WorldWideScience

Sample records for alpha-helical protein networks

  1. Conformons in alpha-helical proteins

    CERN Document Server

    Atanasov, Victor

    2009-01-01

    We propose the conformon as a quantum of conformational change for energy transfer in alpha-helical proteins. The underlying mechanism of interaction between the quantum of excitation and the conformational degrees of freedom is nonlinear and leads to solitary wave packets of conformational energy. The phenomenon is specific to alpha-helices and not to beta-sheets in proteins due to the three strands of hydrogen bonds constituting the alpha-helical backbone.

  2. Alpha-helices direct excitation energy flow in the Fenna Matthews Olson protein.

    Science.gov (United States)

    Müh, Frank; Madjet, Mohamed El-Amine; Adolphs, Julia; Abdurahman, Ayjamal; Rabenstein, Björn; Ishikita, Hiroshi; Knapp, Ernst-Walter; Renger, Thomas

    2007-10-23

    In photosynthesis, light is captured by antenna proteins. These proteins transfer the excitation energy with almost 100% quantum efficiency to the reaction centers, where charge separation takes place. The time scale and pathways of this transfer are controlled by the protein scaffold, which holds the pigments at optimal geometry and tunes their excitation energies (site energies). The detailed understanding of the tuning of site energies by the protein has been an unsolved problem since the first high-resolution crystal structure of a light-harvesting antenna appeared >30 years ago [Fenna RE, Matthews BW (1975) Nature 258:573-577]. Here, we present a combined quantum chemical/electrostatic approach to compute site energies that considers the whole protein in atomic detail and provides the missing link between crystallography and spectroscopy. The calculation of site energies of the Fenna-Matthews-Olson protein results in optical spectra that are in quantitative agreement with experiment and reveals an unexpectedly strong influence of the backbone of two alpha-helices. The electric field from the latter defines the direction of excitation energy flow in the Fenna-Matthews-Olson protein, whereas the effects of amino acid side chains, hitherto thought to be crucial, largely compensate each other. This result challenges the current view of how energy flow is regulated in pigment-protein complexes and demonstrates that attention has to be paid to the backbone architecture. PMID:17940020

  3. Structural Origins of Nitroxide Side Chain Dynamics on Membrane Protein [alpha]-Helical Sites

    Energy Technology Data Exchange (ETDEWEB)

    Kroncke, Brett M.; Horanyi, Peter S.; Columbus, Linda (UV)

    2010-12-07

    Understanding the structure and dynamics of membrane proteins in their native, hydrophobic environment is important to understanding how these proteins function. EPR spectroscopy in combination with site-directed spin labeling (SDSL) can measure dynamics and structure of membrane proteins in their native lipid environment; however, until now the dynamics measured have been qualitative due to limited knowledge of the nitroxide spin label's intramolecular motion in the hydrophobic environment. Although several studies have elucidated the structural origins of EPR line shapes of water-soluble proteins, EPR spectra of nitroxide spin-labeled proteins in detergents or lipids have characteristic differences from their water-soluble counterparts, suggesting significant differences in the underlying molecular motion of the spin label between the two environments. To elucidate these differences, membrane-exposed {alpha}-helical sites of the leucine transporter, LeuT, from Aquifex aeolicus, were investigated using X-ray crystallography, mutational analysis, nitroxide side chain derivatives, and spectral simulations in order to obtain a motional model of the nitroxide. For each crystal structure, the nitroxide ring of a disulfide-linked spin label side chain (R1) is resolved and makes contacts with hydrophobic residues on the protein surface. The spin label at site I204 on LeuT makes a nontraditional hydrogen bond with the ortho-hydrogen on its nearest neighbor F208, whereas the spin label at site F177 makes multiple van der Waals contacts with a hydrophobic pocket formed with an adjacent helix. These results coupled with the spectral effect of mutating the i {+-} 3, 4 residues suggest that the spin label has a greater affinity for its local protein environment in the low dielectric than on a water-soluble protein surface. The simulations of the EPR spectra presented here suggest the spin label oscillates about the terminal bond nearest the ring while maintaining weak

