yong; Zhou, Xiao-Tao; Li, Jia-Da

    2016-01-15

    Mutations in Prokineticin receptor 2 (PKR2), a G-protein-coupled receptor, have been identified in patients with Kallmann syndrome and/or idiopathic hypogonadotropic hypogonadism, characterized by delayed puberty and infertility. In this study, we performed yeast two-hybrid screening by using PKR2 C-terminus (amino acids 333-384) as a bait, and identified Snapin as a novel interaction partner for PKR2. The interaction of Snapin and PKR2 was confirmed in GST pull-down and co-immunoprecipitation studies. We further demonstrated that two α-helix domains in Snapin are required for the interaction. And the interactive motifs of PKR2 were mapped to YFK (343-345) and HWR (351-353), which shared a similar sequence of two aromatic amino acids followed by a basic amino acid. Disruption of Snapin-PKR2 interaction did not affect PKR2 signaling, but increased the ligand-induced degradation, implying a role of Snapin in the trafficking of PKR2.

  16. Structure and Function of Serotonin G protein Coupled Receptors

    PubMed Central

    McCorvy, John D.; Roth, Bryan L.

    2015-01-01

    Serotonin receptors are prevalent throughout the nervous system and the periphery, and remain one of the most lucrative and promising drug discovery targets for disorders ranging from migraine headaches to neuropsychiatric disorders such as schizophrenia and depression. There are 14 distinct serotonin receptors, of which 13 are G protein coupled receptors (GPCRs), which are targets for approximately 40% of the approved medicines. Recent crystallographic and biochemical evidence has provided a converging understanding of the basic structure and functional mechanics of GPCR activation. Currently, two GPCR crystal structures exist for the serotonin family, the 5-HT1B and 5-HT2B receptor, with the antimigraine and valvulopathic drug ergotamine bound. The first serotonin crystal structures not only provide the first evidence of serotonin receptor topography but also provide mechanistic explanations into functional selectivity or biased agonism. This review will detail the findings of these crystal structures from a molecular and mutagenesis perspective for driving rational drug design for novel therapeutics incorporating biased signaling. PMID:25601315

  17. Coupled ER to Golgi Transport Reconstituted with Purified Cytosolic Proteins

    PubMed Central

    Barlowe, Charles

    1997-01-01

    A cell-free vesicle fusion assay that reproduces a subreaction in transport of pro-α-factor from the ER to the Golgi complex has been used to fractionate yeast cytosol. Purified Sec18p, Uso1p, and LMA1 in the presence of ATP and GTP satisfies the requirement for cytosol in fusion of ER-derived vesicles with Golgi membranes. Although these purified factors are sufficient for vesicle docking and fusion, overall ER to Golgi transport in yeast semi-intact cells depends on COPII proteins (components of a membrane coat that drive vesicle budding from the ER). Thus, membrane fusion is coupled to vesicle formation in ER to Golgi transport even in the presence of saturating levels of purified fusion factors. Manipulation of the semi-intact cell assay is used to distinguish freely diffusible ER- derived vesicles containing pro-α-factor from docked vesicles and from fused vesicles. Uso1p mediates vesicle docking and produces a dilution resistant intermediate. Sec18p and LMA1 are not required for the docking phase, but are required for efficient fusion of ER- derived vesicles with the Golgi complex. Surprisingly, elevated levels of Sec23p complex (a subunit of the COPII coat) prevent vesicle fusion in a reversible manner, but do not interfere with vesicle docking. Ordering experiments using the dilution resistant intermediate and reversible Sec23p complex inhibition indicate Sec18p action is required before LMA1 function. PMID:9382859

  18. Trafficking of ciliary G protein-coupled receptors.

    PubMed

    McIntyre, Jeremy C; Hege, Mellisa M; Berbari, Nicolas F

    2016-01-01

    In the last decade highly conserved cellular appendages called cilia have enjoyed a renewed interest from basic, biomedical scientists, and clinicians alike. This interest has grown upon the elucidation that cilia throughout the body serve as important sensory and signaling centers in both development and adult homeostasis. Furthermore, the identification of several rare genetic disorders associated with cilia dysfunction has broadened the field. However, even though their potential role in human health and disease is now recognized many basic questions about their functions remain. This chapter seeks to explore the trafficking of cilia-specific G protein-coupled receptors (GPCRs) and discusses several model systems in which this has been explored. We open the chapter by briefly discussing cilia and GPCRs then begin discussing some aspects of rhodopsin trafficking, arguably the most well studied of cilia GPCRs. We continue with sections on neuronal cilia and olfactory cilia receptor trafficking. Finally, we conclude with the emerging area of dynamic ciliary GPCR trafficking and speculate about future directions and some of the questions that remain for ciliary GPCRs. PMID:26928538

  19. Chimera patterns induced by distance-dependent power-law coupling in ecological networks

    NASA Astrophysics Data System (ADS)

    Banerjee, Tanmoy; Dutta, Partha Sharathi; Zakharova, Anna; Schöll, Eckehard

    2016-09-01

    This paper reports the occurrence of several chimera patterns and the associated transitions among them in a network of coupled oscillators, which are connected by a long-range interaction that obeys a distance-dependent power law. This type of interaction is common in physics and biology and constitutes a general form of coupling scheme, where by tuning the power-law exponent of the long-range interaction the coupling topology can be varied from local via nonlocal to global coupling. To explore the effect of the power-law coupling on collective dynamics, we consider a network consisting of a realistic ecological model of oscillating populations, namely the Rosenzweig-MacArthur model, and show that the variation of the power-law exponent mediates transitions between spatial synchrony and various chimera patterns. We map the possible spatiotemporal states and their scenarios that arise due to the interplay between the coupling strength and the power-law exponent.

  20. Coexistence of regular and irregular dynamics in complex networks of pulse-coupled oscillators.

    PubMed

    Timme, Marc; Wolf, Fred; Geisel, Theo

    2002-12-16

    For general networks of pulse-coupled oscillators, including regular, random, and more complex networks, we develop an exact stability analysis of synchronous states. As opposed to conventional stability analysis, here stability is determined by a multitude of linear operators. We treat this multioperator problem exactly and show that for inhibitory interactions the synchronous state is stable, independent of the parameters and the network connectivity. In randomly connected networks with strong interactions this synchronous state, displaying regular dynamics, coexists with a balanced state exhibiting irregular dynamics. External signals may switch the network between qualitatively distinct states.

  1. Social Networks and Structural Holes: Parent-School Relationships as Loosely Coupled Systems

    ERIC Educational Resources Information Center

    Wanat, Carolyn Louise; Zieglowsky, Laura Thudium

    2010-01-01

    This article describes parent groups as social networks that are loosely coupled to schools. The study investigated parent groups that work together to support schools by networking, responding to change, seeking input on policy decisions, and communicating with school leaders. Parents from one elementary school who participated in two focus group…

  2. Protein-flexibility mediated coupling between photoswitching kinetics and surrounding viscosity of a photochromic fluorescent protein.

    PubMed

    Kao, Ya-Ting; Zhu, Xinxin; Min, Wei

    2012-02-28

    Recent advances in fluorescent proteins (FPs) have generated a remarkable family of optical highlighters with special light responses. Among them, Dronpa exhibits a unique capability of reversible light-regulated on-off switching. However, the environmental dependence of this photochromism is largely unexplored. Herein we report that the photoswitching kinetics of the chromophore inside Dronpa is actually slowed down by increasing medium viscosity outside Dronpa. This finding is a special example of an FP where the environment can exert a hydrodynamic effect on the internal chromophore. We attribute this effect to protein-flexibility mediated coupling where the chromophore's cis-trans isomerization during photoswitching is accompanied by conformational motion of a part of the protein β-barrel whose dynamics should be hindered by medium friction. Consistent with this mechanism, the photoswitching kinetics of Dronpa-3, a structurally more flexible mutant, is found to exhibit a more pronounced viscosity dependence. Furthermore, we mapped out spatial distributions of microviscosity in live cells experienced by a histone protein using the photoswitching kinetics of Dronpa-3 fusion as a contrast mechanism. This unique reporter should provide protein-specific information about the crowded intracellular environments by offering a genetically encoded microviscosity probe, which did not exist with normal FPs before. PMID:22328153

  3. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome.

    PubMed

    Poirot, Olivier; Timsit, Youri

    2016-01-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through "molecular synapses", ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the "sensory-proteins" innervate the functional ribosomal sites, while the "inter-proteins" interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. PMID:27225526

  4. Variety of alternative stable phase-locking in networks of electrically coupled relaxation oscillators.

    PubMed

    Meyrand, Pierre; Bem, Tiaza

    2014-01-01

    We studied the dynamics of a large-scale model network comprised of oscillating electrically coupled neurons. Cells are modeled as relaxation oscillators with short duty cycle, so they can be considered either as models of pacemaker cells, spiking cells with fast regenerative and slow recovery variables or firing rate models of excitatory cells with synaptic depression or cellular adaptation. It was already shown that electrically coupled relaxation oscillators exhibit not only synchrony but also anti-phase behavior if electrical coupling is weak. We show that a much wider spectrum of spatiotemporal patterns of activity can emerge in a network of electrically coupled cells as a result of switching from synchrony, produced by short external signals of different spatial profiles. The variety of patterns increases with decreasing rate of neuronal firing (or duty cycle) and with decreasing strength of electrical coupling. We study also the effect of network topology--from all-to-all--to pure ring connectivity, where only the closest neighbors are coupled. We show that the ring topology promotes anti-phase behavior as compared to all-to-all coupling. It also gives rise to a hierarchical organization of activity: during each of the main phases of a given pattern cells fire in a particular sequence determined by the local connectivity. We have analyzed the behavior of the network using geometric phase plane methods and we give heuristic explanations of our findings. Our results show that complex spatiotemporal activity patterns can emerge due to the action of stochastic or sensory stimuli in neural networks without chemical synapses, where each cell is equally coupled to others via gap junctions. This suggests that in developing nervous systems where only electrical coupling is present such a mechanism can lead to the establishment of proto-networks generating premature multiphase oscillations whereas the subsequent emergence of chemical synapses would later stabilize

  5. Variety of alternative stable phase-locking in networks of electrically coupled relaxation oscillators.

    PubMed

    Meyrand, Pierre; Bem, Tiaza

    2014-01-01

    We studied the dynamics of a large-scale model network comprised of oscillating electrically coupled neurons. Cells are modeled as relaxation oscillators with short duty cycle, so they can be considered either as models of pacemaker cells, spiking cells with fast regenerative and slow recovery variables or firing rate models of excitatory cells with synaptic depression or cellular adaptation. It was already shown that electrically coupled relaxation oscillators exhibit not only synchrony but also anti-phase behavior if electrical coupling is weak. We show that a much wider spectrum of spatiotemporal patterns of activity can emerge in a network of electrically coupled cells as a result of switching from synchrony, produced by short external signals of different spatial profiles. The variety of patterns increases with decreasing rate of neuronal firing (or duty cycle) and with decreasing strength of electrical coupling. We study also the effect of network topology--from all-to-all--to pure ring connectivity, where only the closest neighbors are coupled. We show that the ring topology promotes anti-phase behavior as compared to all-to-all coupling. It also gives rise to a hierarchical organization of activity: during each of the main phases of a given pattern cells fire in a particular sequence determined by the local connectivity. We have analyzed the behavior of the network using geometric phase plane methods and we give heuristic explanations of our findings. Our results show that complex spatiotemporal activity patterns can emerge due to the action of stochastic or sensory stimuli in neural networks without chemical synapses, where each cell is equally coupled to others via gap junctions. This suggests that in developing nervous systems where only electrical coupling is present such a mechanism can lead to the establishment of proto-networks generating premature multiphase oscillations whereas the subsequent emergence of chemical synapses would later stabilize

  6. Does hyperbolicity impede emergence of chimera states in networks of nonlocally coupled chaotic oscillators?

    NASA Astrophysics Data System (ADS)

    Semenova, N.; Zakharova, A.; Schöll, E.; Anishchenko, V.

    2015-11-01

    We analyze nonlocally coupled networks of identical chaotic oscillators with either time-discrete or time-continuous dynamics (Henon map, Lozi map, Lorenz system). We hypothesize that chimera states, in which spatial domains of coherent (synchronous) and incoherent (desynchronized) dynamics coexist, can be obtained only in networks of oscillators with nonhyperbolic chaotic attractors and cannot be found in networks of systems with hyperbolic chaotic attractors. This hypothesis is supported by analytical results and numerical simulations for hyperbolic and nonhyperbolic cases.

  7. Steps towards a repertoire of comprehensive maps of human protein interaction networks: the Human Proteotheque Initiative (HuPI).

    PubMed

    Coulombe, Benoit; Blanchette, Mathieu; Jeronimo, Célia

    2008-04-01

    Defining human protein interaction networks has become essential to develop an overall, systems-based understanding of the molecular events that sustain cell growth in normal and disease conditions. To characterize protein interaction networks from human cells, we have undertaken the development of a systematic, unbiased technology pipeline that couples experimental and computational approaches. This discovery engine is central to the Human Proteotheque Initiative (HuPI), a multidisciplinary project aimed at building a repertoire of comprehensive maps of human protein interaction networks, the Human Proteotheque. The information contained in the Proteotheque is made publicly available through an interactive web site that can be consulted to visualize some of the fundamental molecular connections formed in human cells and to determine putative functions of previously uncharacterized proteins based on guilt by association. The process governing the evolution of HuPI towards becoming a repository of accurate and complete protein interaction maps is described.

  8. The role of gap junction proteins in the development of neural network functional topology.

    PubMed

    Anava, S; Saad, Y; Ayali, A

    2013-10-01

    Gap junctions (GJs) provide a common form of intercellular communication in most animal cells and tissues, from Hydra to human, including electrical synaptic signalling. Cell coupling via GJs has an important role in development in general, and in neural network development in particular. However, quantitative studies monitoring GJ proteins throughout nervous system development are few. Direct investigations demonstrating a role for GJ proteins by way of experimental manipulation of their expression are also rare. In the current work we focused on the role of invertebrate GJ proteins (innexins) in the in vitro development of neural network functional topology, using two-dimensional neural culture preparations derived from the frontal ganglion of the desert locust, Schistocerca gregaria. Immunocytochemistry and quantitative real-time PCR revealed a dynamic expression pattern of the innexins during development of the cultured networks. Changes were observed both in the levels and in the localization of expression. Down-regulating the expression of innexins, by using double-strand RNA for the first time in locust neural cultures, induced clear changes in network morphology, as well as inhibition of synaptogenesis, thus suggesting a role for GJs during the development of the functional topology of neuronal networks.

  9. Adaptive Control of Synchronization in Delay-Coupled Heterogeneous Networks of FitzHugh-Nagumo Nodes

    NASA Astrophysics Data System (ADS)

    Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

    We study synchronization in delay-coupled neural networks of heterogeneous nodes. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. We show that an adaptive tuning of the overall coupling strength can be used to counteract the effect of the heterogeneity. Our adaptive controller is demonstrated on ring networks of FitzHugh-Nagumo systems which are paradigmatic for excitable dynamics but can also — depending on the system parameters — exhibit self-sustained periodic firing. We show that the adaptively tuned time-delayed coupling enables synchronization even if parameter heterogeneities are so large that excitable nodes coexist with oscillatory ones.

  10. Detecting protein complexes from active protein interaction networks constructed with dynamic gene expression profiles

    PubMed Central

    2013-01-01

    Background Protein interaction networks (PINs) are known to be useful to detect protein complexes. However, most available PINs are static, which cannot reflect the dynamic changes in real networks. At present, some researchers have tried to construct dynamic networks by incorporating time-course (dynamic) gene expression data with PINs. However, the inevitable background noise exists in the gene expression array, which could degrade the quality of dynamic networkds. Therefore, it is needed to filter out contaminated gene expression data before further data integration and analysis. Results Firstly, we adopt a dynamic model-based method to filter noisy data from dynamic expression profiles. Then a new method is proposed for identifying active proteins from dynamic gene expression profiles. An active protein at a time point is defined as the protein the expression level of whose corresponding gene at that time point is higher than a threshold determined by a standard variance involved threshold function. Furthermore, a noise-filtered active protein interaction network (NF-APIN) is constructed. To demonstrate the efficiency of our method, we detect protein complexes from the NF-APIN, compared with those from other dynamic PINs. Conclusion A dynamic model based method can effectively filter out noises in dynamic gene expression data. Our method to compute a threshold for determining the active time points of noise-filtered genes can make the dynamic construction more accuracy and provide a high quality framework for network analysis, such as protein complex prediction. PMID:24565281

  11. Master stability islands for amplitude death in networks of delay-coupled oscillators.

    PubMed

    Huddy, Stanley R; Sun, Jie

    2016-05-01

    This paper presents a master stability function (MSF) approach for analyzing the stability of amplitude death (AD) in networks of delay-coupled oscillators. Unlike the familiar MSFs for instantaneously coupled networks, which typically have a single input encoding for the effects of the eigenvalues of the network Laplacian matrix, for delay-coupled networks we show that such MSFs generally require two additional inputs: the time delay and the coupling strength. To utilize the MSF for determining the stability of AD of general networks for a chosen nonlinear system (node dynamics) and coupling function, we introduce the concept of master stability islands (MSIs), which are two-dimensional stability islands of the delay-coupling parameter space together with a third dimension ("altitude") encoding for eigenvalues that result in stable AD. We numerically compute the MSFs and visualize the corresponding MSIs for several common chaotic systems including the Rössler, the Lorenz, and Chen's system and find that it is generally possible to achieve AD and that a nonzero time delay is necessary for the stabilization of the AD states.

  12. Master stability islands for amplitude death in networks of delay-coupled oscillators

    NASA Astrophysics Data System (ADS)

    Huddy, Stanley R.; Sun, Jie

    2016-05-01

    This paper presents a master stability function (MSF) approach for analyzing the stability of amplitude death (AD) in networks of delay-coupled oscillators. Unlike the familiar MSFs for instantaneously coupled networks, which typically have a single input encoding for the effects of the eigenvalues of the network Laplacian matrix, for delay-coupled networks we show that such MSFs generally require two additional inputs: the time delay and the coupling strength. To utilize the MSF for determining the stability of AD of general networks for a chosen nonlinear system (node dynamics) and coupling function, we introduce the concept of master stability islands (MSIs), which are two-dimensional stability islands of the delay-coupling parameter space together with a third dimension ("altitude") encoding for eigenvalues that result in stable AD. We numerically compute the MSFs and visualize the corresponding MSIs for several common chaotic systems including the Rössler, the Lorenz, and Chen's system and find that it is generally possible to achieve AD and that a nonzero time delay is necessary for the stabilization of the AD states.

  13. Functional module identification in protein interaction networks by interaction patterns

    PubMed Central

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP2 by random walk. A spectral approximate algorithm SLCP2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP2 to a new algorithm (greedy algorithm for LCP2) GLCP2 to identify overlapping functional modules. We compare SLCP2 and GLCP2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/∼xqian/fmi/slcp2hop

  14. A novel framework of classical and quantum prisoner's dilemma games on coupled networks.

    PubMed

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-01-01

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner's dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner's dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner's dilemma is greatly impacted by the combined effect of entanglement and coupling.

  15. A novel framework of classical and quantum prisoner’s dilemma games on coupled networks

    PubMed Central

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-01-01

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner’s dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner’s dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner’s dilemma is greatly impacted by the combined effect of entanglement and coupling. PMID:26975447

  16. Evolutionarily Conserved Network Properties of Intrinsically Disordered Proteins

    PubMed Central

    Rangarajan, Nivedita; Kulkarni, Prakash; Hannenhalli, Sridhar

    2015-01-01

    Background Intrinsically disordered proteins (IDPs) lack a stable tertiary structure in isolation. Remarkably, however, a substantial portion of IDPs undergo disorder-to-order transitions upon binding to their cognate partners. Structural flexibility and binding plasticity enable IDPs to interact with a broad range of partners. However, the broader network properties that could provide additional insights into the functional role of IDPs are not known. Results Here, we report the first comprehensive survey of network properties of IDP-induced sub-networks in multiple species from yeast to human. Our results show that IDPs exhibit greater-than-expected modularity and are connected to the rest of the protein interaction network (PIN) via proteins that exhibit the highest betweenness centrality and connect to fewer-than-expected IDP communities, suggesting that they form critical communication links from IDP modules to the rest of the PIN. Moreover, we found that IDPs are enriched at the top level of regulatory hierarchy. Conclusion Overall, our analyses reveal coherent and remarkably conserved IDP-centric network properties, namely, modularity in IDP-induced network and a layer of critical nodes connecting IDPs with the rest of the PIN. PMID:25974317

  17. A Social Network Comparison of Low-Income Black and White Newlywed Couples

    PubMed Central

    Jackson, Grace L.; Kennedy, David; Bradbury, Thomas N.; Karney, Benjamin R.

    2014-01-01

    Relative to White families, Black families have been described as relying on extended social networks to compensate for other social and economic disadvantages. The presence or absence of supportive social networks should be especially relevant to young couples entering marriage, but to date there has been little effort to describe the social networks of comparable Black and White newlyweds. The current study addressed this gap by drawing on interviews with 57 first-married newlyweds from low-income communities to compare the composition and structure of Black and White couples’ duocentric social networks. The results indicated that low-income Black couples entered marriage at a social disadvantage relative to White couples, with more family relationships but fewer positive relationships and fewer sources of emotional support (for wives), fewer connections to married individuals, and fewer shared relationships between spouses. Black couples’ relative social disadvantages persisted even when various economic and demographic variables were controlled. PMID:25214673

  18. Optimizing a global alignment of protein interaction networks

    PubMed Central

    Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie

    2013-01-01

    Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352

  19. A ligand channel through the G protein coupled receptor opsin.

    PubMed

    Hildebrand, Peter W; Scheerer, Patrick; Park, Jung Hee; Choe, Hui-Woog; Piechnick, Ronny; Ernst, Oliver P; Hofmann, Klaus Peter; Heck, Martin

    2009-01-01

    The G protein coupled receptor rhodopsin contains a pocket within its seven-transmembrane helix (TM) structure, which bears the inactivating 11-cis-retinal bound by a protonated Schiff-base to Lys296 in TM7. Light-induced 11-cis-/all-trans-isomerization leads to the Schiff-base deprotonated active Meta II intermediate. With Meta II decay, the Schiff-base bond is hydrolyzed, all-trans-retinal is released from the pocket, and the apoprotein opsin reloaded with new 11-cis-retinal. The crystal structure of opsin in its active Ops* conformation provides the basis for computational modeling of retinal release and uptake. The ligand-free 7TM bundle of opsin opens into the hydrophobic membrane layer through openings A (between TM1 and 7), and B (between TM5 and 6), respectively. Using skeleton search and molecular docking, we find a continuous channel through the protein that connects these two openings and comprises in its central part the retinal binding pocket. The channel traverses the receptor over a distance of ca. 70 A and is between 11.6 and 3.2 A wide. Both openings are lined with aromatic residues, while the central part is highly polar. Four constrictions within the channel are so narrow that they must stretch to allow passage of the retinal beta-ionone-ring. Constrictions are at openings A and B, respectively, and at Trp265 and Lys296 within the retinal pocket. The lysine enforces a 90 degrees elbow-like kink in the channel which limits retinal passage. With a favorable Lys side chain conformation, 11-cis-retinal can take the turn, whereas passage of the all-trans isomer would require more global conformational changes. We discuss possible scenarios for the uptake of 11-cis- and release of all-trans-retinal. If the uptake gate of 11-cis-retinal is assigned to opening B, all-trans is likely to leave through the same gate. The unidirectional passage proposed previously requires uptake of 11-cis-retinal through A and release of photolyzed all-trans-retinal through

  20. Discovery of three novel orphan G-protein-coupled receptors.

    PubMed

    Marchese, A; Sawzdargo, M; Nguyen, T; Cheng, R; Heng, H H; Nowak, T; Im, D S; Lynch, K R; George, S R; O'dowd, B F

    1999-02-15

    We have discovered three novel human genes, GPR34, GPR44, and GPR45, encoding family A G-protein-coupled receptors (GPCRs). The receptor encoded by GPR34 is most similar to the P2Y receptor subfamily, while the receptor encoded by GPR44 is most similar to chemoattractant receptors. The receptor encoded by GPR45 is the mammalian orthologue of a putative lysophosphatidic acid receptor from Xenopus laevis. Partial sequence of GPR34 was discovered during a search of the GenBank database of expressed sequence tags (ESTs). This sequence information was used both to isolate the full-length translational open reading frame from a human genomic library and to assemble a contig from additional GPR34 EST cDNAs. Northern blot and in situ hybridization analyses revealed GPR34 mRNA transcripts in several human and rat brain regions. Also, we used polymerase chain reaction (PCR) to amplify human genomic DNA using degenerate oligonucleotides designed from sequences encoding transmembrane domains 3 and 7 of opioid and somatostatin receptors. Two PCR products partially encoding novel GPCRs, named GPR44 and GPR45, were discovered and used to isolate the full-length translational open reading frames from a human genomic library. Both GPR44 and GPR45 are expressed in the central nervous system and periphery. For chromosomal localization, fluorescence in situ hybridization analysis was performed to assign GPR34 to chromosomes 4p12 and Xp11. 3, GPR44 to chromosome 11q12-q13.3, and GPR45 to chromosome 2q11. 1-q12. PMID:10036181

  1. Optimizing information flow in small genetic networks. IV. Spatial coupling

    NASA Astrophysics Data System (ADS)

    Sokolowski, Thomas R.; Tkačik, Gašper

    2015-06-01

    We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the input) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively different regulatory strategy emerges where individual cells respond to the input in a nearly steplike fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.

  2. A complex-centric view of protein network evolution.

    PubMed

    Yosef, Nir; Kupiec, Martin; Ruppin, Eytan; Sharan, Roded

    2009-07-01

    The recent availability of protein-protein interaction networks for several species makes it possible to study protein complexes in an evolutionary context. In this article, we present a novel network-based framework for reconstructing the evolutionary history of protein complexes. Our analysis is based on generalizing evolutionary measures for single proteins to the level of whole subnetworks, comprehensively considering a broad set of computationally derived complexes and accounting for both sequence and interaction changes. Specifically, we compute sets of orthologous complexes across species, and use these to derive evolutionary rate and age measures for protein complexes. We observe significant correlations between the evolutionary properties of a complex and those of its member proteins, suggesting that protein complexes form early in evolution and evolve as coherent units. Additionally, our approach enables us to directly quantify the extent to which gene duplication has played a role in the evolution of complexes. We find that about one quarter of the sets of orthologous complexes have originated from evolutionary cores of homodimers that underwent duplication and divergence, testifying to the important role of gene duplication in protein complex evolution. PMID:19465379

  3. Protein function prediction using guilty by association from interaction networks.

    PubMed

    Piovesan, Damiano; Giollo, Manuel; Ferrari, Carlo; Tosatto, Silvio C E

    2015-12-01

    Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.

  4. Influence of homology and node age on the growth of protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Bottinelli, Arianna; Bassetti, Bruno; Lagomarsino, Marco Cosentino; Gherardi, Marco

    2012-10-01

    Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

  5. Distinct G protein-coupled receptor recycling pathways allow spatial control of downstream G protein signaling.

    PubMed

    Bowman, Shanna Lynn; Shiwarski, Daniel John; Puthenveedu, Manojkumar A

    2016-09-26

    G protein-coupled receptors (GPCRs) are recycled via a sequence-dependent pathway that is spatially and biochemically distinct from bulk recycling. Why there are two distinct recycling pathways from the endosome is a fundamental question in cell biology. In this study, we show that the separation of these two pathways is essential for normal spatial encoding of GPCR signaling. The prototypical β-2 adrenergic receptor (B2AR) activates Gα stimulatory protein (Gαs) on the endosome exclusively in sequence-dependent recycling tubules marked by actin/sorting nexin/retromer tubular (ASRT) microdomains. B2AR was detected in an active conformation in bulk recycling tubules, but was unable to activate Gαs. Protein kinase A phosphorylation of B2AR increases the fraction of receptors localized to ASRT domains and biases the downstream transcriptional effects of B2AR to genes controlled by endosomal signals. Our results identify the physiological relevance of separating GPCR recycling from bulk recycling and suggest a mechanism to tune downstream responses of GPCR signaling by manipulating the spatial origin of G protein signaling. PMID:27646272

  6. Distinct G protein-coupled receptor recycling pathways allow spatial control of downstream G protein signaling.

    PubMed

    Bowman, Shanna Lynn; Shiwarski, Daniel John; Puthenveedu, Manojkumar A

    2016-09-26

    G protein-coupled receptors (GPCRs) are recycled via a sequence-dependent pathway that is spatially and biochemically distinct from bulk recycling. Why there are two distinct recycling pathways from the endosome is a fundamental question in cell biology. In this study, we show that the separation of these two pathways is essential for normal spatial encoding of GPCR signaling. The prototypical β-2 adrenergic receptor (B2AR) activates Gα stimulatory protein (Gαs) on the endosome exclusively in sequence-dependent recycling tubules marked by actin/sorting nexin/retromer tubular (ASRT) microdomains. B2AR was detected in an active conformation in bulk recycling tubules, but was unable to activate Gαs. Protein kinase A phosphorylation of B2AR increases the fraction of receptors localized to ASRT domains and biases the downstream transcriptional effects of B2AR to genes controlled by endosomal signals. Our results identify the physiological relevance of separating GPCR recycling from bulk recycling and suggest a mechanism to tune downstream responses of GPCR signaling by manipulating the spatial origin of G protein signaling.

  7. Towards a matrix mechanics framework for dynamic protein network

    PubMed Central

    2010-01-01

    Protein–protein interaction networks are currently visualized by software generated interaction webs based upon static experimental data. Current state is limited to static, mostly non-compartmental network and non time resolved protein interactions. A satisfactory mathematical foundation for particle interactions within a viscous liquid state (situation within the cytoplasm) does not exist nor do current computer programs enable building dynamic interaction networks for time resolved interactions. Building mathematical foundation for intracellular protein interactions can be achieved in two increments (a) trigger and capture the dynamic molecular changes for a select subset of proteins using several model systems and high throughput time resolved proteomics and, (b) use this information to build the mathematical foundation and computational algorithm for a compartmentalized and dynamic protein interaction network. Such a foundation is expected to provide benefit in at least two spheres: (a) understanding physiology enabling explanation of phenomenon such as incomplete penetrance in genetic disorders and (b) enabling several fold increase in biopharmaceutical production using impure starting materials. PMID:20805933

  8. Visualization of coupled protein folding and binding in bacteria and purification of the heterodimeric complex

    NASA Astrophysics Data System (ADS)

    Wang, Haoyong; Chong, Shaorong

    2003-01-01

    During overexpression of recombinant proteins in Escherichia coli, misfolded proteins often aggregate and form inclusion bodies. If an aggregation-prone recombinant protein is fused upstream (as an N-terminal fusion) to GFP, aggregation of the recombinant protein domain also leads to misfolding of the downstream GFP domain, resulting in a decrease or loss of fluorescence. We investigated whether the GFP domain could fold correctly if aggregation of the upstream protein domain was prevented in vivo by a coupled protein folding and binding interaction. Such interaction has been previously shown to occur between the E. coli integration host factors and , and between the domains of the general transcriptional coactivator cAMP response element binding protein (CREB)-binding protein and the activator for thyroid hormone and retinoid receptors. In this study, fusion of integration host factor or the CREB-binding protein domain upstream to GFP resulted in aggregation of the fusion protein. Coexpression of their respective partners, on the other hand, allowed soluble expression of the fusion protein and a dramatic increase in fluorescence. The study demonstrated that coupled protein folding and binding could be correlated to GFP fluorescence. A modified miniintein containing an affinity tag was inserted between the upstream protein domain and GFP to allow rapid purification and identification of the heterodimeric complex. The GFP coexpression fusion system may be used to identify novel protein-protein interactions that involve coupled folding and binding or protein partners that can solubilize aggregation-prone recombinant proteins.

  9. Reconstruction and Application of Protein–Protein Interaction Network

    PubMed Central

    Hao, Tong; Peng, Wei; Wang, Qian; Wang, Bin; Sun, Jinsheng

    2016-01-01

    The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms. PMID:27338356

  10. A novel approach to synchronization of nonlinearly coupled network systems with delays

    NASA Astrophysics Data System (ADS)

    Tseng, Jui-Pin

    2016-06-01

    In this investigation, a novel approach to establishing the global synchronization of coupled network systems is presented. Under this approach, individual subsystems can be non-autonomous, and the coupling configuration is rather general. The coupling terms can be non-diffusive, nonlinear, time-dependent, asymmetric, and with time delays. With an iteration scheme, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, delay-dependent and delay-independent criteria for global synchronization can be established. We implement the present approach to analyze synchronization of the FitzHugh-Nagumo systems under delayed and nonlinear sigmoidal coupling. Two examples are presented to demonstrate new dynamical scenarios, where oscillatory behavior and multistability emerge or are suppressed as the coupled neurons synchronize under the synchronization criterion. In addition, asynchrony induced by the coupling strength or coupling delay occurs while the synchronization criterion is violated.

  11. Learning virulent proteins from integrated query networks

    PubMed Central

    2012-01-01

    Background Methods of weakening and attenuating pathogens’ abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories. Results A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction. Conclusions This approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories. PMID:23198735

  12. Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands.

    PubMed

    Xie, Xiang-Qun; Wang, Lirong; Liu, Haibin; Ouyang, Qin; Fang, Cheng; Su, Weiwei

    2014-01-01

    Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA. PMID:24567719

  13. Chemogenomics knowledgebased polypharmacology analyses of drug abuse related G-protein coupled receptors and their ligands

    PubMed Central

    Xie, Xiang-Qun; Wang, Lirong; Liu, Haibin; Ouyang, Qin; Fang, Cheng; Su, Weiwei

    2013-01-01

    Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA. PMID:24567719

  14. Synergistic plasticity of intrinsic conductance and electrical coupling restores synchrony in an intact motor network.

    PubMed

    Lane, Brian J; Samarth, Pranit; Ransdell, Joseph L; Nair, Satish S; Schulz, David J

    2016-01-01

    Motor neurons of the crustacean cardiac ganglion generate virtually identical, synchronized output despite the fact that each neuron uses distinct conductance magnitudes. As a result of this variability, manipulations that target ionic conductances have distinct effects on neurons within the same ganglion, disrupting synchronized motor neuron output that is necessary for proper cardiac function. We hypothesized that robustness in network output is accomplished via plasticity that counters such destabilizing influences. By blocking high-threshold K(+) conductances in motor neurons within the ongoing cardiac network, we discovered that compensation both resynchronized the network and helped restore excitability. Using model findings to guide experimentation, we determined that compensatory increases of both GA and electrical coupling restored function in the network. This is one of the first direct demonstrations of the physiological regulation of coupling conductance in a compensatory context, and of synergistic plasticity across cell- and network-level mechanisms in the restoration of output. PMID:27552052

  15. Contagion processes on the static and activity-driven coupling networks

    NASA Astrophysics Data System (ADS)

    Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

    2016-03-01

    The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

  16. GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble.

    PubMed

    Naveed, Muhammad; Khan, Asifullah; Khan, Asif Ullah

    2012-05-01

    G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at http://111.68.99.218/gpcr-mpredictor/.

  17. Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics.

    PubMed

    Karthikeyan, Bagavathy Shanmugam; Akbarsha, Mohammad Abdulkader; Parthasarathy, Subbiah

    2016-06-21

    Cytochrome P450 (CYP) enzymes that degrade xenobiotics play a critical role in the metabolism and biotransformation of drugs and xenobiotics in humans as well as experimental animal models such as mouse and rat. These proteins function as a network collectively as well as independently. Though there are several reports on the organization, regulation and functionality of various CYP enzymes at the molecular level, the understanding of organization and functionality of these proteins at the holistic level remain unclear. The objective of this study is to understand the organization and functionality of xenobiotic degrading CYP enzymes of human, mouse and rat using network theory approaches and to study species differences that exist among them at the holistic level. For our analysis, a protein-protein interaction (PPI) network for CYP enzymes of human, mouse and rat was constructed using the STRING database. Topology, centrality, modularity and robustness analyses were performed for our predicted CYP PPI networks that were then validated by comparison with randomly generated network models. Network centrality analyses of CYP PPI networks reveal the central/hub proteins in the network. Modular analysis of the CYP PPI networks of human, mouse and rat resulted in functional clusters. These clusters were subjected to ontology and pathway enrichment analysis. The analyses show that the cluster of the human CYP PPI network is enriched with pathways principally related to xenobiotic/drug metabolism. Endo-xenobiotic crosstalk dominated in mouse and rat CYP PPI networks, and they were highly enriched with endogenous metabolic and signaling pathways. Thus, cross-species comparisons and analyses of human, mouse and rat CYP PPI networks gave insights about species differences that existed at the holistic level. More investigations from both reductionist and holistic perspectives can help understand CYP metabolism and species extrapolation in a much better way. PMID:27194593

  18. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters

    PubMed Central

    Hanna, Eileen Marie; Zaki, Nazar; Amin, Amr

    2015-01-01

    Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present “DyCluster”, a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster. PMID:26641660

  19. Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks

    NASA Astrophysics Data System (ADS)

    Barranca, Victor J.; Zhou, Douglas; Cai, David

    2016-06-01

    Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.

  20. Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks.

    PubMed

    Barranca, Victor J; Zhou, Douglas; Cai, David

    2016-06-01

    Utilizing the sparsity ubiquitous in real-world network connectivity, we develop a theoretical framework for efficiently reconstructing sparse feed-forward connections in a pulse-coupled nonlinear network through its output activities. Using only a small ensemble of random inputs, we solve this inverse problem through the compressive sensing theory based on a hidden linear structure intrinsic to the nonlinear network dynamics. The accuracy of the reconstruction is further verified by the fact that complex inputs can be well recovered using the reconstructed connectivity. We expect this Rapid Communication provides a new perspective for understanding the structure-function relationship as well as compressive sensing principle in nonlinear network dynamics.