  4. Crystal structure of tetranectin, a trimeric plasminogen-binding protein with an alpha-helical coiled coil

    DEFF Research Database (Denmark)

    Nielsen, B B; Kastrup, J S; Rasmussen, H;

    1997-01-01

    Tetranectin is a plasminogen kringle 4-binding protein. The crystal structure has been determined at 2.8 A resolution using molecular replacement. Human tetranectin is a homotrimer forming a triple alpha-helical coiled coil. Each monomer consists of a carbohydrate recognition domain (CRD) connected...... the third is present only in long-form CRDs. Tetranectin represents the first structure of a long-form CRD with intact calcium-binding sites. In tetranectin, the third disulfide bridge tethers the CRD to the long helix in the coiled coil. The trimerization of tetranectin as well as the fixation of the CRDs...... relative to the helices in the coiled coil indicate a demand for high specificity in the recognition and binding of ligands....

  5. The alpha-helical domain of liver fatty acid binding protein is responsible for the diffusion-mediated transfer of fatty acids to phospholipid membranes.

    Science.gov (United States)

    Córsico, Betina; Liou, Heng Ling; Storch, Judith

    2004-03-30

    Intestinal fatty acid binding protein (IFABP) and liver FABP (LFABP), homologous proteins expressed at high levels in intestinal absorptive cells, employ markedly different mechanisms for the transfer of fatty acids (FAs) to acceptor membranes. Transfer from IFABP occurs during protein-membrane collisional interactions, while for LFABP, transfer occurs by diffusion through the aqueous phase. Earlier, we had shown that the helical domain of IFABP is critical in determining its collisional FA transfer mechanism. In the study presented here, we have engineered a pair of chimeric proteins, one with the "body" (ligand binding domain) of IFABP and the alpha-helical region of LFABP (alphaLbetaIFABP) and the other with the ligand binding pocket of LFABP and the helical domain of IFABP (alphaIbetaLFABP). The objective of this work was to determine whether the change in the alpha-helical domain of each FABP would alter the rate and mechanism of transfer of FA from the chimeric proteins in comparison with those of the wild-type proteins. The fatty acid transfer properties of the FABP chimeras were examined using a fluorescence resonance transfer assay. The results showed a significant modification of the absolute rate of FA transfer from the chimeric proteins compared to that of the wild type, indicating that the slower rate of FA transfer observed for wild-type LFABP relative to that of wild-type IFABP is, in part, determined by the helical domain of the proteins. In addition to these quantitative changes, it was of great interest to observe that the apparent mechanism of FA transfer also changed when the alpha-helical domain was exchanged, with transfer from alphaLbetaIFABP occurring by aqueous diffusion and transfer from alphaIbetaLFABP occurring via protein-membrane collisional interactions. These results demonstrate that the alpha-helical region of LFABP is responsible for its diffusional mechanism of fatty acid transfer to membranes. PMID:15035630

  6. Rational design of alpha-helical tandem repeat proteins with closed architectures

    Science.gov (United States)

    Doyle, Lindsey; Hallinan, Jazmine; Bolduc, Jill; Parmeggiani, Fabio; Baker, David; Stoddard, Barry L.; Bradley, Philip