  1. Transactivation of Epidermal Growth Factor Receptor by G Protein-Coupled Receptors: Recent Progress, Challenges and Future Research

    PubMed Central

    Wang, Zhixiang

    2016-01-01

    Both G protein-coupled receptors (GPCRs) and receptor-tyrosine kinases (RTKs) regulate large signaling networks, control multiple cell functions and are implicated in many diseases including various cancers. Both of them are also the top therapeutic targets for disease treatment. The discovery of the cross-talk between GPCRs and RTKs connects these two vast signaling networks and complicates the already complicated signaling networks that regulate cell signaling and function. In this review, we focus on the transactivation of epidermal growth factor receptor (EGFR), a subfamily of RTKs, by GPCRs. Since the first report of EGFR transactivation by GPCR, significant progress has been made including the elucidation of the mechanisms underlying the transactivation. Here, we first provide a basic picture for GPCR, EGFR and EGFR transactivation by GPCR. We then discuss the progress made in the last five years and finally provided our view of the future challenge and future researches needed to overcome these challenges. PMID:26771606

  2. Decoding protein networks during virus entry by quantitative proteomics.

    PubMed

    Gerold, Gisa; Bruening, Janina; Pietschmann, Thomas

    2016-06-15

    Virus entry into host cells relies on interactions between viral and host structures including lipids, carbohydrates and proteins. Particularly, protein-protein interactions between viral surface proteins and host proteins as well as secondary host protein-protein interactions play a pivotal role in coordinating virus binding and uptake. These interactions are dynamic and frequently involve multiprotein complexes. In the past decade mass spectrometry based proteomics methods have reached sensitivities and high throughput compatibilities of genomics methods and now allow the reliable quantitation of proteins in complex samples from limited material. As proteomics provides essential information on the biologically active entity namely the protein, including its posttranslational modifications and its interactions with other proteins, it is an indispensable method in the virologist's toolbox. Here we review protein interactions during virus entry and compare classical biochemical methods to study entry with novel technically advanced quantitative proteomics techniques. We highlight the value of quantitative proteomics in mapping functional virus entry networks, discuss the benefits and limitations and illustrate how the methodology will help resolve unsettled questions in virus entry research in the future. PMID:26365680

  3. β-Arrestin-Selective G Protein-Coupled Receptor Agonists Engender Unique Biological Efficacy in Vivo

    PubMed Central

    Gesty-Palmer, Diane; Yuan, Ling; Martin, Bronwen; Wood, William H.; Lee, Mi-Hye; Janech, Michael G.; Tsoi, Lam C.; Zheng, W. Jim; Maudsley, Stuart

    2013-01-01

    Biased G protein-coupled receptor agonists are orthosteric ligands that possess pathway-selective efficacy, activating or inhibiting only a subset of the signaling repertoire of their cognate receptors. In vitro, d-Trp12,Tyr34-bPTH(7–34) [bPTH(7–34)], a biased agonist for the type 1 PTH receptor, antagonizes receptor-G protein coupling but activates arrestin-dependent signaling. In vivo, both bPTH(7–34) and the conventional agonist hPTH(1–34) stimulate anabolic bone formation. To understand how two PTH receptor ligands with markedly different in vitro efficacy could elicit similar in vivo responses, we analyzed transcriptional profiles from calvarial bone of mice treated for 8 wk with vehicle, bPTH(7–34) or hPTH(1–34). Treatment of wild-type mice with bPTH(7–34) primarily affected pathways that promote expansion of the osteoblast pool, notably cell cycle regulation, cell survival, and migration. These responses were absent in β-arrestin2-null mice, identifying them as downstream targets of β-arrestin2-mediated signaling. In contrast, hPTH(1–34) primarily affected pathways classically associated with enhanced bone formation, including collagen synthesis and matrix mineralization. hPTH(1–34) actions were less dependent on β-arrestin2, as might be expected of a ligand capable of G protein activation. In vitro, bPTH(7–34) slowed the rate of preosteoblast proliferation, enhanced osteoblast survival when exposed to an apoptotic stimulus, and stimulated cell migration in wild-type, but not β-arrestin2-null, calvarial osteoblasts. These results suggest that bPTH(7–34) and hPTH(1–34) affect bone mass in vivo through predominantly separate genomic mechanisms created by largely distinct receptor-signaling networks and demonstrate that functional selectivity can be exploited to change the quality of G protein-coupled receptor efficacy. PMID:23315939

  4. P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

    PubMed

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

    Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

  5. Transient spatiotemporal chaos in a diffusively and synaptically coupled Morris-Lecar neuronal network

    NASA Astrophysics Data System (ADS)

    Lafranceschina, Jacopo

    Transient spatiotemporal chaos was reported in models for chemical reactions and in experiments for turbulence in shear flow. This study shows that transient spatiotemporal chaos also exists in a diffusively coupled Morris-Lecar (ML) neuronal network, with a collapse to either a global rest state or to a state of pulse propagation. Adding synaptic coupling to this network reduces the average lifetime of spatiotemporal chaos for small to intermediate coupling strengths and almost all numbers of synapses. For large coupling strengths, close to the threshold of excitation, the average lifetime increases beyond the value for only diffusive coupling, and the collapse to the rest state dominates over the collapse to a traveling pulse state. The regime of spatiotemporal chaos is characterized by a slightly increasing Lyapunov exponent and degree of phase coherence as the number of synaptic links increases. In contrast to the diffusive network, the pulse solution must not be asymptotic in the presence of synapses. The fact that chaos could be transient in higher dimensional systems, such as the one being explored in this study, point to its presence in every day life. Transient spatiotemporal chaos in a network of coupled neurons and the associated chaotic saddle provide a possibility for switching between metastable states observed in information processing and brain function. Such transient dynamics have been observed experimentally by Mazor, when stimulating projection neurons in the locust antennal lobe with different odors.

  6. Mining the Modular Structure of Protein Interaction Networks

    PubMed Central

    Furlong, Laura Inés; Chernomoretz, Ariel

    2015-01-01

    Background Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. Methodology We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera’s cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. Results As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge. PMID:25856434

  7. Dynamical Coupling of Intrinsically Disordered Proteins and Their Hydration Water: Comparison with Folded Soluble and Membrane Proteins

    PubMed Central

    Gallat, F.-X.; Laganowsky, A.; Wood, K.; Gabel, F.; van Eijck, L.; Wuttke, J.; Moulin, M.; Härtlein, M.; Eisenberg, D.; Colletier, J.-P.; Zaccai, G.; Weik, M.

    2012-01-01

    Hydration water is vital for various macromolecular biological activities, such as specific ligand recognition, enzyme activity, response to receptor binding, and energy transduction. Without hydration water, proteins would not fold correctly and would lack the conformational flexibility that animates their three-dimensional structures. Motions in globular, soluble proteins are thought to be governed to a certain extent by hydration-water dynamics, yet it is not known whether this relationship holds true for other protein classes in general and whether, in turn, the structural nature of a protein also influences water motions. Here, we provide insight into the coupling between hydration-water dynamics and atomic motions in intrinsically disordered proteins (IDP), a largely unexplored class of proteins that, in contrast to folded proteins, lack a well-defined three-dimensional structure. We investigated the human IDP tau, which is involved in the pathogenic processes accompanying Alzheimer disease. Combining neutron scattering and protein perdeuteration, we found similar atomic mean-square displacements over a large temperature range for the tau protein and its hydration water, indicating intimate coupling between them. This is in contrast to the behavior of folded proteins of similar molecular weight, such as the globular, soluble maltose-binding protein and the membrane protein bacteriorhodopsin, which display moderate to weak coupling, respectively. The extracted mean square displacements also reveal a greater motional flexibility of IDP compared with globular, folded proteins and more restricted water motions on the IDP surface. The results provide evidence that protein and hydration-water motions mutually affect and shape each other, and that there is a gradient of coupling across different protein classes that may play a functional role in macromolecular activity in a cellular context. PMID:22828339

  8. Retinal counterion switch in the photoactivation of the G protein-coupled receptor rhodopsin.

    PubMed

    Yan, Elsa C Y; Kazmi, Manija A; Ganim, Ziad; Hou, Jian-Min; Pan, Douhai; Chang, Belinda S W; Sakmar, Thomas P; Mathies, Richard A

    2003-08-01

    The biological function of Glu-181 in the photoactivation process of rhodopsin is explored through spectroscopic studies of site-specific mutants. Preresonance Raman vibrational spectra of the unphotolyzed E181Q mutant are nearly identical to spectra of the native pigment, supporting the view that Glu-181 is uncharged (protonated) in the dark state. The pH dependence of the absorption of the metarhodopsin I (Meta I)-like photoproduct of E181Q is investigated, revealing a dramatic shift of its Schiff base pKa compared with the native pigment. This result is most consistent with the assignment of Glu-181 as the primary counterion of the retinylidene protonated Schiff base in the Meta I state, implying that there is a counterion switch from Glu-113 in the dark state to Glu-181 in Meta I. We propose a model where the counterion switch occurs by transferring a proton from Glu-181 to Glu-113 through an H-bond network formed primarily with residues on extracellular loop II (EII). The resulting reorganization of EII is then coupled to movements of helix III through a conserved disulfide bond (Cys110-Cys187); this process may be a general element of G protein-coupled receptor activation.

  9. Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

    PubMed Central

    Poirot, Olivier; Timsit, Youri

    2016-01-01

    From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. PMID:27225526

  10. A neural network dynamics that resembles protein evolution

    NASA Astrophysics Data System (ADS)

    Ferrán, Edgardo A.; Ferrara, Pascual

    1992-06-01

    We use neutral networks to classify proteins according to their sequence similarities. A network composed by 7 × 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of cytochrome c proteins belonging to 76 different species. As a result of the training, the network self-organized the activation of its neurons into topologically ordered maps, wherein phylogenetically related sequences were positioned close to each other. The evolution of the topological map during learning, in a representative computational experiment, roughly resembles the way in which one species evolves into several others. For instance, sequences corresponding to vertebrates, initially grouped together into one neuron, were placed in a contiguous zone of the final neural map, with sequences of fishes, amphibia, reptiles, birds and mammals associated to different neurons. Some apparent wrong classifications are due to the fact that some proteins have a greater degree of sequence identity than the one expected by phylogenetics. In the final neural map, each synaptic vector may be considered as the pattern corresponding to the ancestor of all the proteins that are attached to that neuron. Although it may be also tempting to link real time with learning epochs and to use this relationship to calibrate the molecular evolutionary clock, this is not correct because the evolutionary time schedule obtained with the neural network depends highly on the discrete way in which the winner neighborhood is decreased during learning.

  11. Spliced X-box Binding Protein 1 Couples the Unfolded Protein Response to Hexosamine Biosynthetic Pathway

    PubMed Central

    Wang, Zhao V.; Deng, Yingfeng; Gao, Ningguo; Pedrozo, Zully; Li, Dan L.; Morales, Cyndi R.; Criollo, Alfredo; Luo, Xiang; Tan, Wei; Jiang, Nan; Lehrman, Mark A.; Rothermel, Beverly A.; Lee, Ann-Hwee; Lavandero, Sergio; Mammen, Pradeep P.A.; Ferdous, Anwarul; Gillette, Thomas G.; Scherer, Philipp E.; Hill, Joseph A.

    2014-01-01

    SUMMARY The hexosamine biosynthetic pathway (HBP) generates UDP-GlcNAc (uridine diphosphate N-acetylglucosamine) for glycan synthesis and O-linked GlcNAc (O-GlcNAc) protein modifications. Despite the established role of the HBP in metabolism and multiple diseases, regulation of the HBP remains largely undefined. Here, we show that spliced X-box binding protein 1 (Xbp1s), the most conserved signal transducer of the unfolded protein response (UPR), is a direct transcriptional activator of the HBP. We demonstrate that the UPR triggers HBP activation via Xbp1s-dependent transcription of genes coding for key, rate-limiting enzymes. We further establish that this previously unrecognized UPR-HBP axis is triggered in a variety of stress conditions. Finally, we demonstrate a physiologic role for the UPR-HBP axis, by showing that acute stimulation of Xbp1s in heart by ischemia/reperfusion confers robust cardioprotection in part through induction of the HBP. Collectively, these studies reveal that Xbp1s couples the UPR to the HBP to protect cells under stress. PMID:24630721

  12. Spliced X-box binding protein 1 couples the unfolded protein response to hexosamine biosynthetic pathway.

    PubMed

    Wang, Zhao V; Deng, Yingfeng; Gao, Ningguo; Pedrozo, Zully; Li, Dan L; Morales, Cyndi R; Criollo, Alfredo; Luo, Xiang; Tan, Wei; Jiang, Nan; Lehrman, Mark A; Rothermel, Beverly A; Lee, Ann-Hwee; Lavandero, Sergio; Mammen, Pradeep P A; Ferdous, Anwarul; Gillette, Thomas G; Scherer, Philipp E; Hill, Joseph A

    2014-03-13

    The hexosamine biosynthetic pathway (HBP) generates uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) for glycan synthesis and O-linked GlcNAc (O-GlcNAc) protein modifications. Despite the established role of the HBP in metabolism and multiple diseases, regulation of the HBP remains largely undefined. Here, we show that spliced X-box binding protein 1 (Xbp1s), the most conserved signal transducer of the unfolded protein response (UPR), is a direct transcriptional activator of the HBP. We demonstrate that the UPR triggers HBP activation via Xbp1s-dependent transcription of genes coding for key, rate-limiting enzymes. We further establish that this previously unrecognized UPR-HBP axis is triggered in a variety of stress conditions. Finally, we demonstrate a physiologic role for the UPR-HBP axis by showing that acute stimulation of Xbp1s in heart by ischemia/reperfusion confers robust cardioprotection in part through induction of the HBP. Collectively, these studies reveal that Xbp1s couples the UPR to the HBP to protect cells under stress.

  13. Competing G protein-coupled receptor kinases balance G protein and β-arrestin signaling.

    PubMed

    Heitzler, Domitille; Durand, Guillaume; Gallay, Nathalie; Rizk, Aurélien; Ahn, Seungkirl; Kim, Jihee; Violin, Jonathan D; Dupuy, Laurence; Gauthier, Christophe; Piketty, Vincent; Crépieux, Pascale; Poupon, Anne; Clément, Frédérique; Fages, François; Lefkowitz, Robert J; Reiter, Eric

    2012-01-01

    Seven-transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through β-arrestins, whose recruitment to the activated receptor is regulated by G protein-coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal-regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT(1A)R) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)-based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well-established function in the desensitization of G-protein activation, GRK2 exerts a strong negative effect on β-arrestin-dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2-dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT(1A)R, and HEK293 cells expressing other 7TMRs.

  14. Spliced X-box binding protein 1 couples the unfolded protein response to hexosamine biosynthetic pathway.

    PubMed

    Wang, Zhao V; Deng, Yingfeng; Gao, Ningguo; Pedrozo, Zully; Li, Dan L; Morales, Cyndi R; Criollo, Alfredo; Luo, Xiang; Tan, Wei; Jiang, Nan; Lehrman, Mark A; Rothermel, Beverly A; Lee, Ann-Hwee; Lavandero, Sergio; Mammen, Pradeep P A; Ferdous, Anwarul; Gillette, Thomas G; Scherer, Philipp E; Hill, Joseph A

    2014-03-13

    The hexosamine biosynthetic pathway (HBP) generates uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) for glycan synthesis and O-linked GlcNAc (O-GlcNAc) protein modifications. Despite the established role of the HBP in metabolism and multiple diseases, regulation of the HBP remains largely undefined. Here, we show that spliced X-box binding protein 1 (Xbp1s), the most conserved signal transducer of the unfolded protein response (UPR), is a direct transcriptional activator of the HBP. We demonstrate that the UPR triggers HBP activation via Xbp1s-dependent transcription of genes coding for key, rate-limiting enzymes. We further establish that this previously unrecognized UPR-HBP axis is triggered in a variety of stress conditions. Finally, we demonstrate a physiologic role for the UPR-HBP axis by showing that acute stimulation of Xbp1s in heart by ischemia/reperfusion confers robust cardioprotection in part through induction of the HBP. Collectively, these studies reveal that Xbp1s couples the UPR to the HBP to protect cells under stress. PMID:24630721

  15. Effects of positively charged redox molecules on disulfide-coupled protein folding.

    PubMed

    Okumura, Masaki; Shimamoto, Shigeru; Nakanishi, Takeyoshi; Yoshida, Yu-ichiro; Konogami, Tadafumi; Maeda, Shogo; Hidaka, Yuji

    2012-11-01

    In vitro folding of disulfide-containing proteins is generally regulated by redox molecules, such as glutathione. However, the role of the cross-disulfide-linked species formed between the redox molecule and the protein as a folding intermediate in the folding mechanism is poorly understood. In the present study, we investigated the effect of the charge on a redox molecule on disulfide-coupled protein folding. Several types of aliphatic thiol compounds including glutathione were examined for the folding of disulfide-containing-proteins, such as lysozyme and prouroguanylin. The results indicate that the positive charge and its dispersion play a critical role in accelerating disulfide-coupled protein folding.

  16. Protein identification by spectral networks analysis.

    PubMed

    Bandeira, Nuno; Tsur, Dekel; Frank, Ari; Pevzner, Pavel A

    2007-04-10

    Advances in tandem mass spectrometry (MS/MS) steadily increase the rate of generation of MS/MS spectra. As a result, the existing approaches that compare spectra against databases are already facing a bottleneck, particularly when interpreting spectra of modified peptides. Here we explore a concept that allows one to perform an MS/MS database search without ever comparing a spectrum against a database. We propose to take advantage of spectral pairs, which are pairs of spectra obtained from overlapping (often nontryptic) peptides or from unmodified and modified versions of the same peptide. Having a spectrum of a modified peptide paired with a spectrum of an unmodified peptide allows one to separate the prefix and suffix ladders, to greatly reduce the number of noise peaks, and to generate a small number of peptide reconstructions that are likely to contain the correct one. The MS/MS database search is thus reduced to extremely fast pattern-matching (rather than time-consuming matching of spectra against databases). In addition to speed, our approach provides a unique paradigm for identifying posttranslational modifications by means of spectral networks analysis. PMID:17404225

  17. Two generalized algorithms measuring phase-amplitude cross-frequency coupling in neuronal oscillations network.

    PubMed

    Li, Qun; Zheng, Chen-Guang; Cheng, Ning; Wang, Yi-Yi; Yin, Tao; Zhang, Tao

    2016-06-01

    An increasing number of studies pays attention to cross-frequency coupling in neuronal oscillations network, as it is considered to play an important role in exchanging and integrating of information. In this study, two generalized algorithms, phase-amplitude coupling-evolution map approach and phase-amplitude coupling-conditional mutual information which have been developed and applied originally in an identical rhythm, are generalized to measure cross-frequency coupling. The effectiveness of quantitatively distinguishing the changes of coupling strength from the measurement of phase-amplitude coupling (PAC) is demonstrated based on simulation data. The data suggest that the generalized algorithms are able to effectively evaluate the strength of PAC, which are consistent with those traditional approaches, such as PAC-PLV and PAC-MI. Experimental data, which are local field potentials obtained from anaesthetized SD rats, have also been analyzed by these two generalized approaches. The data show that the theta-low gamma PAC in the hippocampal CA3-CA1 network is significantly decreased in the glioma group compared to that in the control group. The results, obtained from either simulation data or real experimental signals, are consistent with that of those traditional approaches PAC-MI and PAC-PLV. It may be considered as a proper indicator for the cross frequency coupling in sub-network, such as the hippocampal CA3 and CA1. PMID:27275379

  18. Two generalized algorithms measuring phase-amplitude cross-frequency coupling in neuronal oscillations network.

    PubMed

    Li, Qun; Zheng, Chen-Guang; Cheng, Ning; Wang, Yi-Yi; Yin, Tao; Zhang, Tao

    2016-06-01

    An increasing number of studies pays attention to cross-frequency coupling in neuronal oscillations network, as it is considered to play an important role in exchanging and integrating of information. In this study, two generalized algorithms, phase-amplitude coupling-evolution map approach and phase-amplitude coupling-conditional mutual information which have been developed and applied originally in an identical rhythm, are generalized to measure cross-frequency coupling. The effectiveness of quantitatively distinguishing the changes of coupling strength from the measurement of phase-amplitude coupling (PAC) is demonstrated based on simulation data. The data suggest that the generalized algorithms are able to effectively evaluate the strength of PAC, which are consistent with those traditional approaches, such as PAC-PLV and PAC-MI. Experimental data, which are local field potentials obtained from anaesthetized SD rats, have also been analyzed by these two generalized approaches. The data show that the theta-low gamma PAC in the hippocampal CA3-CA1 network is significantly decreased in the glioma group compared to that in the control group. The results, obtained from either simulation data or real experimental signals, are consistent with that of those traditional approaches PAC-MI and PAC-PLV. It may be considered as a proper indicator for the cross frequency coupling in sub-network, such as the hippocampal CA3 and CA1.