    2015-01-01

    Tandem repeat proteins, which are formed by repetition of modular units of protein sequence and structure, play important biological roles as macromolecular binding and scaffolding domains, enzymes, and building blocks for the assembly of fibrous materials1,2. The modular nature of repeat proteins enables the rapid construction and diversification of extended binding surfaces by duplication and recombination of simple building blocks3,4. The overall architecture of tandem repeat protein structures – which is dictated by the internal geometry and local packing of the repeat building blocks – is highly diverse, ranging from extended, super-helical folds that bind peptide, DNA, and RNA partners5–9, to closed and compact conformations with internal cavities suitable for small molecule binding and catalysis10. Here we report the development and validation of computational methods for de novo design of tandem repeat protein architectures driven purely by geometric criteria defining the inter-repeat geometry, without reference to the sequences and structures of existing repeat protein families. We have applied these methods to design a series of closed alpha-solenoid11 repeat structures (alpha-toroids) in which the inter-repeat packing geometry is constrained so as to juxtapose the N- and C-termini; several of these designed structures have been validated by X-ray crystallography. Unlike previous approaches to tandem repeat protein engineering12–20, our design procedure does not rely on template sequence or structural information taken from natural repeat proteins and hence can produce structures unlike those seen in nature. As an example, we have successfully designed and validated closed alpha-solenoid repeats with a left-handed helical architecture that – to our knowledge – is not yet present in the protein structure database21. PMID:26675735

  7. HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction

    Directory of Open Access Journals (Sweden)

    Changhui Yan

    2008-01-01

    Full Text Available α-helical transmembrane (TM proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA that can predict the topology of α-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in α-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method.

  8. Plasmodium vivax antigen discovery based on alpha-helical coiled coil protein motif.

    Directory of Open Access Journals (Sweden)

    Nora Céspedes

    Full Text Available Protein α-helical coiled coil structures that elicit antibody responses, which block critical functions of medically important microorganisms, represent a means for vaccine development. By using bioinformatics algorithms, a total of 50 antigens with α-helical coiled coil motifs orthologous to Plasmodium falciparum were identified in the P. vivax genome. The peptides identified in silico were chemically synthesized; circular dichroism studies indicated partial or high α-helical content. Antigenicity was evaluated using human sera samples from malaria-endemic areas of Colombia and Papua New Guinea. Eight of these fragments were selected and used to assess immunogenicity in BALB/c mice. ELISA assays indicated strong reactivity of serum samples from individuals residing in malaria-endemic regions and sera of immunized mice, with the α-helical coiled coil structures. In addition, ex vivo production of IFN-γ by murine mononuclear cells confirmed the immunogenicity of these structures and the presence of T-cell epitopes in the peptide sequences. Moreover, sera of mice immunized with four of the eight antigens recognized native proteins on blood-stage P. vivax parasites, and antigenic cross-reactivity with three of the peptides was observed when reacted with both the P. falciparum orthologous fragments and whole parasites. Results here point to the α-helical coiled coil peptides as possible P. vivax malaria vaccine candidates as were observed for P. falciparum. Fragments selected here warrant further study in humans and non-human primate models to assess their protective efficacy as single components or assembled as hybrid linear epitopes.

  9. Characterizing alpha helical properties of Ebola viral proteins as potential targets for inhibition of alpha-helix mediated protein-protein interactions.

    Science.gov (United States)

    Chakraborty, Sandeep; Rao, Basuthkar J; Asgeirsson, Bjarni; Dandekar, Abhaya

    2014-01-01

    Ebola, considered till recently as a rare and endemic disease, has dramatically transformed into a potentially global humanitarian crisis. The genome of Ebola, a member of the Filoviridae family, encodes seven proteins. Based on the recently implemented software (PAGAL) for analyzing the hydrophobicity and amphipathicity properties of alpha helices (AH) in proteins, we characterize the helices in the Ebola proteome. We demonstrate that AHs with characteristically unique features are involved in critical interactions with the host proteins. For example, the Ebola virus membrane fusion subunit, GP2, from the envelope glycoprotein ectodomain has an AH with a large hydrophobic moment. The neutralizing antibody (KZ52) derived from a human survivor of the 1995 Kikwit outbreak recognizes a protein epitope on this AH, emphasizing the critical nature of this secondary structure in the virulence of the Ebola virus. Our method ensures a comprehensive list of such `hotspots'. These helices probably are or can be the target of molecules designed to inhibit AH mediated protein-protein interactions. Further, by comparing the AHs in proteins of the related Marburg viruses, we are able to elicit subtle changes in the proteins that might render them ineffective to previously successful drugs. Such differences are difficult to identify by a simple sequence or structural alignment. Thus, analyzing AHs in the small Ebola proteome can aid rational design aimed at countering the `largest Ebola epidemic, affecting multiple countries in West Africa' ( http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/index.html). PMID:25717367