  19. Cascade of failures in coupled network systems with multiple support-dependence relations

    NASA Astrophysics Data System (ADS)

    Shao, Jia; Buldyrev, Sergey V.; Havlin, Shlomo; Stanley, H. Eugene

    2011-03-01

    We study, both analytically and numerically, the cascade of failures in two coupled network systems A and B, where multiple support-dependence relations are randomly built between nodes of networks A and B. In our model we assume that each node in one network can function only if it has at least a single support link connecting it to a functional node in the other network. We assume that networks A and B have (i) sizes NA and NB, (ii) degree distributions of connectivity links PA(k) and PB(k), (iii) degree distributions of support links P˜A(k) and P˜B(k), and (iv) random attack removes (1-RA)NA and (1-RB)NB nodes form the networks A and B, respectively. We find the fractions of nodes μ∞A and μ∞B which remain functional (giant component) at the end of the cascade process in networks A and B in terms of the generating functions of the degree distributions of their connectivity and support links. In a special case of Erdős-Rényi networks with average degrees a and b in networks A and B, respectively, and Poisson distributions of support links with average degrees a˜ and b˜ in networks A and B, respectively, μ∞A=RA[1-exp(-ãμ∞B)][1-exp(-aμ∞A)] and μ∞B=RB[1-exp(-b˜μ∞A)][1-exp(-bμ∞B)]. In the limit of a˜→∞ and b˜→∞, both networks become independent, and our model becomes equivalent to a random attack on a single Erdős-Rényi network. We also test our theory on two coupled scale-free networks, and find good agreement with the simulations.

  20. Protein Network of the Pseudomonas aeruginosa Denitrification Apparatus

    PubMed Central

    Borrero-de Acuña, José Manuel; Rohde, Manfred; Wissing, Josef; Jänsch, Lothar; Schobert, Max; Molinari, Gabriella; Timmis, Kenneth N.

    2016-01-01

    ABSTRACT Oxidative phosphorylation using multiple-component, membrane-associated protein complexes is the most effective way for a cell to generate energy. Here, we systematically investigated the multiple protein-protein interactions of the denitrification apparatus of the pathogenic bacterium Pseudomonas aeruginosa. During denitrification, nitrate (Nar), nitrite (Nir), nitric oxide (Nor), and nitrous oxide (Nos) reductases catalyze the reaction cascade of NO3− → NO2− → NO → N2O → N2. Genetic experiments suggested that the nitric oxide reductase NorBC and the regulatory protein NosR are the nucleus of the denitrification protein network. We utilized membrane interactomics in combination with electron microscopy colocalization studies to elucidate the corresponding protein-protein interactions. The integral membrane proteins NorC, NorB, and NosR form the core assembly platform that binds the nitrate reductase NarGHI and the periplasmic nitrite reductase NirS via its maturation factor NirF. The periplasmic nitrous oxide reductase NosZ is linked via NosR. The nitrate transporter NarK2, the nitrate regulatory system NarXL, various nitrite reductase maturation proteins, NirEJMNQ, and the Nos assembly lipoproteins NosFL were also found to be attached. A number of proteins associated with energy generation, including electron-donating dehydrogenases, the complete ATP synthase, almost all enzymes of the tricarboxylic acid (TCA) cycle, and the Sec system of protein transport, among many other proteins, were found to interact with the denitrification proteins. This deduced nitrate respirasome is presumably only one part of an extensive cytoplasmic membrane-anchored protein network connecting cytoplasmic, inner membrane, and periplasmic proteins to mediate key activities occurring at the barrier/interface between the cytoplasm and the external environment. IMPORTANCE The processes of cellular energy generation are catalyzed by large multiprotein enzyme complexes

  1. Network Analysis of Circular Permutations in Multidomain Proteins Reveals Functional Linkages for Uncharacterized Proteins

    PubMed Central

    Adjeroh, Donald; Jiang, Yue; Jiang, Bing-Hua; Lin, Jie

    2014-01-01

    Various studies have implicated different multidomain proteins in cancer. However, there has been little or no detailed study on the role of circular multidomain proteins in the general problem of cancer or on specific cancer types. This work represents an initial attempt at investigating the potential for predicting linkages between known cancer-associated proteins with uncharacterized or hypothetical multidomain proteins, based primarily on circular permutation (CP) relationships. First, we propose an efficient algorithm for rapid identification of both exact and approximate CPs in multidomain proteins. Using the circular relations identified, we construct networks between multidomain proteins, based on which we perform functional annotation of multidomain proteins. We then extend the method to construct subnetworks for selected cancer subtypes, and performed prediction of potential link-ages between uncharacterized multidomain proteins and the selected cancer types. We include practical results showing the performance of the proposed methods. PMID:25741177

  2. Tools for investigating functional interactions between ligands and G-protein-coupled receptors.

    PubMed

    Lerner, M R

    1994-04-01

    A general assay for evaluating functional interactions between ligands and G-protein-coupled receptors within minutes has been developed. The system uses the principles employed by animals such as reptiles, amphibians and fish to control their colors. In nature, activation of G-protein-coupled receptors expressed by skin cells called chromatophores effects pigment redistribution within the cells to change an animal's coloration. The in vitro 'chameleon in a dish' equivalent can use essentially any cloned G-protein-coupled receptor. PMID:7517590

  3. GPCR-ModSim: A comprehensive web based solution for modeling G-protein coupled receptors

    PubMed Central

    Esguerra, Mauricio; Siretskiy, Alexey; Bello, Xabier; Sallander, Jessica; Gutiérrez-de-Terán, Hugo

    2016-01-01

    GPCR-ModSim (http://open.gpcr-modsim.org) is a centralized and easy to use service dedicated to the structural modeling of G-protein Coupled Receptors (GPCRs). 3D molecular models can be generated from amino acid sequence by homology-modeling techniques, considering different receptor conformations. GPCR-ModSim includes a membrane insertion and molecular dynamics (MD) equilibration protocol, which can be used to refine the generated model or any GPCR structure uploaded to the server, including if desired non-protein elements such as orthosteric or allosteric ligands, structural waters or ions. We herein revise the main characteristics of GPCR-ModSim and present new functionalities. The templates used for homology modeling have been updated considering the latest structural data, with separate profile structural alignments built for inactive, partially-active and active groups of templates. We have also added the possibility to perform multiple-template homology modeling in a unique and flexible way. Finally, our new MD protocol considers a series of distance restraints derived from a recently identified conserved network of helical contacts, allowing for a smoother refinement of the generated models which is particularly advised when there is low homology to the available templates. GPCR- ModSim has been tested on the GPCR Dock 2013 competition with satisfactory results. PMID:27166369

  4. Predicting G-protein-coupled receptors families using different physiochemical properties and pseudo amino acid composition.

    PubMed

    Rehman, Zia-Ur; Mirza, Muhammad Tayyeb; Khan, Asifullah; Xhaard, Henri

    2013-01-01

    G-protein-coupled receptors (GPCRs) initiate signaling pathways via trimetric guanine nucleotide-binding proteins. GPCRs are classified based on their ligand-binding properties and molecular phylogenetic analyses. Nonetheless, these later analyses are in most case dependent on multiple sequence alignments, themselves dependent on human intervention and expertise. Alignment-free classifications of GPCR sequences, in addition to being unbiased, present many applications uncovering hidden physicochemical parameters shared among specific groups of receptors, to being used in automated workflows for large-scale molecular modeling applications. Current alignment-free classification methods, however, do not reach a full accuracy. This chapter discusses how GPCRs amino acid sequences can be classified using pseudo amino acid composition and multiscale energy representation of different physiochemical properties of amino acids. A hybrid feature extraction strategy is shown to be suitable to represent GPCRs and to be able to exploit GPCR amino acid sequence discrimination capability in spatial as well as transform domain. Classification strategies such as support vector machine and probabilistic neural network are then discussed in regards to GPCRs classification. The work of GPCR-Hybrid web predictor is also discussed.

  5. Graph spectral analysis of protein interaction network evolution.

    PubMed

    Thorne, Thomas; Stumpf, Michael P H

    2012-10-01

    We present an analysis of protein interaction network data via the comparison of models of network evolution to the observed data. We take a bayesian approach and perform posterior density estimation using an approximate bayesian computation with sequential Monte Carlo method. Our approach allows us to perform model selection over a selection of potential network growth models. The methodology we apply uses a distance defined in terms of graph spectra which captures the network data more naturally than previously used summary statistics such as the degree distribution. Furthermore, we include the effects of sampling into the analysis, to properly correct for the incompleteness of existing datasets, and have analysed the performance of our method under various degrees of sampling. We consider a number of models focusing not only on the biologically relevant class of duplication models, but also including models of scale-free network growth that have previously been claimed to describe such data. We find a preference for a duplication-divergence with linear preferential attachment model in the majority of the interaction datasets considered. We also illustrate how our method can be used to perform multi-model inference of network parameters to estimate properties of the full network from sampled data.

  6. Adaptive synchronization in delay-coupled networks of Stuart-Landau oscillators.

    PubMed

    Selivanov, Anton A; Lehnert, Judith; Dahms, Thomas; Hövel, Philipp; Fradkov, Alexander L; Schöll, Eckehard

    2012-01-01

    We consider networks of delay-coupled Stuart-Landau oscillators. In these systems, the coupling phase has been found to be a crucial control parameter. By proper choice of this parameter one can switch between different synchronous oscillatory states of the network. Applying the speed-gradient method, we derive an adaptive algorithm for an automatic adjustment of the coupling phase such that a desired state can be selected from an otherwise multistable regime. We propose goal functions based on both the difference of the oscillators and a generalized order parameter and demonstrate that the speed-gradient method allows one to find appropriate coupling phases with which different states of synchronization, e.g., in-phase oscillation, splay, or various cluster states, can be selected.

  7. The role of axonal delay in the synchronization of networks of coupled cortical oscillators.

    PubMed

    Crook, S M; Ermentrout, G B; Vanier, M C; Bower, J M

    1997-04-01

    Coupled oscillator models use a single phase variable to approximate the voltage oscillation of each neuron during repetitive firing where the behavior of the model depends on the connectivity and the interaction function chosen to describe the coupling. We introduce a network model consisting of a continuum of these oscillators that includes the effects of spatially decaying coupling and axonal delay. We derive equations for determining the stability of solutions and analyze the network behavior for two different interaction functions. The first is a sine function, and the second is derived from a compartmental model of a pyramidal cell. In both cases, the system of coupled neural oscillators can undergo a bifurcation from synchronous oscillations to waves. The change in qualitative behavior is due to the axonal delay, which causes distant connections to encourage a phase shift between cells. We suggest that this mechanism could contribute to the behavior observed in several neurobiological systems.

  8. Self-similarity of human protein interaction networks: a novel strategy of distinguishing proteins

    PubMed Central

    Fadhal, Emad; Gamieldien, Junaid; Mwambene, Eric C.

    2015-01-01

    The successful determination of reliable protein interaction networks (PINs) in several species in the post-genomic era has hitherto facilitated the quest to understanding systems and structural properties of such networks. It is envisaged that a clearer understanding of their intrinsic topological properties would elucidate evolutionary and biological topography of organisms. This, in turn, may inform the understanding of diseases' aetiology. By analysing sub-networks that are induced in various layers identified by zones defined as distance from central proteins, we show that zones of human PINs display self-similarity patterns. What is observed at a global level is repeated at lower levels of inducement. Furthermore, it is observed that these levels of strength point to refinement and specialisations in these layers. This may point to the fact that various levels of representations in the self-similarity phenomenon offer a way of measuring and distinguishing the importance of proteins in the network. To consolidate our findings, we have also considered a gene co-expression network and a class of gene regulatory networks in the same framework. In all cases, the phenomenon is significantly evident. In particular, the truly unbiased regulatory networks show finer level of articulation of self-similarity. PMID:25720740

  9. Experimental observation of chimera and cluster states in a minimal globally coupled network

    NASA Astrophysics Data System (ADS)

    Hart, Joseph D.; Bansal, Kanika; Murphy, Thomas E.; Roy, Rajarshi

    2016-09-01

    A "chimera state" is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

  10. Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators.

    PubMed

    Nakano, H; Saito, T

    2004-09-01

    This paper studies a pulse-coupled network consisting of simple chaotic spiking oscillators (CSOs). If a unit oscillator and its neighbor(s) have (almost) the same parameter values, they exhibit in-phase synchronization of chaos. As the parameter values differ, they exhibit asynchronous phenomena. Based on such behavior, some synchronous groups appear partially in the network. Typical phenomena are verified in the laboratory via a simple test circuit. These phenomena can be evaluated numerically by using an effective mapping procedure. We then apply the proposed network to image segmentation. Using a lattice pulse-coupled network via grouping synchronous phenomena, the input image data can be segmented into some sub-regions.

  11. Spike phase synchronization in delayed-coupled neural networks: Uniform vs. non-uniform transmission delay

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2013-03-01

    In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as individual dynamical systems and coupled through a number of network structures such as scale-free, Erdős-Rényi, and modular. The individual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There were specific values of delay where the phase synchronization reached to its peaks. The behavior of the phase synchronization in the networks with non-uniform delays was different with the former case, where the phase synchrony decreased as distributed delays introduced to the networks.

  12. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    PubMed

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.

  13. Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

    PubMed Central

    Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D.; Ravi, Sathya N.; Bendlin, Barbara B.; Johnson, Sterling C.; Singh, Vikas

    2016-01-01

    There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function. To do so, one typically performs so-called tractography procedures on diffusion MR brain images and derives measures of brain connectivity expressed as graphs. The nodes correspond to distinct brain regions and the edges encode the strength of the connection. The scientific interest is in characterizing the evolution of these graphs over time or from healthy individuals to diseased. We pose this important question in terms of the Laplacian of the connectivity graphs derived from various longitudinal or disease time points — quantifying its progression is then expressed in terms of coupling the harmonic bases of a full set of Laplacians. We derive a coupled system of generalized eigenvalue problems (and corresponding numerical optimization schemes) whose solution helps characterize the full life cycle of brain connectivity evolution in a given dataset. Finally, we show a set of results on a diffusion MR imaging dataset of middle aged people at risk for Alzheimer’s disease (AD), who are cognitively healthy. In such asymptomatic adults, we find that a framework for characterizing brain connectivity evolution provides the ability to predict cognitive scores for individual subjects, and for estimating the progression of participant’s brain connectivity into the future. PMID:27812274

  14. An activation switch in the rhodopsin family of G protein-coupled receptors: the thyrotropin receptor.

    PubMed

    Urizar, Eneko; Claeysen, Sylvie; Deupí, Xavier; Govaerts, Cedric; Costagliola, Sabine; Vassart, Gilbert; Pardo, Leonardo

    2005-04-29

    We aimed at understanding molecular events involved in the activation of a member of the G protein-coupled receptor family, the thyrotropin receptor. We have focused on the transmembrane region and in particular on a network of polar interactions between highly conserved residues. Using molecular dynamics simulations and site-directed mutagenesis techniques we have identified residue Asn-7.49, of the NPxxY motif of TM 7, as a molecular switch in the mechanism of thyrotropin receptor (TSHr) activation. Asn-7.49 appears to adopt two different conformations in the inactive and active states. These two states are characterized by specific interactions between this Asn and polar residues in the transmembrane domain. The inactive gauche+ conformation is maintained by interactions with residues Thr-6.43 and Asp-6.44. Mutation of these residues into Ala increases the constitutive activity of the receptor by factors of approximately 14 and approximately 10 relative to wild type TSHr, respectively. Upon receptor activation Asn-7.49 adopts the trans conformation to interact with Asp-2.50 and a putatively charged residue that remains to be identified. In addition, the conserved Leu-2.46 of the (N/S)LxxxD motif also plays a significant role in restraining the receptor in the inactive state because the L2.46A mutation increases constitutive activity by a factor of approximately 13 relative to wild type TSHr. As residues Leu-2.46, Asp-2.50, and Asn-7.49 are strongly conserved, this molecular mechanism of TSHr activation can be extended to other members of the rhodopsin-like family of G protein-coupled receptors.

  15. Calculation of correlation function of a spatially coupled spiking neural network

    NASA Astrophysics Data System (ADS)

    Qiu, Siwei; Chow, Carson

    The dynamics of a large but finite number of coupled spiking neurons is not well understood. We analyze finite size effects in a network of synaptically coupled theta neurons. We show how the system can be characterized by a functional integral from which finite size effects are calculated perturbatively. We discuss the implications of this technique for bump attractors. Thanks to support of the Intramural Research Program of the NIH, NIDDK.

  16. A physical interaction network of dengue virus and human proteins.

    PubMed

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  17. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

    PubMed

    Li, Wan; Chen, Lina; He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia

    2013-01-01

    The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

  18. Role of β-arrestins and arrestin domain-containing proteins in G protein-coupled receptor trafficking.

    PubMed

    Kang, Dong Soo; Tian, Xufan; Benovic, Jeffrey L

    2014-04-01

    The arrestin clan can now be broadly divided into three structurally similar subgroups: the originally identified arrestins (visual and β-arrestins), the α-arrestins and a group of Vps26-related proteins. The visual and β-arrestins selectively bind to agonist-occupied phosphorylated G protein-coupled receptors (GPCRs) and inhibit GPCR coupling to heterotrimeric G proteins while the β-arrestins also function as adaptor proteins to regulate GPCR trafficking and G protein-independent signaling. The α-arrestins have also recently been implicated in regulating GPCR trafficking while Vps26 regulates retrograde trafficking. In this review, we provide an overview of the α-arrestins and β-arrestins with a focus on our current understanding of how these adaptor proteins regulate GPCR trafficking.

  19. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

    PubMed

    Keith, Benjamin P; Robertson, David L; Hentges, Kathryn E

    2014-01-01

    Mutations in genes potentially lead to a number of genetic diseases with differing severity. These disease genes have been the focus of research in recent years showing that the disease gene population as a whole is not homogeneous, and can be categorized according to their interactions. Locus heterogeneity describes a single disorder caused by mutations in different genes each acting individually to cause the same disease. Using datasets of experimentally derived human disease genes and protein interactions, we created a protein interaction network to investigate the relationships between the products of genes associated with a disease displaying locus heterogeneity, and use network parameters to suggest properties that distinguish these disease genes from the overall disease gene population. Through the manual curation of known causative genes of 100 diseases displaying locus heterogeneity and 397 single-gene Mendelian disorders, we use network parameters to show that our locus heterogeneity network displays distinct properties from the global disease network and a Mendelian network. Using the global human proteome, through random simulation of the network we show that heterogeneous genes display significant interconnectivity. Further topological analysis of this network revealed clustering of locus heterogeneity genes that cause identical disorders, indicating that these disease genes are involved in similar biological processes. We then use this information to suggest additional genes that may contribute to diseases with locus heterogeneity.