  10. Phase space trajectories and Lyapunov exponents in the dynamics of an alpha-helical protein lattice with intra- and inter-spine interactions

    Energy Technology Data Exchange (ETDEWEB)

    Angelin Jeba, K.; Latha, M. M., E-mail: lathaisaac@yahoo.com [Department of Physics, Women' s Christian College, Nagercoil 629 001 (India); Jain, Sudhir R. [Nuclear Physics Division, Bhabha Atomic Research Centre, Mumbai 400085 (India)

    2015-11-15

    The nonlinear dynamics of intra- and inter-spine interaction models of alpha-helical proteins is investigated by proposing a Hamiltonian using the first quantized operators. Hamilton's equations of motion are derived, and the dynamics is studied by constructing the trajectories and phase space plots in both cases. The phase space plots display a chaotic behaviour in the dynamics, which opens questions about the relationship between the chaos and exciton-exciton and exciton-phonon interactions. This is verified by plotting the Lyapunov characteristic exponent curves.

  11. Phase space trajectories and Lyapunov exponents in the dynamics of an alpha-helical protein lattice with intra- and inter-spine interactions

    International Nuclear Information System (INIS)

    The nonlinear dynamics of intra- and inter-spine interaction models of alpha-helical proteins is investigated by proposing a Hamiltonian using the first quantized operators. Hamilton's equations of motion are derived, and the dynamics is studied by constructing the trajectories and phase space plots in both cases. The phase space plots display a chaotic behaviour in the dynamics, which opens questions about the relationship between the chaos and exciton-exciton and exciton-phonon interactions. This is verified by plotting the Lyapunov characteristic exponent curves

  12. A fast method for the quantitative estimation of the distribution of hydrophobic and hydrophilic segments in alpha-helices of membrane proteins.

    Science.gov (United States)

    Luzhkov, V B; Surkov, N F

    2000-01-01

    The work presents a fast quantitative approach for estimating the orientations of hydrophilic and hydrophobic regions in the helical wheels of membrane-spanning alpha-helices of transmembrane proteins. The common hydropathy analysis provides an estimate of the integral hydrophobicity in a moving window which scans an amino acid sequence. The new parameter, orientation hydrophobicity, is based on the estimate of hydrophobicity of the angular segment that scans the helical wheel of a given amino acid sequence. The corresponding procedure involves the treatment of transmembrane helices as cylinders with equal surface elements for each amino acid residue. The orientation hydrophobicity, P(phi), phi = 0-360 degrees, of a helical cylinder is given as a sum of hydrophobicities of individual amino acids which are taken as the S-shaped functions of the angle between the centre of amino acid surface element and the centre of the segment. Non-zero contribution to P(phi) comes only from the amino acids belonging to the angular segment for a given angle phi. The size of the angular segment is related to the size of the channel pore. The amplitudes of amino acid S-functions are calibrated in the way that their maximum values (reached when the amino acid is completely exposed into the pore) are equal to the corresponding hydropathy index in the selected scale (here taken as Goldman-Engelman-Steitz hydropathy scale). The given procedure is applied in the studies of three ionic channels with well characterized three-dimensional structures where the channel pore is formed by a bundle of alpha-helices: cholera toxin B, nicotinic acetylcholine homopentameric alpha7 receptor, and phospholamban. The estimated maximum of hydrophilic properties at the helical wheels are in a good agreement with the spatial orientations of alpha-helices in the corresponding channel pores.