  20. Structural biology. Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor.

    PubMed

    Burg, John S; Ingram, Jessica R; Venkatakrishnan, A J; Jude, Kevin M; Dukkipati, Abhiram; Feinberg, Evan N; Angelini, Alessandro; Waghray, Deepa; Dror, Ron O; Ploegh, Hidde L; Garcia, K Christopher

    2015-03-01

    Chemokines are small proteins that function as immune modulators through activation of chemokine G protein-coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state-like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor's inactive state. PMID:25745166

  1. G protein coupled receptors as targets for next generation pesticides.

    PubMed

    Audsley, Neil; Down, Rachel E

    2015-12-01

    There is an on-going need for the discovery and development of new pesticides due to the loss of existing products through the continuing development of resistance, the desire for products with more favourable environmental and toxicological profiles and the need to implement the principles of integrated pest management. Insect G protein coupled receptors (GPCRs) have important roles in modulating biology, physiology and behaviour, including reproduction, osmoregulation, growth and development. Modifying normal receptor function by blocking or over stimulating its actions may either result in the death of a pest or disrupt its normal fitness or reproductive capacity to reduce pest populations. Hence GPCRs offer potential targets for the development of next generation pesticides providing opportunities to discover new chemistries for invertebrate pest control. Such receptors are important targets for pharmaceutical drugs, but are under-exploited by the agro-chemical industry. The octopamine receptor agonists are the only pesticides with a recognized mode of action, as described in the classification scheme developed by the Insecticide Resistance Action Committee, that act via a GPCR. The availability of sequenced insect genomes has facilitated the characterization of insect GPCRs, but the development and utilization of screening assays to identify lead compounds has been slow. Various studies using knock-down technologies or applying the native ligands and/or neuropeptide analogues to pest insects in vivo, have however demonstrated that modifying normal receptor function can have an insecticidal effect. This review presents examples of potential insect neuropeptide receptors that are potential targets for lead compound development, using case studies from three representative pest species, Tribolium castaneum, Acyrthosiphon pisum, and Drosophila suzukii. Functional analysis studies on T. castaneum suggest that GPCRs involved in growth and development (eclosion

  2. Analysis and identification of toxin targets by topological properties in protein-protein interaction network.

    PubMed

    Yang, Lei; Wang, Jizhe; Wang, Huiping; Lv, Yingli; Zuo, Yongchun; Jiang, Wei

    2014-05-21

    Proteins do not exert their function in isolation of one another, but interact together in protein-protein interaction (PPI) networks. Analysis of topological properties of proteins in the PPI network is very helpful to understand the function of proteins. However, until recently, no one has ever undertaken to investigate toxin targets by topological properties. In this study, for the first time, 12 topological properties are used to investigate the characteristics of toxin targets in the PPI network. Most of the topological properties are found to be statistically discriminative between toxin targets and other proteins, and toxin targets tend to play more important roles in the PPI network. In addition, based on the topological properties and the sequence information, support vector machine (SVM) is used to predict toxin targets. The results obtained by the jackknife test and 10-fold cross validation are encouraging, indicating that SVM is a useful tool for predicting toxin targets, or at least can play complementary roles in relevant areas.

  3. Differential protein network analysis of the immune cell lineage.

    PubMed

    Clancy, Trevor; Hovig, Eivind

    2014-01-01

    Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks. PMID:25309909

  4. Coupling protein complex analysis to peptide based proteomics.

    PubMed

    Gao, Qiang; Madian, Ashraf G; Liu, Xiuping; Adamec, Jiri; Regnier, Fred E

    2010-12-01

    Proteolysis is a central component of most proteomics methods. Unfortunately much of the information relating to the structural diversity of proteins is lost during digestion. This paper describes a method in which the native proteome of yeast was subjected to preliminary fractionation by size exclusion chromatography (SEC) prior to trypsin digestion of SEC fractions and reversed phase chromatography-mass spectral analysis to identify tryptic peptides thus generated. Through this approach proteins associated with other proteins in high molecular mass complexes were recognized and identified. A focus of this work was on the identification of Hub proteins that associate with multiple interaction partners. A critical component of this strategy is to choose methods and conditions that maximize retention of native structure during the various stages of analysis prior to proteolysis, especially during cell lysis. Maximum survival of protein complexes during lysis was obtained with the French press and bead-beater methods of cell disruption at approximately pH 8 with 200 mM NaCl in the lysis buffer. Structure retention was favored by higher ionic strength, suggesting that hydrophobic effects are important in maintaining the structure of protein complexes. Recovery of protein complexes declined substantially with storage at any temperature, but storage at -20°C was best when low temperature storage was necessary. Slightly lower recovery was obtained with storage at -80°C while lowest recovery was achieved at 4°C. It was concluded that initial fractionation of native proteins in cell lysates by SEC prior to RPC-MS/MS of tryptic digests can be used to recognize and identify proteins in complexes along with their interaction partners in known protein complexes.

  5. Dynamic coupling of complex brain networks and dual-task behavior.

    PubMed

    Alavash, Mohsen; Thiel, Christiane M; Gießing, Carsten

    2016-04-01

    Multi-tasking is a familiar situation where behavioral performance is often challenged. To date, fMRI studies investigating the neural underpinning of dual-task interference have mostly relied on local brain activation maps or static brain connectivity networks. Here, based on task fMRI we explored how fluctuations in behavior during concurrent performance of a visuospatial and a speech task relate to alternations in the topology of dynamic brain connectivity networks. We combined a time-resolved functional connectivity and complex network analysis with a sliding window approach applied to the trial by trial behavioral responses to investigate the coupling between dynamic brain networks and dual-task behavior at close temporal proximity. Participants showed fluctuations in their dual-task behavior over time, with the accuracy in the component tasks being statistically independent from one another. On the global level of brain networks we found that dynamic changes of network topology were differentially coupled with the behavior in each component task during the course of dual-tasking. While momentary decrease in the global efficiency of dynamic brain networks correlated with subsequent increase in visuospatial accuracy, better speech performance was preceded by higher global network efficiency and was followed by an increase in between-module connectivity over time. Additionally, dynamic alternations in the modular organization of brain networks at the posterior cingulate cortex were differentially predictive for the visuospatial as compared to the speech accuracy over time. Our results provide the first evidence that, during the course of dual-tasking, each component task is supported by a distinct topological configuration of brain connectivity networks. This finding suggests that the failure of functional brain connectivity networks to adapt to an optimal topology supporting the performance in both component tasks at the same time contributes to the moment to

  6. Modeling synchronization in networks of delay-coupled fiber ring lasers.

    PubMed

    Lindley, Brandon S; Schwartz, Ira B

    2011-11-21

    We study the onset of synchronization in a network of N delay-coupled stochastic fiber ring lasers with respect to various parameters when the coupling power is weak. In particular, for groups of three or more ring lasers mutually coupled to a central hub laser, we demonstrate a robust tendency toward out-of-phase (achronal) synchronization between the N-1 outer lasers and the single inner laser. In contrast to the achronal synchronization, we find the outer lasers synchronize with zero-lag (isochronal) with respect to each other, thus forming a set of N-1 coherent fiber lasers.

  7. Chromatin topology is coupled to Polycomb group protein subnuclear organization

    PubMed Central

    Wani, Ajazul H.; Boettiger, Alistair N.; Schorderet, Patrick; Ergun, Ayla; Münger, Christine; Sadreyev, Ruslan I.; Zhuang, Xiaowei; Kingston, Robert E.; Francis, Nicole J.

    2016-01-01

    The genomes of metazoa are organized at multiple scales. Many proteins that regulate genome architecture, including Polycomb group (PcG) proteins, form subnuclear structures. Deciphering mechanistic links between protein organization and chromatin architecture requires precise description and mechanistic perturbations of both. Using super-resolution microscopy, here we show that PcG proteins are organized into hundreds of nanoscale protein clusters. We manipulated PcG clusters by disrupting the polymerization activity of the sterile alpha motif (SAM) of the PcG protein Polyhomeotic (Ph) or by increasing Ph levels. Ph with mutant SAM disrupts clustering of endogenous PcG complexes and chromatin interactions while elevating Ph level increases cluster number and chromatin interactions. These effects can be captured by molecular simulations based on a previously described chromatin polymer model. Both perturbations also alter gene expression. Organization of PcG proteins into small, abundant clusters on chromatin through Ph SAM polymerization activity may shape genome architecture through chromatin interactions. PMID:26759081

  8. Robust autoassociative memory with coupled networks of Kuramoto-type oscillators

    NASA Astrophysics Data System (ADS)

    Heger, Daniel; Krischer, Katharina

    2016-08-01

    Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction.

  9. Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization.

    PubMed

    Che, Yanqiu; Li, Ruixue; Han, Chunxiao; Cui, Shigang; Wang, Jiang; Wei, Xile; Deng, Bin

    2013-03-01

    This paper presents an adaptive anticipatory synchronization based method for simultaneous identification of topology and parameters of uncertain nonlinearly coupled complex dynamical networks with time delays. An adaptive controller is proposed, based on Lyapunov stability theorem and Barbǎlat's Lemma, to guarantee the stability of the anticipatory synchronization manifold between drive and response networks. Meanwhile, not only the identification criteria of network topology and system parameters are obtained but also the anticipatory time is identified. Numerical simulation results illustrate the effectiveness of the proposed method. PMID:23556964

  10. Robust autoassociative memory with coupled networks of Kuramoto-type oscillators.

    PubMed

    Heger, Daniel; Krischer, Katharina

    2016-08-01

    Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction. PMID:27627319

  11. Robust autoassociative memory with coupled networks of Kuramoto-type oscillators.

    PubMed

    Heger, Daniel; Krischer, Katharina

    2016-08-01

    Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction.

  12. Feedback control design for the complete synchronisation of two coupled Boolean networks

    NASA Astrophysics Data System (ADS)

    Li, Fangfei

    2016-09-01

    In the literatures, to design state feedback controllers to make the response Boolean network synchronise with the drive Boolean network is rarely considered. Motivated by this, feedback control design for the complete synchronisation of two coupled Boolean networks is investigated in this paper. A necessary condition for the existence of a state feedback controller achieving the complete synchronisation is established first. Then, based on the necessary condition, the feedback control law is proposed. Finally, an example is worked out to illustrate the proposed design procedure.

  13. Interplay between chaperones and protein disorder promotes the evolution of protein networks.

    PubMed

    Pechmann, Sebastian; Frydman, Judith

    2014-06-01

    Evolution is driven by mutations, which lead to new protein functions but come at a cost to protein stability. Non-conservative substitutions are of interest in this regard because they may most profoundly affect both function and stability. Accordingly, organisms must balance the benefit of accepting advantageous substitutions with the possible cost of deleterious effects on protein folding and stability. We here examine factors that systematically promote non-conservative mutations at the proteome level. Intrinsically disordered regions in proteins play pivotal roles in protein interactions, but many questions regarding their evolution remain unanswered. Similarly, whether and how molecular chaperones, which have been shown to buffer destabilizing mutations in individual proteins, generally provide robustness during proteome evolution remains unclear. To this end, we introduce an evolutionary parameter λ that directly estimates the rate of non-conservative substitutions. Our analysis of λ in Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens sequences reveals how co- and post-translationally acting chaperones differentially promote non-conservative substitutions in their substrates, likely through buffering of their destabilizing effects. We further find that λ serves well to quantify the evolution of intrinsically disordered proteins even though the unstructured, thus generally variable regions in proteins are often flanked by very conserved sequences. Crucially, we show that both intrinsically disordered proteins and highly re-wired proteins in protein interaction networks, which have evolved new interactions and functions, exhibit a higher λ at the expense of enhanced chaperone assistance. Our findings thus highlight an intricate interplay of molecular chaperones and protein disorder in the evolvability of protein networks. Our results illuminate the role of chaperones in enabling protein evolution, and underline the importance of the cellular

  14. Interplay between Chaperones and Protein Disorder Promotes the Evolution of Protein Networks

    PubMed Central

    Pechmann, Sebastian; Frydman, Judith

    2014-01-01

    Evolution is driven by mutations, which lead to new protein functions but come at a cost to protein stability. Non-conservative substitutions are of interest in this regard because they may most profoundly affect both function and stability. Accordingly, organisms must balance the benefit of accepting advantageous substitutions with the possible cost of deleterious effects on protein folding and stability. We here examine factors that systematically promote non-conservative mutations at the proteome level. Intrinsically disordered regions in proteins play pivotal roles in protein interactions, but many questions regarding their evolution remain unanswered. Similarly, whether and how molecular chaperones, which have been shown to buffer destabilizing mutations in individual proteins, generally provide robustness during proteome evolution remains unclear. To this end, we introduce an evolutionary parameter λ that directly estimates the rate of non-conservative substitutions. Our analysis of λ in Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens sequences reveals how co- and post-translationally acting chaperones differentially promote non-conservative substitutions in their substrates, likely through buffering of their destabilizing effects. We further find that λ serves well to quantify the evolution of intrinsically disordered proteins even though the unstructured, thus generally variable regions in proteins are often flanked by very conserved sequences. Crucially, we show that both intrinsically disordered proteins and highly re-wired proteins in protein interaction networks, which have evolved new interactions and functions, exhibit a higher λ at the expense of enhanced chaperone assistance. Our findings thus highlight an intricate interplay of molecular chaperones and protein disorder in the evolvability of protein networks. Our results illuminate the role of chaperones in enabling protein evolution, and underline the importance of the cellular

  15. High-throughput screening of antagonists for the orphan G-protein coupled receptor GPR139

    PubMed Central

    Wang, Jia; Zhu, Lin-yun; Liu, Qing; Hentzer, Morten; Smith, Garrick Paul; Wang, Ming-wei

    2015-01-01

    Aim: To discover antagonists of the orphan G-protein coupled receptor GPR139 through high-throughput screening of a collection of diverse small molecules. Methods: Calcium mobilization assays were used to identify initial hits and for subsequent confirmation studies. Results: Five small molecule antagonists, representing 4 different scaffolds, were identified following high-throughput screening of 16 000 synthetic compounds. Conclusion: The findings provide important tools for further study of this orphan G-protein coupled receptor. PMID:26027661

  16. The Role of Romantic Partners, Family and Peer Networks in Dating Couples' Views about Cohabitation.

    PubMed

    Manning, Wendy D; Cohen, Jessica A; Smock, Pamela J

    2011-01-01

    Emerging adults are increasingly cohabiting, but few studies have considered the role of social context in the formation of their views of cohabitation. Drawing on 40 semi-structured interviews with dating couples, we explored the role of romantic partners, family, and peers on evaluations of cohabitation. In couples where each member had a differing view about cohabitation, one romantic partner's desire to not cohabit trumped their partner's more ambivalent feelings about cohabitation. The influence of family in the formation of cohabitation views was evident through a variety of mechanisms, including parental advice, social modeling, religious values, and economic control. Peers also played a key role, with couples using the vicarious trials of their peer networks to judge how cohabitation would affect their own relationship. By using a couple perspective, assessing reports from both members of each couple, this study showcases how beliefs about cohabitation are formed within an intimate dyad. PMID:23087542

  17. The Role of Romantic Partners, Family and Peer Networks in Dating Couples' Views about Cohabitation.

    PubMed

    Manning, Wendy D; Cohen, Jessica A; Smock, Pamela J

    2011-01-01

    Emerging adults are increasingly cohabiting, but few studies have considered the role of social context in the formation of their views of cohabitation. Drawing on 40 semi-structured interviews with dating couples, we explored the role of romantic partners, family, and peers on evaluations of cohabitation. In couples where each member had a differing view about cohabitation, one romantic partner's desire to not cohabit trumped their partner's more ambivalent feelings about cohabitation. The influence of family in the formation of cohabitation views was evident through a variety of mechanisms, including parental advice, social modeling, religious values, and economic control. Peers also played a key role, with couples using the vicarious trials of their peer networks to judge how cohabitation would affect their own relationship. By using a couple perspective, assessing reports from both members of each couple, this study showcases how beliefs about cohabitation are formed within an intimate dyad.

  18. The energetic network of hotspot residues between Cdc25B phosphatase and its protein substrate

    PubMed Central

    Sohn, Jungsan; Rudolph, Johannes

    2006-01-01

    Summary We have investigated the functional network of hotspot residues at the remote docking site of two cell cycle regulators, namely Cdc25B phosphatase and its native protein substrate Cdk2-pTpY/CycA. Specifically, we have studied the roles of energetically important residues (Arg488, Arg492, Tyr497 on Cdc25B and Asp206 and Asp210 on Cdk2-pTpY/CycA) by generating a diverse set of substitutions and performing double- and triple mutant cycle analyses. This transient protein-protein interaction is particularly well-suited for this mutagenic approach because various control experiments ensure that the effect of each mutation is limited to the interaction of interest. We find binary coupling energies for ion pairs and hydrogen bonds ranging from 0.7 to 3.9 kcal/mol and ternary coupling energies of 1.9 and 2.8 kcal/mol. Overall our biochemical analyses are in good agreement with the docked structure of the complex and suggest the following roles for the individual hotspot residues on Cdc25B. The most important contributor, Arg492, forms a specific and tight bidentate interaction with Asp206 and a weaker interaction with Asp210 that cannot be replaced by a Lys. Although Tyr497 does not directly participate in this ionic network, it is important for buttressing Arg492 using both its hydrophobic (aromatic ring) and hydrophilic characteristics (hydrogen bonding). Arg488 participates less specifically in the electrostatic network with Asp206 and Asp210 of the protein substrate as it can partially be replaced by Lys. Our data provide insight how a cluster of residues in a docking site remote from the site of the chemical reaction can bring about efficient and specific substrate recognition. PMID:16950393

  19. Allosteric coupling from G protein to the agonist-binding pocket in GPCRs.

    PubMed

    DeVree, Brian T; Mahoney, Jacob P; Vélez-Ruiz, Gisselle A; Rasmussen, Soren G F; Kuszak, Adam J; Edwald, Elin; Fung, Juan-Jose; Manglik, Aashish; Masureel, Matthieu; Du, Yang; Matt, Rachel A; Pardon, Els; Steyaert, Jan; Kobilka, Brian K; Sunahara, Roger K

    2016-07-01

    G-protein-coupled receptors (GPCRs) remain the primary conduit by which cells detect environmental stimuli and communicate with each other. Upon activation by extracellular agonists, these seven-transmembrane-domain-containing receptors interact with heterotrimeric G proteins to regulate downstream second messenger and/or protein kinase cascades. Crystallographic evidence from a prototypic GPCR, the β2-adrenergic receptor (β2AR), in complex with its cognate G protein, Gs, has provided a model for how agonist binding promotes conformational changes that propagate through the GPCR and into the nucleotide-binding pocket of the G protein α-subunit to catalyse GDP release, the key step required for GTP binding and activation of G proteins. The structure also offers hints about how G-protein binding may, in turn, allosterically influence ligand binding. Here we provide functional evidence that G-protein coupling to the β2AR stabilizes a ‘closed’ receptor conformation characterized by restricted access to and egress from the hormone-binding site. Surprisingly, the effects of G protein on the hormone-binding site can be observed in the absence of a bound agonist, where G-protein coupling driven by basal receptor activity impedes the association of agonists, partial agonists, antagonists and inverse agonists. The ability of bound ligands to dissociate from the receptor is also hindered, providing a structural explanation for the G-protein-mediated enhancement of agonist affinity, which has been observed for many GPCR–G-protein pairs. Our data also indicate that, in contrast to agonist binding alone, coupling of a G protein in the absence of an agonist stabilizes large structural changes in a GPCR. The effects of nucleotide-free G protein on ligand-binding kinetics are shared by other members of the superfamily of GPCRs, suggesting that a common mechanism may underlie G-protein-mediated enhancement of agonist affinity. PMID:27362234

  20. Coupled cluster algorithms for networks of shared memory parallel processors

    NASA Astrophysics Data System (ADS)

    Bentz, Jonathan L.; Olson, Ryan M.; Gordon, Mark S.; Schmidt, Michael W.; Kendall, Ricky A.

    2007-05-01

    As the popularity of using SMP systems as the building blocks for high performance supercomputers increases, so too increases the need for applications that can utilize the multiple levels of parallelism available in clusters of SMPs. This paper presents a dual-layer distributed algorithm, using both shared-memory and distributed-memory techniques to parallelize a very important algorithm (often called the "gold standard") used in computational chemistry, the single and double excitation coupled cluster method with perturbative triples, i.e. CCSD(T). The algorithm is presented within the framework of the GAMESS [M.W. Schmidt, K.K. Baldridge, J.A. Boatz, S.T. Elbert, M.S. Gordon, J.J. Jensen, S. Koseki, N. Matsunaga, K.A. Nguyen, S. Su, T.L. Windus, M. Dupuis, J.A. Montgomery, General atomic and molecular electronic structure system, J. Comput. Chem. 14 (1993) 1347-1363]. (General Atomic and Molecular Electronic Structure System) program suite and the Distributed Data Interface [M.W. Schmidt, G.D. Fletcher, B.M. Bode, M.S. Gordon, The distributed data interface in GAMESS, Comput. Phys. Comm. 128 (2000) 190]. (DDI), however, the essential features of the algorithm (data distribution, load-balancing and communication overhead) can be applied to more general computational problems. Timing and performance data for our dual-level algorithm is presented on several large-scale clusters of SMPs.

  1. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.

    PubMed

    Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

    2014-12-01

    Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/ . PMID:25424913

  2. FunPred-1: protein function prediction from a protein interaction network using neighborhood analysis.

    PubMed

    Saha, Sovan; Chatterjee, Piyali; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita

    2014-12-01

    Proteins are responsible for all biological activities in living organisms. Thanks to genome sequencing projects, large amounts of DNA and protein sequence data are now available, but the biological functions of many proteins are still not annotated in most cases. The unknown function of such non-annotated proteins may be inferred or deduced from their neighbors in a protein interaction network. In this paper, we propose two new methods to predict protein functions based on network neighborhood properties. FunPred 1.1 uses a combination of three simple-yet-effective scoring techniques: the neighborhood ratio, the protein path connectivity and the relative functional similarity. FunPred 1.2 applies a heuristic approach using the edge clustering coefficient to reduce the search space by identifying densely connected neighborhood regions. The overall accuracy achieved in FunPred 1.2 over 8 functional groups involving hetero-interactions in 650 yeast proteins is around 87%, which is higher than the accuracy with FunPred 1.1. It is also higher than the accuracy of many of the state-of-the-art protein function prediction methods described in the literature. The test datasets and the complete source code of the developed software are now freely available at http://code.google.com/p/cmaterbioinfo/ .

  3. From local to global changes in proteins: a network view.

    PubMed

    Vuillon, Laurent; Lesieur, Claire

    2015-04-01

    To fulfill the biological activities in living organisms, proteins are endowed with dynamics, robustness and adaptability. The three properties co-exist because they allow global changes in structure to arise from local perturbations (dynamics). Robustness refers to the ability of the protein to incur such changes without suffering loss of function; adaptability is the emergence of a new biological activity. Since loss of function may jeopardize the survival of the organism and lead to disease, adaptability may occur through the combination of two local perturbations that together rescue the initial function. The review highlights the relevancy of computational network analysis to understand how a local change produces global changes. PMID:25791607

  4. Identifying the topology of a coupled FitzHugh-Nagumo neurobiological network via a pinning mechanism.

    PubMed

    Zhou, Jin; Yu, Wenwu; Li, Xiumin; Small, Michael; Lu, Jun-an

    2009-10-01

    Topology identification of a network has received great interest for the reason that the study on many key properties of a network assumes a special known topology. Different from recent similar works in which the evolution of all the nodes in a complex network need to be received, this brief presents a novel criterion to identify the topology of a coupled FitzHugh-Nagumo (FHN) neurobiological network by receiving the membrane potentials of only a fraction of the neurons. Meanwhile, although incomplete information is received, the evolution of all the neurons including membrane potentials and recovery variables are traced. Based on Schur complement and Lyapunov stability theory, the exact weight configuration matrix can be estimated by a simple adaptive feedback control. The effectiveness of the proposed approach is successfully verified by neural networks with fixed and switching topologies.

  5. EAMCD: an efficient algorithm based on minimum coupling distance for community identification in complex networks

    NASA Astrophysics Data System (ADS)

    Zhao, GuoDong; Wu, Yan; Ren, YuanFang; Zhu, Ming

    2013-01-01

    Community structure is an important feature in many real-world networks, which can help us understand structure and function in complex networks better. In recent years, there have been many algorithms proposed to detect community structure in complex networks. In this paper, we try to detect potential community beams whose link strengths are greater than surrounding links and propose the minimum coupling distance (MCD) between community beams. Based on MCD, we put forward an optimization heuristic algorithm (EAMCD) for modularity density function to welded these community beams into community frames which are seen as a core part of community. Using the principle of random walk, we regard the remaining nodes into the community frame to form a community. At last, we merge several small community frame fragments using local greedy strategy for the modularity density general function. Real-world and synthetic networks are used to demonstrate the effectiveness of our algorithm in detecting communities in complex networks.

  6. Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet.

    PubMed

    Yang, Q; Siganos, G; Faloutsos, M; Lonardi, S

    2006-01-01

    Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against man-made communication networks, and more specifically, the Internet. Our comparative study is based on a comprehensive set of graph metrics. Our results exhibit an interesting dichotomy. On the one hand, both networks share several macroscopic properties such as scale-free and small-world properties. On the other hand, the two networks exhibit significant topological differences, such as the cliqueishness of the highest degree nodes. We attribute these differences to the distinct design principles and constraints that both networks are assumed to satisfy. We speculate that the evolutionary constraints that favor the survivability and diversification are behind the building process of PPI networks, whereas the leading force in shaping the Internet topology is a decentralized optimization process geared towards efficient node communication.

  7. An inter-species protein-protein interaction network across vast evolutionary distance.

    PubMed

    Zhong, Quan; Pevzner, Samuel J; Hao, Tong; Wang, Yang; Mosca, Roberto; Menche, Jörg; Taipale, Mikko; Taşan, Murat; Fan, Changyu; Yang, Xinping; Haley, Patrick; Murray, Ryan R; Mer, Flora; Gebreab, Fana; Tam, Stanley; MacWilliams, Andrew; Dricot, Amélie; Reichert, Patrick; Santhanam, Balaji; Ghamsari, Lila; Calderwood, Michael A; Rolland, Thomas; Charloteaux, Benoit; Lindquist, Susan; Barabási, Albert-László; Hill, David E; Aloy, Patrick; Cusick, Michael E; Xia, Yu; Roth, Frederick P; Vidal, Marc

    2016-04-22

    In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

  8. Prediction and Annotation of Plant Protein Interaction Networks

    SciTech Connect

    McDermott, Jason E.; Wang, Jun; Yu, Jun; Wong, Gane Ka-Shu; Samudrala, Ram

    2009-02-01

    Large-scale experimental studies of interactions between components of biological systems have been performed for a variety of eukaryotic organisms. However, there is a dearth of such data for plants. Computational methods for prediction of relationships between proteins, primarily based on comparative genomics, provide a useful systems-level view of cellular functioning and can be used to extend information about other eukaryotes to plants. We have predicted networks for Arabidopsis thaliana, Oryza sativa indica and japonica and several plant pathogens using the Bioverse (http://bioverse.compbio.washington.edu) and show that they are similar to experimentally-derived interaction networks. Predicted interaction networks for plants can be used to provide novel functional annotations and predictions about plant phenotypes and aid in rational engineering of biosynthesis pathways.

  9. Neural network definitions of highly predictable protein secondary structure classes

    SciTech Connect

    Lapedes, A. |; Steeg, E.; Farber, R.

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  10. G Protein-Coupled Receptor Signaling in Stem Cells and Cancer

    PubMed Central

    Lynch, Jennifer R.; Wang, Jenny Yingzi

    2016-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of cell-surface signaling proteins that bind extracellular ligands and transduce signals into cells via heterotrimeric G proteins. GPCRs are highly tractable drug targets. Aberrant expression of GPCRs and G proteins has been observed in various cancers and their importance in cancer stem cells has begun to be appreciated. We have recently reported essential roles for G protein-coupled receptor 84 (GPR84) and G protein subunit Gαq in the maintenance of cancer stem cells in acute myeloid leukemia. This review will discuss how GPCRs and G proteins regulate stem cells with a focus on cancer stem cells, as well as their implications for the development of novel targeted cancer therapies. PMID:27187360

  11. Structural basis of GDP release and gating in G protein coupled Fe[superscript 2+] transport

    SciTech Connect

    Guilfoyle, Amy; Maher, Megan J.; Rapp, Mikaela; Clarke, Ronald; Harrop, Stephen; Jormakka, Mika

    2009-09-29

    G proteins are key molecular switches in the regulation of membrane protein function and signal transduction. The prokaryotic membrane protein FeoB is involved in G protein coupled Fe{sup 2+} transport, and is unique in that the G protein is directly tethered to the membrane domain. Here, we report the structure of the soluble domain of FeoB, including the G protein domain, and its assembly into an unexpected trimer. Comparisons between nucleotide free and liganded structures reveal the closed and open state of a central cytoplasmic pore, respectively. In addition, these data provide the first observation of a conformational switch in the nucleotide-binding G5 motif, defining the structural basis for GDP release. From these results, structural parallels are drawn to eukaryotic G protein coupled membrane processes.

  12. Network topology and Turing instabilities in small arrays of diffusively coupled reactors

    NASA Astrophysics Data System (ADS)

    Horsthemke, Werner; Lam, Kwan; Moore, Peter K.

    2004-08-01

    We study the effect of the network structure on the diffusion-induced instability to nonuniform steady states in arrays of diffusively coupled reactors. The kinetics is given by the Lengyel-Epstein model, and we derive the conditions for Turing instabilities in all arrays of two, three, and four reactors.

  13. Dissecting spatio-temporal protein networks driving human heart development and related disorders.

    PubMed

    Lage, Kasper; Møllgård, Kjeld; Greenway, Steven; Wakimoto, Hiroko; Gorham, Joshua M; Workman, Christopher T; Bendsen, Eske; Hansen, Niclas T; Rigina, Olga; Roque, Francisco S; Wiese, Cornelia; Christoffels, Vincent M; Roberts, Amy E; Smoot, Leslie B; Pu, William T; Donahoe, Patricia K; Tommerup, Niels; Brunak, Søren; Seidman, Christine E; Seidman, Jonathan G; Larsen, Lars A

    2010-06-22

    Aberrant organ development is associated with a wide spectrum of disorders, from schizophrenia to congenital heart disease, but systems-level insight into the underlying processes is very limited. Using heart morphogenesis as general model for dissecting the functional architecture of organ development, we combined detailed phenotype information from deleterious mutations in 255 genes with high-confidence experimental interactome data, and coupled the results to thorough experimental validation. Hereby, we made the first systematic analysis of spatio-temporal protein networks driving many stages of a developing organ identifying several novel signaling modules. Our results show that organ development relies on surprisingly few, extensively recycled, protein modules that integrate into complex higher-order networks. This design allows the formation of a complicated organ using simple building blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio-temporal protein networks that drive the formation of organs, which in the future may lay the foundation of novel approaches in treatments, diagnostics, and regenerative medicine. PMID:20571530

  14. A network model to correlate conformational change and the impedance spectrum of single proteins

    NASA Astrophysics Data System (ADS)

    Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino

    2008-02-01

    Integrated nanodevices based on proteins or biomolecules are attracting increasing interest in today's research. In fact, it has been shown that proteins such as azurin and bacteriorhodopsin manifest some electrical properties that are promising for the development of active components of molecular electronic devices. Here we focus on two relevant kinds of protein: bovine rhodopsin, prototype of G-protein-coupled-receptor (GPCR) proteins, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer's disease. Both these proteins exert their function starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different impedance spectra associated with the different configurations. The distinct types of conformational change of rhodopsin and AChE agree with their dissimilar electrical responses. In particular, for rhodopsin the model predicts variations of the impedance spectra up to about 30%, while for AChE the same variations are limited to about 10%, which supports the existence of a dynamical equilibrium between its native and complexed states.

  15. Energy transfer in nonlinear network models of proteins

    NASA Astrophysics Data System (ADS)

    Piazza, F.; Sanejouand, Y.-H.

    2009-12-01

    We investigate how nonlinearity and topological disorder affect the energy relaxation of local kicks in coarse-grained network models of proteins. We find that nonlinearity promotes long-range, coherent transfer of substantial energy to specific functional sites, while depressing transfer to generic locations. In some cases, transfer is mediated by the self-localization of discrete breathers at distant locations from the kick, acting as efficient energy-accumulating centers.

  16. Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

    PubMed Central

    Ollikainen, Noah; de Jong, René M.; Kortemme, Tanja

    2015-01-01

    Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution. PMID:26397464

  17. Altered intrinsic functional coupling between core neurocognitive networks in Parkinson's disease.

    PubMed

    Putcha, Deepti; Ross, Robert S; Cronin-Golomb, Alice; Janes, Amy C; Stern, Chantal E

    2015-01-01

    Parkinson's disease (PD) is largely attributed to disruptions in the nigrostriatal dopamine system. These neurodegenerative changes may also have a more global effect on intrinsic brain organization at the cortical level. Functional brain connectivity between neurocognitive systems related to cognitive processing is critical for effective neural communication, and is disrupted across neurological disorders. Three core neurocognitive networks have been established as playing a critical role in the pathophysiology of many neurological disorders: the default-mode network (DMN), the salience network (SN), and the central executive network (CEN). In healthy adults, DMN-CEN interactions are anti-correlated while SN-CEN interactions are strongly positively correlated even at rest, when individuals are not engaging in any task. These intrinsic between-network interactions at rest are necessary for efficient suppression of the DMN and activation of the CEN during a range of cognitive tasks. To identify whether these network interactions are disrupted in individuals with PD, we used resting state functional magnetic resonance imaging (rsfMRI) to compare between-network connectivity between 24 PD participants and 20 age-matched controls (MC). In comparison to the MC, individuals with PD showed significantly less SN-CEN coupling and greater DMN-CEN coupling during rest. Disease severity, an index of striatal dysfunction, was related to reduced functional coupling between the striatum and SN. These results demonstrate that individuals with PD have a dysfunctional pattern of interaction between core neurocognitive networks compared to what is found in healthy individuals, and that interaction between the SN and the striatum is even more profoundly disrupted in those with greater disease severity. PMID:25685711

  18. On correlated reaction sets and coupled reaction sets in metabolic networks.

    PubMed

    Marashi, Sayed-Amir; Hosseini, Zhaleh

    2015-08-01

    Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.

  19. Dyadic, Partner, and Social Network Influences on Intimate Partner Violence among Male-Male Couples

    PubMed Central

    Stephenson, Rob; Sato, Kimi N.; Finneran, Catherine

    2013-01-01

    Introduction: Despite a recent focus on intimate partner violence (IPV) among men who have sex with men (MSM), the male-male couple is largely absent from the IPV literature. Specifically, research on dyadic factors shaping IPV in male-male couples is lacking. Methods: We took a subsample of 403 gay/bisexual men with main partners from a 2011 survey of approximately 1,000 gay and bisexual men from Atlanta. Logistic regression models of recent (<12 month) experience and perpetration of physical and sexual IPV examined dyadic factors, including racial differences, age differences, and social network characteristics of couples as key covariates shaping the reporting of IPV. Results: Findings indicate that men were more likely to report perpetration of physical violence if they were a different race to their main partner, whereas main partner age was associated with decreased reporting of physical violence. Having social networks that contained more gay friends was associated with significant reductions in the reporting of IPV, whereas having social networks comprised of sex partners or closeted gay friends was associated with increased reporting of IPV victimization and perpetration. Conclusion: The results point to several unique factors shaping the reporting of IPV within male-male couples and highlight the need for intervention efforts and prevention programs that focus on male couples, a group largely absent from both research and prevention efforts. PMID:23930144

  20. Translationally coupled initiation of protein synthesis in Bacillus subtilis.

    PubMed Central

    Sprengel, R; Reiss, B; Schaller, H

    1985-01-01

    The neomycin phosphotransferase gene (neo) from Transposon Tn5 is active in Gram-negative bacteria but silent in B. subtilis since it lacks an appropriate ribosome binding site for Gram-positive bacteria. Neo translation could be reactivated by coupling its initiation to the translational termination of the highly expressed beta-lactamase gene (penP) from B. licheniformis. This initiation occurred at the authentic neo start codon. Its efficiency was independent of the nucleotide sequence 5 to the neo gene, but strongly affected by the distance between the termination and initiation codon. It was the highest if both codons overlapped in the sequence ATGA. In B. licheniformis, a translationally coupled neo gene was inducible expressed as the penP gene demonstrating the potential of the technique to monitor the activity of expression units for which no direct assays exists. Images PMID:3923434

  1. Protein Mediated Magnetic Coupling between Lactate and Water Protons

    NASA Astrophysics Data System (ADS)

    Swanson, Scott D.

    1998-11-01

    The magnetic coupling between methyl lactate protons and water protons in samples of cross-linked bovine serum albumin (BSA) is studied. Cross-relaxation spectroscopy shows efficient magnetization transfer from immobilized BSA to both water and methyl lactate protons. Transient and steady-state NOE experiments reveal a negative intermolecular NOE between methyl lactate and water protons. Lactate is indirectly detected by selectively saturating the methyl lactate protons and measuring the decrease in water proton magnetization. Indirect detection of methyl lactate protons is an order of magnitude more sensitive than direct detection in these model systems. Lactate was indirectly imaged, via the water proton resonance, with 1.1-μl voxels in 2 min. Immobilized BSA reduces the intermolecular correlation time between water and lactate protons into the spin-diffusion limit where the NOE is negative. Possible molecular mechanisms for this coupling and applications toin vivospectroscopy are discussed.

  2. Cascading failures in coupled networks with both inner-dependency and inter-dependency links

    PubMed Central

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-01-01

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well. PMID:27142883

  3. Spatiotemporal Dynamics of a Network of Coupled Time-Delay Digital Tanlock Loops

    NASA Astrophysics Data System (ADS)

    Paul, Bishwajit; Banerjee, Tanmoy; Sarkar, B. C.

    The time-delay digital tanlock loop (TDTLs) is an important class of phase-locked loop that is widely used in electronic communication systems. Although nonlinear dynamics of an isolated TDTL has been studied in the past but the collective behavior of TDTLs in a network is an important topic of research and deserves special attention as in practical communication systems separate entities are rarely isolated. In this paper, we carry out the detailed analysis and numerical simulations to explore the spatiotemporal dynamics of a network of a one-dimensional ring of coupled TDTLs with nearest neighbor coupling. The equation representing the network is derived and we carry out analytical calculations using the circulant matrix formalism to obtain the stability criteria. An extensive numerical simulation reveals that with the variation of gain parameter and coupling strength the network shows a variety of spatiotemporal dynamics such as frozen random pattern, pattern selection, spatiotemporal intermittency and fully developed spatiotemporal chaos. We map the distinct dynamical regions of the system in two-parameter space. Finally, we quantify the spatiotemporal dynamics by using quantitative measures like Lyapunov exponent and the average quadratic deviation of the full network.