  13. Integrability and soliton solutions for an inhomogeneous generalized fourth-order nonlinear Schrödinger equation describing the inhomogeneous alpha helical proteins and Heisenberg ferromagnetic spin chains

    International Nuclear Information System (INIS)

    For describing the dynamics of alpha helical proteins with internal molecular excitations, nonlinear couplings between lattice vibrations and molecular excitations, and spin excitations in one-dimensional isotropic biquadratic Heisenberg ferromagnetic spin with the octupole–dipole interactions, we consider an inhomogeneous generalized fourth-order nonlinear Schrödinger equation. Based on the Ablowitz–Kaup–Newell–Segur system, infinitely many conservation laws for the equation are derived. Through the auxiliary function, bilinear forms and N-soliton solutions for the equation are obtained. Interactions of solitons are discussed by means of the asymptotic analysis. Effects of linear inhomogeneity on the interactions of solitons are also investigated graphically and analytically. Since the inhomogeneous coefficient of the equation h=α x+β, the soliton takes on the parabolic profile during the evolution. Soliton velocity is related to the parameter α, distance scale coefficient and biquadratic exchange coefficient, but has no relation with the parameter β. Soliton amplitude and width are only related to α. Soliton position is related to β

  14. Characterizing alpha helical properties of Ebola viral proteins as potential targets for inhibition of alpha-helix mediated protein-protein interactions [v3; ref status: indexed, http://f1000r.es/50u

    Directory of Open Access Journals (Sweden)

    Sandeep Chakraborty

    2015-01-01

    Full Text Available Ebola, considered till recently as a rare and endemic disease, has dramatically transformed into a potentially global humanitarian crisis. The genome of Ebola, a member of the Filoviridae family, encodes seven proteins. Based on the recently implemented software (PAGAL for analyzing the hydrophobicity and amphipathicity properties of alpha helices (AH in proteins, we characterize the helices in the Ebola proteome. We demonstrate that AHs with characteristically unique features are involved in critical interactions with the host proteins. For example, the Ebola virus membrane fusion subunit, GP2, from the envelope glycoprotein ectodomain has an AH with a large hydrophobic moment. The neutralizing antibody (KZ52 derived from a human survivor of the 1995 Kikwit outbreak recognizes a protein epitope on this AH, emphasizing the critical nature of this secondary structure in the virulence of the Ebola virus. Our method ensures a comprehensive list of such `hotspots'. These helices probably are or can be the target of molecules designed to inhibit AH mediated protein-protein interactions. Further, by comparing the AHs in proteins of the related Marburg viruses, we are able to elicit subtle changes in the proteins that might render them ineffective to previously successful drugs. Such differences are difficult to identify by a simple sequence or structural alignment. Thus, analyzing AHs in the small Ebola proteome can aid rational design aimed at countering the `largest Ebola epidemic, affecting multiple countries in West Africa' (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/index.html.

  15. The effect of charge reversal mutations in the alpha-helical region of liver fatty acid binding protein on the binding of fatty-acyl CoAs, lysophospholipids and bile acids.

    Science.gov (United States)

    Hagan, Robert M; Davies, Joanna K; Wilton, David C

    2002-10-01

    Liver fatty acid binding protein (LFABP) is unique among the various types of FABPs in that it can bind a variety of ligands in addition to fatty acids. LFABP is able to bind long chain fatty acids with a 2:1 stoichiometry and the crystal structure has identified two fatty acid binding sites in the binding cavity. The presumed primary site (site 1) involves the fatty acid binding with the carboxylate group buried in the cavity whereas the fatty acid at site 2 has the carboxylate group solvent-exposed within the ligand portal region and in the vicinity of alpha-helix II. The alpha-helical region contains three cationic residues, K20, K31, K33 and modelling studies suggest that K31 on alpha-helix II could make an electrostatic contribution to anionic ligands binding to site 2. The preparation of three charge reversal mutants of LFABP, K20E, K31E and K33E has allowed an investigation of the role of site 2 in ligand binding, particularly those ligands with a bulky anionic head group. The binding of oleoyl CoA, lysophosphatidic acid, lysophosphatidylcholine, lithocholic acid and taurolithocholate 3-sulphate to LFABP has been studied using the alpha-helical mutants. The results support the concept that such ligands bind at site 2 of LFABP where solvent exposure allows the accommodation of their bulky anionic group. PMID:12479568

  16. The integrity of the alpha-helical domain of intestinal fatty acid binding protein is essential for the collision-mediated transfer of fatty acids to phospholipid membranes.

    Science.gov (United States)

    Franchini, G R; Storch, J; Corsico, B

    2008-04-01

    Intestinal FABP (IFABP) and liver FABP (LFABP), homologous proteins expressed at high levels in intestinal absorptive cells, employ markedly different mechanisms of fatty acid transfer to acceptor model membranes. Transfer from IFABP occurs during protein-membrane collisional interactions, while for LFABP transfer occurs by diffusion through the aqueous phase. In addition, transfer from IFABP is markedly faster than from LFABP. The overall goal of this study was to further explore the structural differences between IFABP and LFABP which underlie their large functional differences in ligand transport. In particular, we addressed the role of the alphaI-helix domain in the unique transport properties of intestinal FABP. A chimeric protein was engineered with the 'body' (ligand binding domain) of IFABP and the alphaI-helix of LFABP (alpha(I)LbetaIFABP), and the fatty acid transfer properties of the chimeric FABP were examined using a fluorescence resonance energy transfer assay. The results showed a significant decrease in the absolute rate of FA transfer from alpha(I)LbetaIFABP compared to IFABP. The results indicate that the alphaI-helix is crucial for IFABP collisional FA transfer, and further indicate the participation of the alphaII-helix in the formation of a protein-membrane "collisional complex". Photo-crosslinking experiments with a photoactivable reagent demonstrated the direct interaction of IFABP with membranes and further support the importance of the alphaI helix of IFABP in its physical interaction with membranes. PMID:18284926

  17. Amphipathic alpha-helices and putative cholesterol binding domains of the influenza virus matrix M1 protein are crucial for virion structure organisation.

    Science.gov (United States)

    Tsfasman, Tatyana; Kost, Vladimir; Markushin, Stanislav; Lotte, Vera; Koptiaeva, Irina; Bogacheva, Elena; Baratova, Ludmila; Radyukhin, Victor

    2015-12-01

    The influenza virus matrix M1 protein is an amphitropic membrane-associated protein, forming the matrix layer immediately beneath the virus raft membrane, thereby ensuring the proper structure of the influenza virion. The objective of this study was to elucidate M1 fine structural characteristics, which determine amphitropic properties and raft membrane activities of the protein, via 3D in silico modelling with subsequent mutational analysis. Computer simulations suggest the amphipathic nature of the M1 α-helices and the existence of putative cholesterol binding (CRAC) motifs on six amphipathic α-helices. Our finding explains for the first time many features of this protein, particularly the amphitropic properties and raft/cholesterol binding potential. To verify these results, we generated mutants of the A/WSN/33 strain via reverse genetics. The M1 mutations included F32Y in the CRAC of α-helix 2, W45Y and W45F in the CRAC of α-helix 3, Y100S in the CRAC of α-helix 6, M128A and M128S in the CRAC of α-helix 8 and a double L103I/L130I mutation in both a putative cholesterol consensus motif and the nuclear localisation signal. All mutations resulted in viruses with unusual filamentous morphology. Previous experimental data regarding the morphology of M1-gene mutant influenza viruses can now be explained in structural terms and are consistent with the pivotal role of the CRAC-domains and amphipathic α-helices in M1-lipid interactions.

  18. Infrared and vibrational CD spectra of partially solvated alpha-helices: DFT-based simulations with explicit solvent.