  4. Cascading failures in coupled networks with both inner-dependency and inter-dependency links.

    PubMed

    Liu, Run-Ran; Li, Ming; Jia, Chun-Xiao; Wang, Bing-Hong

    2016-05-04

    We study the percolation in coupled networks with both inner-dependency and inter-dependency links, where the inner- and inter-dependency links represent the dependencies between nodes in the same or different networks, respectively. We find that when most of dependency links are inner- or inter-ones, the coupled networks system is fragile and makes a discontinuous percolation transition. However, when the numbers of two types of dependency links are close to each other, the system is robust and makes a continuous percolation transition. This indicates that the high density of dependency links could not always lead to a discontinuous percolation transition as the previous studies. More interestingly, although the robustness of the system can be optimized by adjusting the ratio of the two types of dependency links, there exists a critical average degree of the networks for coupled random networks, below which the crossover of the two types of percolation transitions disappears, and the system will always demonstrate a discontinuous percolation transition. We also develop an approach to analyze this model, which is agreement with the simulation results well.

  5. The N and C Termini of ZO-1 Are Surrounded by Distinct Proteins and Functional Protein Networks*

    PubMed Central

    Van Itallie, Christina M.; Aponte, Angel; Tietgens, Amber Jean; Gucek, Marjan; Fredriksson, Karin; Anderson, James Melvin

    2013-01-01

    The proteins and functional protein networks of the tight junction remain incompletely defined. Among the currently known proteins are barrier-forming proteins like occludin and the claudin family; scaffolding proteins like ZO-1; and some cytoskeletal, signaling, and cell polarity proteins. To define a more complete list of proteins and infer their functional implications, we identified the proteins that are within molecular dimensions of ZO-1 by fusing biotin ligase to either its N or C terminus, expressing these fusion proteins in Madin-Darby canine kidney epithelial cells, and purifying and identifying the resulting biotinylated proteins by mass spectrometry. Of a predicted proteome of ∼9000, we identified more than 400 proteins tagged by biotin ligase fused to ZO-1, with both identical and distinct proteins near the N- and C-terminal ends. Those proximal to the N terminus were enriched in transmembrane tight junction proteins, and those proximal to the C terminus were enriched in cytoskeletal proteins. We also identified many unexpected but easily rationalized proteins and verified partial colocalization of three of these proteins with ZO-1 as examples. In addition, functional networks of interacting proteins were tagged, such as the basolateral but not apical polarity network. These results provide a rich inventory of proteins and potential novel insights into functions and protein networks that should catalyze further understanding of tight junction biology. Unexpectedly, the technique demonstrates high spatial resolution, which could be generally applied to defining other subcellular protein compartmentalization. PMID:23553632

  6. Compiled data set of exact NOE distance limits, residual dipolar couplings and scalar couplings for the protein GB3

    PubMed Central

    Vögeli, Beat; Olsson, Simon; Riek, Roland; Güntert, Peter

    2015-01-01

    We compiled an NMR data set consisting of exact nuclear Overhauser enhancement (eNOE) distance limits, residual dipolar couplings (RDCs) and scalar (J) couplings for GB3, which forms one of the largest and most diverse data set for structural characterization of a protein to date. All data have small experimental errors, which are carefully estimated. We use the data in the research article Vogeli et al., 2015, Complementarity and congruence between exact NOEs and traditional NMR probes for spatial decoding of protein dynamics, J. Struct. Biol., 191, 3, 306–317, doi:10.1016/j.jsb.2015.07.008 [1] for cross-validation in multiple-state structural ensemble calculation. We advocate this set to be an ideal test case for molecular dynamics simulations and structure calculations. PMID:26504890

  7. Improving Protein Fold Recognition by Deep Learning Networks

    PubMed Central

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold. PMID:26634993

  8. Improving Protein Fold Recognition by Deep Learning Networks.

    PubMed

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-04

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  9. Improving Protein Fold Recognition by Deep Learning Networks

    NASA Astrophysics Data System (ADS)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-01

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  10. Improving Protein Fold Recognition by Deep Learning Networks.

    PubMed

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold. PMID:26634993

  11. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism.

    PubMed

    Li, Lulu; Ho, Daniel W C; Cao, Jinde; Lu, Jianquan

    2016-04-01

    Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results.

  12. Lag Synchronization Between Two Coupled Networks via Open-Plus-Closed-Loop and Adaptive Controls

    NASA Astrophysics Data System (ADS)

    Hu, Tong-Chun; Wu, Yong-Qing; Li, Shi-Xing

    2016-01-01

    In this paper, we study lag synchronization between two coupled networks and apply two types of control schemes, including the open-plus-closed-loop (OPCL) and adaptive controls. We then design the corresponding control algorithms according to the OPCL and adaptive feedback schemes. With the designed controllers, we obtain two theorems on the lag synchronization based on Lyapunov stability theory and Barbalat's lemma. Finally we provide numerical examples to show the effectiveness of the obtained controllers and see that the adaptive control is stronger than the OPCL control when realizing the lag synchronization between two coupled networks with different coupling structures. Supported by the National Natural Science Foundation of China under Grant No. 61304173, Foundation of Liaoning Educational Committee (No. 13-1069) and Hangzhou Polytechnic (No. KZYZ-2009-2)

  13. Monolignol radical-radical coupling networks in western red cedar and Arabidopsis and their evolutionary implications

    NASA Technical Reports Server (NTRS)

    Kim, Myoung K.; Jeon, Jae-Heung; Davin, Laurence B.; Lewis, Norman G.

    2002-01-01

    The discovery of a nine-member multigene dirigent family involved in control of monolignol radical-radical coupling in the ancient gymnosperm, western red cedar, suggested that a complex multidimensional network had evolved to regulate such processes in vascular plants. Accordingly, in this study, the corresponding promoter regions for each dirigent multigene member were obtained by genome-walking, with Arabidopsis being subsequently transformed to express each promoter fused to the beta-glucuronidase (GUS) reporter gene. It was found that each component gene of the proposed network is apparently differentially expressed in individual tissues, organs and cells at all stages of plant growth and development. The data so obtained thus further support the hypothesis that a sophisticated monolignol radical-radical coupling network exists in plants which has been highly conserved throughout vascular plant evolution.

  14. Noise-sustained synchronization of electrically coupled FitzHugh-Nagumo networks under counterphase external forcing

    NASA Astrophysics Data System (ADS)

    Sánchez, Alejandro D.; Izús, Gonzalo G.

    2016-05-01

    We study the stochastic dynamics of two electrically coupled networks of excitable FitzHugh-Nagumo cells, each of them phase-repulsively linked to form a ring able to develop noise-sustained structures. All cells are submitted to Gaussian white noises with common intensity η, while each network is forced with opposite phase by an adiabatic subthreshold harmonic signal. In terms of the nonequilibrium potential of a four-cell reduced model we have interpreted the dynamics, explained the observed activation and synchronization of the structures, and calculated the optimal η level as a function of coupling strength between networks. The values obtained from the reduced model coincide in order of magnitude with those arising from numerical simulations of the full system.

  15. Distributed adaptive pinning control for cluster synchronization of nonlinearly coupled Lur'e networks

    NASA Astrophysics Data System (ADS)

    Tang, Ze; Park, Ju H.; Lee, Tae H.

    2016-10-01

    This paper is devoted to the cluster synchronization issue of nonlinearly coupled Lur'e networks under the distributed adaptive pinning control strategy. The time-varying delayed networks consisted of identical and nonidentical Lur'e systems are discussed respectively by applying the edge-based pinning control scheme. In each cluster, the edges belonging to the spanning tree are pinned. In view of the nonlinearly couplings of the networks, for the first time, an efficient distributed nonlinearly adaptive update law based on the local information of the dynamical behaviors of node is proposed. Sufficient criteria for the achievement of cluster synchronization are derived based on S-procedure, Kronecker product and Lyapunov stability theory. Additionally, some illustrative examples are provided to demonstrate the effectiveness of the theoretical results.

  16. A second-generation protein-protein interaction network of Helicobacter pylori.

    PubMed

    Häuser, Roman; Ceol, Arnaud; Rajagopala, Seesandra V; Mosca, Roberto; Siszler, Gabriella; Wermke, Nadja; Sikorski, Patricia; Schwarz, Frank; Schick, Matthias; Wuchty, Stefan; Aloy, Patrick; Uetz, Peter

    2014-05-01

    Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein-protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.

  17. Protein networks in induced sputum from smokers and COPD patients

    PubMed Central

    Baraniuk, James N; Casado, Begona; Pannell, Lewis K; McGarvey, Peter B; Boschetto, Piera; Luisetti, Maurizio; Iadarola, Paolo

    2015-01-01

    Rationale Subtypes of cigarette smoke-induced disease affect different lung structures and may have distinct pathophysiological mechanisms. Objective To determine if proteomic classification of the cellular and vascular origins of sputum proteins can characterize these mechanisms and phenotypes. Subjects and methods Individual sputum specimens from lifelong nonsmokers (n=7) and smokers with normal lung function (n=13), mucous hypersecretion with normal lung function (n=11), obstructed airflow without emphysema (n=15), and obstruction plus emphysema (n=10) were assessed with mass spectrometry. Data reduction, logarithmic transformation of spectral counts, and Cytoscape network-interaction analysis were performed. The original 203 proteins were reduced to the most informative 50. Sources were secretory dimeric IgA, submucosal gland serous and mucous cells, goblet and other epithelial cells, and vascular permeability. Results Epithelial proteins discriminated nonsmokers from smokers. Mucin 5AC was elevated in healthy smokers and chronic bronchitis, suggesting a continuum with the severity of hypersecretion determined by mechanisms of goblet-cell hyperplasia. Obstructed airflow was correlated with glandular proteins and lower levels of Ig joining chain compared to other groups. Emphysema subjects’ sputum was unique, with high plasma proteins and components of neutrophil extracellular traps, such as histones and defensins. In contrast, defensins were correlated with epithelial proteins in all other groups. Protein-network interactions were unique to each group. Conclusion The proteomes were interpreted as complex “biosignatures” that suggest distinct pathophysiological mechanisms for mucin 5AC hypersecretion, airflow obstruction, and inflammatory emphysema phenotypes. Proteomic phenotyping may improve genotyping studies by selecting more homogeneous study groups. Each phenotype may require its own mechanistically based diagnostic, risk-assessment, drug- and other

  18. The prion protein is an agonistic ligand of the G protein-coupled receptor Adgrg6.

    PubMed

    Küffer, Alexander; Lakkaraju, Asvin K K; Mogha, Amit; Petersen, Sarah C; Airich, Kristina; Doucerain, Cédric; Marpakwar, Rajlakshmi; Bakirci, Pamela; Senatore, Assunta; Monnard, Arnaud; Schiavi, Carmen; Nuvolone, Mario; Grosshans, Bianka; Hornemann, Simone; Bassilana, Frederic; Monk, Kelly R; Aguzzi, Adriano

    2016-08-25

    Ablation of the cellular prion protein PrP(C) leads to a chronic demyelinating polyneuropathy affecting Schwann cells. Neuron-restricted expression of PrP(C) prevents the disease, suggesting that PrP(C) acts in trans through an unidentified Schwann cell receptor. Here we show that the cAMP concentration in sciatic nerves from PrP(C)-deficient mice is reduced, suggesting that PrP(C) acts via a G protein-coupled receptor (GPCR). The amino-terminal flexible tail (residues 23-120) of PrP(C) triggered a concentration-dependent increase in cAMP in primary Schwann cells, in the Schwann cell line SW10, and in HEK293T cells overexpressing the GPCR Adgrg6 (also known as Gpr126). By contrast, naive HEK293T cells and HEK293T cells expressing several other GPCRs did not react to the flexible tail, and ablation of Gpr126 from SW10 cells abolished the flexible tail-induced cAMP response. The flexible tail contains a polycationic cluster (KKRPKPG) similar to the GPRGKPG motif of the Gpr126 agonist type-IV collagen. A KKRPKPG-containing PrPC-derived peptide (FT(23-50)) sufficed to induce a Gpr126-dependent cAMP response in cells and mice, and improved myelination in hypomorphic gpr126 mutant zebrafish (Danio rerio). Substitution of the cationic residues with alanines abolished the biological activity of both FT(23-50) and the equivalent type-IV collagen peptide. We conclude that PrP(C) promotes myelin homeostasis through flexible tail-mediated Gpr126 agonism. As well as clarifying the physiological role of PrP(C), these observations are relevant to the pathogenesis of demyelinating polyneuropathies--common debilitating diseases for which there are limited therapeutic options. PMID:27501152

  19. G-protein coupled receptor-associated sorting protein 1 (GASP-1), a ubiquitous tumor marker.

    PubMed

    Zheng, Xiaoyi; Chang, Frank; Zhang, Xinmin; Rothman, Vicki L; Tuszynski, George P

    2012-08-01

    Using an innovative "2-D high performance liquid electrophoresis" (2-D HPLE) technology we identified that a specific fragment of G-protein coupled receptor-associated sorting protein 1 (GASP-1) was present in the sera of breast cancer patients and was over-expressed in early and late stage breast tumors (Tuszynski, G.P. et al., 2011). In this study we further investigated the significance of GASP-1 as a tumor marker by investigating the expression GASP-1 in different kinds of tumors as well as in the sera of patients with various cancers. Over expression of GASP-1 was detected in brain, pancreatic, and breast cancers as compared to their respective normal tissues as assessed by immunohistochemical staining of tissue arrays using a "peptide specific" GASP-1 antibody. We found that across these cancers, GASP-1 was expressed approximately 10 fold more in the cancer as compared to normal tissue. The increase in GASP-1 expression was also seen in hyperplastic and inflammatory lesions of breast and pancreatic cancers as compared to normal tissue. GASP-1 was primarily expressed in the tumor epithelium of the epithelial-derived cancers and in the transformed glial cells of the brain tumors. Using a sensitive "competitive ELISA" for GASP-1, we found that sera from patients with brain, liver, breast and lung cancers expressed 4-7 fold more GASP-1 peptide than sera from normal healthy individuals. These studies identify GASP-1 as a potential new serum and tumor biomarker for several cancers and suggest that GASP-1 may be a novel target for development of cancer therapeutics. PMID:22483848

  20. Amyloid Beta-Protein and Neural Network Dysfunction

    PubMed Central

    Peña-Ortega, Fernando

    2013-01-01

    Understanding the neural mechanisms underlying brain dysfunction induced by amyloid beta-protein (Aβ) represents one of the major challenges for Alzheimer's disease (AD) research. The most evident symptom of AD is a severe decline in cognition. Cognitive processes, as any other brain function, arise from the activity of specific cell assemblies of interconnected neurons that generate neural network dynamics based on their intrinsic and synaptic properties. Thus, the origin of Aβ-induced cognitive dysfunction, and possibly AD-related cognitive decline, must be found in specific alterations in properties of these cells and their consequences in neural network dynamics. The well-known relationship between AD and alterations in the activity of several neural networks is reflected in the slowing of the electroencephalographic (EEG) activity. Some features of the EEG slowing observed in AD, such as the diminished generation of different network oscillations, can be induced in vivo and in vitro upon Aβ application or by Aβ overproduction in transgenic models. This experimental approach offers the possibility to study the mechanisms involved in cognitive dysfunction produced by Aβ. This type of research may yield not only basic knowledge of neural network dysfunction associated with AD, but also novel options to treat this modern epidemic. PMID:26316994

  1. Minireview: Role of intracellular scaffolding proteins in the regulation of endocrine G protein-coupled receptor signaling.

    PubMed

    Walther, Cornelia; Ferguson, Stephen S G

    2015-06-01

    The majority of hormones stimulates and mediates their signal transduction via G protein-coupled receptors (GPCRs). The signal is transmitted into the cell due to the association of the GPCRs with heterotrimeric G proteins, which in turn activates an extensive array of signaling pathways to regulate cell physiology. However, GPCRs also function as scaffolds for the recruitment of a variety of cytoplasmic protein-interacting proteins that bind to both the intracellular face and protein interaction motifs encoded by GPCRs. The structural scaffolding of these proteins allows GPCRs to recruit large functional complexes that serve to modulate both G protein-dependent and -independent cellular signaling pathways and modulate GPCR intracellular trafficking. This review focuses on GPCR interacting PSD95-disc large-zona occludens domain containing scaffolds in the regulation of endocrine receptor signaling as well as their potential role as therapeutic targets for the treatment of endocrinopathies.

  2. Protein Unfolding Coupled to Ligand Binding: Differential Scanning Calorimetry Simulation Approach

    ERIC Educational Resources Information Center

    Celej, Maria Soledad; Fidelio, Gerardo Daniel; Dassie, Sergio Alberto

    2005-01-01

    A comprehensive theoretical description of thermal protein unfolding coupled to ligand binding is presented. The thermodynamic concepts are independent of the method used to monitor protein unfolding but a differential scanning calorimetry is being used as a tool for examining the unfolding process.

  3. An automated approach to network features of protein structure ensembles.

    PubMed

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-10-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

  4. Text mining for metabolic pathways, signaling cascades, and protein networks.

    PubMed

    Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

    2005-05-10

    The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks. PMID:15886388

  5. Robust synchronization of complex networks with uncertain couplings and incomplete information

    NASA Astrophysics Data System (ADS)

    Wang, Fan; Liang, Jinling; Wang, Zidong; Alsaadi, Fuad E.

    2016-07-01

    The mean square exponential (MSE) synchronization problem is investigated in this paper for complex networks with simultaneous presence of uncertain couplings and incomplete information, which comprise both the randomly occurring delay and the randomly occurring non-linearities. The network considered is uncertain with time-varying stochastic couplings. The randomly occurring delay and non-linearities are modelled by two Bernoulli-distributed white sequences with known probabilities to better describe realistic complex networks. By utilizing the coordinate transformation, the addressed complex network can be exponentially synchronized in the mean square if the MSE stability of a transformed subsystem can be assured. The stability problem is studied firstly for the transformed subsystem based on the Lyapunov functional method. Then, an easy-to-verify sufficient criterion is established by further decomposing the transformed system, which embodies the joint impacts of the single-node dynamics, the network topology and the statistical quantities of the uncertainties on the synchronization of the complex network. Numerical examples are exploited to illustrate the effectiveness of the proposed methods.