    Science.gov (United States)

    Turner, David R; Kubelka, Jan

    2007-02-22

    Theoretical simulations are used to investigate the effects of aqueous solvent on the vibrational spectra of model alpha-helices, which are only partly exposed to solvent to mimic alpha-helices in proteins. Infrared absorption (IR) and vibrational circular dichroism (VCD) amide I' spectra for 15-amide alanine alpha-helices are simulated using density functional theory (DFT) calculations combined with the property transfer method. The solvent is modeled by explicit water molecules hydrogen bonded to the solvated amide groups. Simulated spectra for two partially solvated model alpha-helices, one corresponding to a more exposed and the other to a more buried structure, are compared to the fully solvated and unsolvated (gas phase) simulations. The dependence of the amide I spectra on the orientation of the partially solvated helix with respect to the solvent and effects of solvation on the amide I' of 13C isotopically substituted alpha-helices are also investigated. The partial exposure to solvent causes significant broadening of the amide I' bands due to differences in the vibrational frequencies of the explicitly solvated and unsolvated amide groups. The different degree of partial solvation is reflected primarily in the frequency shifts of the unsolvated (buried) amide group vibrations. Depending on which side of the alpha-helix is exposed to solvent, the simulated IR band-shapes exhibit significant changes, from broad and relatively featureless to distinctly split into two maxima. The simulated amide I' VCD band-shapes for the partially solvated alpha-helices parallel the broadening of the IR and exhibit more sign variation, but generally preserve the sign pattern characteristic of the alpha-helical structures and are much less dependent on the alpha-helix orientation with respect to the solvent. The simulated amide I' IR spectra for the model peptides with explicitly hydrogen-bonded water are consistent with the experimental data for small alpha-helical proteins

  19. Examining the Conservation of Kinks in Alpha Helices.

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    Eleanor C Law

    Full Text Available Kinks are a structural feature of alpha-helices and many are known to have functional roles. Kinks have previously tended to be defined in a binary fashion. In this paper we have deliberately moved towards defining them on a continuum, which given the unimodal distribution of kink angles is a better description. From this perspective, we examine the conservation of kinks in proteins. We find that kink angles are not generally a conserved property of homologs, pointing either to their not being functionally critical or to their function being related to conformational flexibility. In the latter case, the different structures of homologs are providing snapshots of different conformations. Sequence identity between homologous helices is informative in terms of kink conservation, but almost equally so is the sequence identity of residues in spatial proximity to the kink. In the specific case of proline, which is known to be prevalent in kinked helices, loss of a proline from a kinked helix often also results in the loss of a kink or reduction in its kink angle. We carried out a study of the seven transmembrane helices in the GPCR family and found that changes in kinks could be related both to subfamilies of GPCRs and also, in a particular subfamily, to the binding of agonists or antagonists. These results suggest conformational change upon receptor activation within the GPCR family. We also found correlation between kink angles in different helices, and the possibility of concerted motion could be investigated further by applying our method to molecular dynamics simulations. These observations reinforce the belief that helix kinks are key, functional, flexible points in structures.

  20. Improved prediction for N-termini of alpha-helices using empirical information.

    Science.gov (United States)

    Wilson, Claire L; Boardman, Paul E; Doig, Andrew J; Hubbard, Simon J

    2004-11-01

    The prediction of the secondary structure of proteins from their amino acid sequences remains a key component of many approaches to the protein folding problem. The most abundant form of regular secondary structure in proteins is the alpha-helix, in which specific residue preferences exist at the N-terminal locations. Propensities derived from these observed amino acid frequencies in the Protein Data Bank (PDB) database correlate well with experimental free energies measured for residues at different N-terminal positions in alanine-based peptides. We report a novel method to exploit this data to improve protein secondary structure prediction through identification of the correct N-terminal sequences in alpha-helices, based on existing popular methods for secondary structure prediction. With this algorithm, the number of correctly predicted alpha-helix start positions was improved from 30% to 38%, while the overall prediction accuracy (Q3) remained the same, using cross-validated testing. Although the algorithm was developed and tested on multiple sequence alignment-based secondary structure predictions, it was also able to improve the predictions of start locations by methods that use single sequences to make their predictions. Furthermore, the residue frequencies at N-terminal positions of the improved predictions better reflect those seen at the N-terminal positions of alpha-helices in proteins. This has implications for areas such as comparative modeling, where a more accurate prediction of the N-terminal regions of alpha-helices should benefit attempts to model adjacent loop regions. The algorithm is available as a Web tool, located at http://rocky.bms.umist.ac.uk/elephant. PMID:15340919

  1. The role of position a in determining the stability and oligomerization state of alpha-helical coiled coils: 20 amino acid stability coefficients in the hydrophobic core of proteins.