  6. Response functions for electrically coupled neuronal network: a method of local point matching and its applications.

    PubMed

    Yihe, Lu; Timofeeva, Yulia

    2016-06-01

    Neuronal networks connected by electrical synapses, also referred to as gap junctions, are present throughout the entire central nervous system. Many instances of gap-junctional coupling are formed between dendritic arbours of individual cells, and these dendro-dendritic gap junctions are known to play an important role in mediating various brain rhythms in both normal and pathological states. The dynamics of such neuronal networks modelled by passive or quasi-active (resonant) membranes can be described by the Green's function which provides the fundamental input-output relationships of the entire network. One of the methods for calculating this response function is the so-called 'sum-over-trips' framework which enables the construction of the Green's function for an arbitrary network as a convergent infinite series solution. Here we propose an alternative and computationally efficient approach for constructing the Green's functions on dendro-dendritic gap junction-coupled neuronal networks which avoids any infinite terms in the solutions. Instead, the Green's function is constructed from the solution of a system of linear algebraic equations. We apply this new method to a number of systems including a simple single cell model and two-cell neuronal networks. We also demonstrate that the application of this novel approach allows one to reduce a model with complex dendritic formations to an equivalent model with a much simpler morphological structure. PMID:26994016

  7. Distinct profiles of functional discrimination among G proteins determine the actions of G protein-coupled receptors.

    PubMed

    Masuho, Ikuo; Ostrovskaya, Olga; Kramer, Grant M; Jones, Christopher D; Xie, Keqiang; Martemyanov, Kirill A

    2015-12-01

    Members of the heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptor (GPCR) family play key roles in many physiological functions and are extensively exploited pharmacologically to treat diseases. Many of the diverse effects of individual GPCRs on cellular physiology are transduced by heterotrimeric G proteins, which are composed of α, β, and γ subunits. GPCRs interact with and stimulate the binding of guanosine triphosphate (GTP) to the α subunit to initiate signaling. Mammalian genomes encode 16 different G protein α subunits, each one of which has distinct properties. We developed a single-platform, optical strategy to monitor G protein activation in live cells. With this system, we profiled the coupling ability of individual GPCRs for different α subunits, simultaneously quantifying the magnitude of the signal and the rates at which the receptors activated the G proteins. We found that individual receptors engaged multiple G proteins with varying efficacy and kinetics, generating fingerprint-like profiles. Different classes of GPCR ligands, including full and partial agonists, allosteric modulators, and antagonists, distinctly affected these fingerprints to functionally bias GPCR signaling. Finally, we showed that intracellular signaling modulators further altered the G protein-coupling profiles of GPCRs, which suggests that their differential abundance may alter signaling outcomes in a cell-specific manner. These observations suggest that the diversity of the effects of GPCRs on cellular physiology may be determined by their differential engagement of multiple G proteins, coupling to which produces signals with varying signal magnitudes and activation kinetics, properties that may be exploited pharmacologically. PMID:26628681

  8. Distinguishing between direct and indirect directional couplings in large oscillator networks: Partial or non-partial phase analyses?

    NASA Astrophysics Data System (ADS)

    Rings, Thorsten; Lehnertz, Klaus

    2016-09-01

    We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient. For such networks, both direct and indirect directional couplings can be expected, given the brain's connection structure and effects that may arise from limitations inherent to the recording technique. Our findings indicate that particularly in larger networks (number of nodes ≫10 ), the partialized approach does not provide information about directional couplings extending the information gained with the evolution map approach.

  9. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.

  10. Allosteric mechanisms of G protein coupled receptor signaling: a structural perspective

    PubMed Central

    Thaker, Tarjani M.; Kaya, Ali I.; Preininger, Anita M.; Hamm, Heidi E.; Iverson, T.M.

    2012-01-01

    G protein-Coupled Receptors (GPCRs) use a complex series of intramolecular conformational changes to couple agonist binding to the binding and activation of cognate heterotrimeric G protein (Gαβγ). The mechanisms underlying this long-range activation have been identified using a variety of biochemical and structural approaches and have primarily used visual signal transduction via the GPCR rhodopsin and cognate heterotrimeric G protein transducin (Gt) as a model system. In this chapter, we will review the methods that have revealed allosteric signaling through rhodopsin and transducin. These methods can be applied to a variety of GPCR-mediated signaling pathways. PMID:22052489

  11. Energy patterns in coupled α-helix protein chains with diagonal and off-diagonal couplings

    NASA Astrophysics Data System (ADS)

    Tabi, C. B.; Ondoua, R. Y.; Ekobena Fouda, H. P.; Kofané, T. C.

    2016-07-01

    We introduce off-diagonal effects in the three-stranded model of α-helix chains, which bring about additional nonlinear terms to enhance the way energy spreads among the coupled spines. This is analyzed through the modulational instability theory. The linear stability analysis of plane wave solutions is performed and the competitive effects of diagonal and off-diagonal interactions are studied, followed by direct numerical simulations. Some features of the obtained solitonic structures are discussed.

  12. The structural evolution of a P2Y-like G-protein-coupled receptor.

    PubMed

    Schulz, Angela; Schöneberg, Torsten

    2003-09-12

    Based on the now available crystallographic data of the G-protein-coupled receptor (GPCR) prototype rhodopsin, many studies have been undertaken to build or verify models of other GPCRs. Here, we mined evolution as an additional source of structural information that may guide GPCR model generation as well as mutagenesis studies. The sequence information of 61 cloned orthologs of a P2Y-like receptor (GPR34) enabled us to identify motifs and residues that are important for maintaining the receptor function. The sequence data were compared with available sequences of 77 rhodopsin orthologs. Under a negative selection mode, only 17% of amino acid residues were preserved during 450 million years of GPR34 evolution. On the contrary, in rhodopsin evolution approximately 43% residues were absolutely conserved between fish and mammals. Despite major differences in their structural conservation, a comparison of structural data suggests that the global arrangement of the transmembrane core of GPR34 orthologs is similar to rhodopsin. The evolutionary approach was further applied to functionally analyze the relevance of common scaffold residues and motifs found in most of the rhodopsin-like GPCRs. Our analysis indicates that, in contrast to other GPCRs, maintaining the unique function of rhodopsin requires a more stringent network of relevant intramolecular constrains. PMID:12835326

  13. Allosteric inhibition of g-protein coupled receptor oligomerization: strategies and challenges for drug development.

    PubMed

    Hurevich, Mattan; Talhami, Alaa; Shalev, Deborah E; Gilon, Chaim

    2014-01-01

    G-protein coupled receptors (GPCRs) mediate a large number of biological pathways and are major therapeutic targets. One of the most exiting phenomena of GPCRs is their ability to interact with other GPCRs. GPCRGPCR interactions, also known as GPCR oligomerization, may create various functional entities such as homo- and heterodimers and also form complex multimeric GPCR clusters. In many biological systems, GPCR-GPCR interactions are crucial for signal regulation. The interaction with other receptors results in allosteric modifications of GPCRs through conformational changes. Allosteric inhibition of GPCRs is considered an attractive strategy for drug development and does not involve targeting the orthosteric site. Understanding the nature of GPCR-GPCR interactions is mandatory for developing allosteric inhibitors. Studying GPCR-GPCR interactions is a challenging task and many methods have been developed to analyze these events. This review will highlight some of the methods developed to study GPCR-GPCR interactions and will describe pivotal studies that provided the basic understanding of the importance of GPCR oligomerization. We will also describe the significance of GPCR interaction networks for drug development. Recent studies will be reviewed to illustrate the use of state-of-the-art biophysical and spectroscopic methods for the discovery of GPCR oligomerization modulators.

  14. An algebra of dimerization and its implications for G-protein coupled receptor signaling.

    PubMed

    Woolf, Peter J; Linderman, Jennifer J

    2004-07-21

    Many species of receptors form dimers, but how can we use this information to make predictions about signal transduction? This problem is particularly difficult when receptors dimerize with many different species, leading to a combinatoric increase in the possible number of dimer pairs. As an example system, we focus on receptors in the G-protein coupled receptor (GPCR) family. GPCRs have been shown to reversibly form dimers, but this dimerization does not directly affect signal transduction. Here we present a new theoretical framework called a dimerization algebra. This algebra provides a systematic and rational way to represent, manipulate, and in some cases simplify large and often complicated networks of dimerization interactions. To compliment this algebra, Monte Carlo simulations are used to predict dimerization's effect on receptor organization on the membrane, signal transduction, and internalization. These simulation results are directly comparable to various experimental measures such as fluorescence resonance energy transfer (FRET), and as such provide a link between the dimerization algebra and experimental data. As an example, we show how the algebra and computational results can be used to predict the effects of dimerization on the dopamine D2 and somatastatin SSTR1 receptors. When these predictions were compared to experimental findings from the literature, good agreement was found, demonstrating the utility of our approach. Applications of this work to the development of a novel class of dimerization-modulating drugs are also discussed.

  15. Phospho-tyrosine dependent protein–protein interaction network

    PubMed Central

    Grossmann, Arndt; Benlasfer, Nouhad; Birth, Petra; Hegele, Anna; Wachsmuth, Franziska; Apelt, Luise; Stelzl, Ulrich

    2015-01-01

    Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes. PMID:25814554

  16. The antiferromagnetic cross-coupled spin ladder: Quantum fidelity and tensor networks approach

    NASA Astrophysics Data System (ADS)

    Chen, Xi-Hao; Cho, Sam Young; Zhou, Huan-Qiang; Batchelor, Murray T.

    2016-05-01

    We investigate the phase diagram of the cross-coupled Heisenberg spin ladder with antiferromagnetic couplings. For this model, the results for the existence of the columnar dimer phase, which was predicted on the basis of weak coupling field theory renormalization group arguments, have been conflicting. The numerical work on this model has been based on various approaches, including exact diagonalization, series expansions and density-matrix renormalization group calculations. Using the recently-developed tensor network states and groundstate fidelity approach for quantum spin ladders, we find no evidence for the existence of the columnar dimer phase. We also provide an argument based on the symmetry of the Hamiltonian, which suggests that the phase diagram for antiferromagnetic couplings consists of a single line separating the rung-singlet and the Haldane phases.

  17. Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation

    PubMed Central

    Isin, Basak; Tirupula, Kalyan C.; Oltvai, Zoltán N.; Klein-Seetharaman, Judith; Bahar, Ivet

    2016-01-01

    Identifying the functional motions of membrane proteins is difficult because they range from large-scale collective dynamics to local small atomic fluctuations at different timescales that are difficult to measure experimentally due to the hydrophobic nature of these proteins. Elastic Network Models, and in particular their most widely used implementation, the Anisotropic Network Model (ANM), have proven to be useful computational methods in many recent applications to predict membrane protein dynamics. These models are based on the premise that biomolecules possess intrinsic mechanical characteristics uniquely defined by their particular architectures. In the ANM, interactions between residues in close proximity are represented by harmonic potentials with a uniform spring constant. The slow mode shapes generated by the ANM provide valuable information on the global dynamics of biomolecules that are relevant to their function. In its recent extension in the form of ANM-guided molecular dynamics (MD), this coarse-grained approach is augmented with atomic detail. The results from ANM and its extensions can be used to guide experiments and thus speedup the process of quantifying motions in membrane proteins. Testing the predictions can be accomplished through (a) direct observation of motions through studies of structure and biophysical probes, (b) perturbation of the motions by, e.g., cross-linking or site-directed mutagenesis, and (c) by studying the effects of such perturbations on protein function, typically through ligand binding and activity assays. To illustrate the applicability of the combined computational ANM—experimental testing framework to membrane proteins, we describe—alongside the general protocols—here the application of ANM to rhodopsin, a prototypical member of the pharmacologically relevant G-protein coupled receptor family. PMID:22976035

  18. Topology of Protein Interaction Network Shapes Protein Abundances and Strengths of Their Functional and Nonspecific Interactions

    SciTech Connect

    Maslov, S.; Heo, M.; Shakhnovich, E.

    2011-03-08

    How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a 'frustration' effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.

  19. Computational methods for studying G protein-coupled receptors (GPCRs).

    PubMed

    Kaczor, Agnieszka A; Rutkowska, Ewelina; Bartuzi, Damian; Targowska-Duda, Katarzyna M; Matosiuk, Dariusz; Selent, Jana

    2016-01-01

    The functioning of GPCRs is classically described by the ternary complex model as the interplay of three basic components: a receptor, an agonist, and a G protein. According to this model, receptor activation results from an interaction with an agonist, which translates into the activation of a particular G protein in the intracellular compartment that, in turn, is able to initiate particular signaling cascades. Extensive studies on GPCRs have led to new findings which open unexplored and exciting possibilities for drug design and safer and more effective treatments with GPCR targeting drugs. These include discovery of novel signaling mechanisms such as ligand promiscuity resulting in multitarget ligands and signaling cross-talks, allosteric modulation, biased agonism, and formation of receptor homo- and heterodimers and oligomers which can be efficiently studied with computational methods. Computer-aided drug design techniques can reduce the cost of drug development by up to 50%. In particular structure- and ligand-based virtual screening techniques are a valuable tool for identifying new leads and have been shown to be especially efficient for GPCRs in comparison to water-soluble proteins. Modern computer-aided approaches can be helpful for the discovery of compounds with designed affinity profiles. Furthermore, homology modeling facilitated by a growing number of available templates as well as molecular docking supported by sophisticated techniques of molecular dynamics and quantitative structure-activity relationship models are an excellent source of information about drug-receptor interactions at the molecular level. PMID:26928552

  20. Coupled motion in proteins revealed by pressure perturbation

    PubMed Central

    Fu, Yinan; Kasinath, Vignesh; Moorman, Veronica R.; Nucci, Nathaniel V.; Hilser, Vincent J.; Wand, A. Joshua

    2012-01-01

    The cooperative nature of protein substructure and internal motion is a critical aspect of their functional competence about which little is known experimentally. NMR relaxation is used here to monitor the effects of high-pressure on fast internal motion in the protein ubiquitin. In contrast to the main chain, the motions of the methyl-bearing side chains have a large and variable pressure dependence. Within the core, this pressure sensitivity correlates with the magnitude of motion at ambient pressure. Spatial clustering of the dynamic response to applied hydrostatic pressure is also seen indicating localized cooperativity of motion on the sub-nanosecond time scale and suggesting regions of variable compressibility. These and other features indicate that the native ensemble contains a significant fraction of members with characteristics ascribed to the recently postulated “dry molten globule.” The accompanying variable side chain conformational entropy helps complete our view of the thermodynamic architecture underlying protein stability, folding and function. PMID:22452540

  1. Synchronisation in networks of delay-coupled type-I excitable systems

    NASA Astrophysics Data System (ADS)

    Keane, A.; Dahms, T.; Lehnert, J.; Suryanarayana, S. A.; Hövel, P.; Schöll, E.

    2012-12-01

    We use a generic model for type-I excitability (known as the SNIPER or SNIC model) to describe the local dynamics of nodes within a network in the presence of non-zero coupling delays. Utilising the method of the Master Stability Function, we investigate the stability of the zero-lag synchronised dynamics of the network nodes and its dependence on the two coupling parameters, namely the coupling strength and delay time. Unlike in the FitzHugh-Nagumo model (a model for type-II excitability), there are parameter ranges where the stability of synchronisation depends on the coupling strength and delay time. One important implication of these results is that there exist complex networks for which the adding of inhibitory links in a small-world fashion may not only lead to a loss of stable synchronisation, but may also restabilise synchronisation or introduce multiple transitions between synchronisation and desynchronisation. To underline the scope of our results, we show using the Stuart-Landau model that such multiple transitions do not only occur in excitable systems, but also in oscillatory ones.

  2. Adaptive oscillator networks with conserved overall coupling: Sequential firing and near-synchronized states

    NASA Astrophysics Data System (ADS)

    Picallo, Clara B.; Riecke, Hermann

    2011-03-01

    Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.

  3. Noise-sustained synchronization between electrically coupled FitzHugh-Nagumo networks

    NASA Astrophysics Data System (ADS)

    Cascallares, Guadalupe; Sánchez, Alejandro D.; dell'Erba, Matías G.; Izús, Gonzalo G.

    2015-09-01

    We investigate the capability of electrical synapses to transmit the noise-sustained network activity from one network to another. The particular setup we consider is two identical rings with excitable FitzHugh-Nagumo cell dynamics and nearest-neighbor antiphase intra-ring coupling, electrically coupled between corresponding nodes. The whole system is submitted to independent local additive Gaussian white noises with common intensity η, but only one ring is externally forced by a global adiabatic subthreshold harmonic signal. We then seek conditions for a particular noise level to promote synchronized stable firing patterns. By running numerical integrations with increasing η, we observe the excitation activity to become spatiotemporally self-organized, until η is so strong that spoils sync between networks for a given value of the electric coupling strength. By means of a four-cell model and calculating the stationary probability distribution, we obtain a (signal-dependent) non-equilibrium potential landscape which explains qualitatively the observed regimes, and whose barrier heights give a good estimate of the optimal noise intensity for the sync between networks.

  4. Experimental mapping of nonlinear dynamics in synchronized coupled semiconductor laser networks

    NASA Astrophysics Data System (ADS)

    Argyris, Apostolos; Bourmpos, Michail; Syvridis, Dimitris

    2015-05-01

    The potential of conventional semiconductor lasers to generate complex and chaotic dynamics at a bandwidth that extends up to tens of GHz turns them into useful components in applications oriented to sensing and security. Specifically, latest theoretical and experimental works have demonstrated the capability of mutually coupled semiconductor lasers to exhibit a joint behaviour under various conditions. In an uncoupled network consisting of N similar SLs - representing autonomous nodes in the network - each node emits an optical signal of various dynamics depending on its biasing conditions and internal properties. These nodes remain unsynchronized unless appropriate coupling and biasing conditions apply. A synchronized behaviour can be in principle observed in sub-groups of lasers or in the overall laser network. In the present work, experimental topologies that employ eight SLs, under diverse biasing and coupling conditions, are built and investigated. The deployed systems incorporate off-the-shelf fiber-optic communications components operating at the 1550nm spectral window. The role of emission wavelength detuning of each participating node in the network - at GHz level - is evaluated.

  5. Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity

    PubMed Central

    Wang, Lubin; Zhai, Tianye; Zou, Feng; Ye, Enmao; Jin, Xiao; Li, Wuju; Qi, Jianlin; Yang, Zheng

    2015-01-01

    Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation. PMID:26218521

  6. Predicting protein functions from redundancies in large-scale protein interaction networks

    NASA Technical Reports Server (NTRS)

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

  7. Protein and Signaling Networks in Vertebrate Photoreceptor Cells

    PubMed Central

    Koch, Karl-Wilhelm; Dell’Orco, Daniele

    2015-01-01

    Vertebrate photoreceptor cells are exquisite light detectors operating under very dim and bright illumination. The photoexcitation and adaptation machinery in photoreceptor cells consists of protein complexes that can form highly ordered supramolecular structures and control the homeostasis and mutual dependence of the secondary messengers cyclic guanosine monophosphate (cGMP) and Ca2+. The visual pigment in rod photoreceptors, the G protein-coupled receptor rhodopsin is organized in tracks of dimers thereby providing a signaling platform for the dynamic scaffolding of the G protein transducin. Illuminated rhodopsin is turned off by phosphorylation catalyzed by rhodopsin kinase (GRK1) under control of Ca2+-recoverin. The GRK1 protein complex partly assembles in lipid raft structures, where shutting off rhodopsin seems to be more effective. Re-synthesis of cGMP is another crucial step in the recovery of the photoresponse after illumination. It is catalyzed by membrane bound sensory guanylate cyclases (GCs) and is regulated by specific neuronal Ca2+-sensor proteins called guanylate cyclase-activating proteins (GCAPs). At least one GC (ROS-GC1) was shown to be part of a multiprotein complex having strong interactions with the cytoskeleton and being controlled in a multimodal Ca2+-dependent fashion. The final target of the cGMP signaling cascade is a cyclic nucleotide-gated (CNG) channel that is a hetero-oligomeric protein located in the plasma membrane and interacting with accessory proteins in highly organized microdomains. We summarize results and interpretations of findings related to the inhomogeneous organization of signaling units in photoreceptor outer segments. PMID:26635520

  8. Convolutional neural network architectures for predicting DNA–protein binding

    PubMed Central

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  9. Synergistic plasticity of intrinsic conductance and electrical coupling restores synchrony in an intact motor network

    PubMed Central

    Lane, Brian J; Samarth, Pranit; Ransdell, Joseph L; Nair, Satish S; Schulz, David J

    2016-01-01

    Motor neurons of the crustacean cardiac ganglion generate virtually identical, synchronized output despite the fact that each neuron uses distinct conductance magnitudes. As a result of this variability, manipulations that target ionic conductances have distinct effects on neurons within the same ganglion, disrupting synchronized motor neuron output that is necessary for proper cardiac function. We hypothesized that robustness in network output is accomplished via plasticity that counters such destabilizing influences. By blocking high-threshold K+ conductances in motor neurons within the ongoing cardiac net