    OpenAIRE

    Wagschal, K.; Tripet, B; Lavigne, P; Mant, C.; Hodges, R. S.

    1999-01-01

    We describe here a systematic investigation into the role of position a in the hydrophobic core of a model coiled-coil protein in determining coiled-coil stability and oligomerization state. We employed a model coiled coil that allowed the formation of an extended three-stranded trimeric oligomerization state for some of the analogs; however, due to the presence of a Cys-Gly-Gly linker, unfolding occurred from the same two-stranded monomeric oligomerization state for all of the analogs. Denat...

  2. Efficiency of paramagnetism-based constraints to determine the spatial arrangement of {alpha}-helical secondary structure elements

    Energy Technology Data Exchange (ETDEWEB)

    Bertini, Ivano [University of Florence, CERM and Department of Chemistry (Italy)], E-mail: bertini@cerm.unifi.it; Longinetti, Marco [Dipartimento di Ingegneria Agraria e Forestale (Italy); Luchinat, Claudio; Parigi, Giacomo [University of Florence, CERM and Department of Agricultural Biotechnology (Italy); Sgheri, Luca [Istituto di Analisi Globale ed Applicazioni (CNR) (Italy)

    2002-02-15

    A computational approach has been developed to assess the power of paramagnetism-based backbone constraints with respect to the determination of the tertiary structure, once the secondary structure elements are known. This is part of the general assessment of paramagnetism-based constraints which are known to be relevant when used in conjunction with all classical constraints. The paramagnetism-based constraints here investigated are the pseudocontact shifts, the residual dipolar couplings due to self-orientation of the metalloprotein in high magnetic fields, and the cross correlation between dipolar relaxation and Curie relaxation. The relative constraints are generated by back-calculation from a known structure. The elements of secondary structure are supposed to be obtained from chemical shift index. The problem of the reciprocal orientation of the helices is addressed. It is shown that the correct fold can be obtained depending on the length of the {alpha}-helical stretches with respect to the length of the non helical segments connecting the {alpha}-helices. For example, the correct fold is straightforwardly obtained for the four-helix bundle protein cytochrome b{sub 562}, while the double EF-hand motif of calbindin D{sub 9k} is hardly obtained without ambiguity. In cases like calbindin D{sub 9k}, the availability of datasets from different metal ions is helpful, whereas less important is the location of the metal ion with respect to the secondary structure elements.

  3. Effects of hydrophobicity on the antifungal activity of alpha-helical antimicrobial peptides.

    NARCIS (Netherlands)

    Jiang, Z.; Kullberg, B.J.; Lee, H van der; Vasil, A.I.; Hale, J.D.; Mant, C.T.; Hancock, R.E.; Vasil, M.L.; Netea, M.G.; Hodges, R.S.

    2008-01-01

    We utilized a series of analogs of D-V13K (a 26-residue amphipathic alpha-helical antimicrobial peptide, denoted D1) to compare and contrast the role of hydrophobicity on antifungal and antibacterial activity to the results obtained previously with Pseudomonas aeruginosa strains. Antifungal activity

  4. Category theoretic analysis of hierarchical protein materials and social networks

    CERN Document Server

    Spivak, David I; Buehler, Markus J

    2011-01-01

    Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we review an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other ologs. We consider a simple example of an alpha-helical and an amyloid-like protein filament subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog f...

  5. Strength of integration of transmembrane alpha-helical peptides in lipid bilayers as determined by atomic force spectroscopy.

    Science.gov (United States)

    Ganchev, Dragomir N; Rijkers, Dirk T S; Snel, Margot M E; Killian, J Antoinette; de Kruijff, Ben

    2004-11-30

    In this study we address the stability of integration of proteins in membranes. Using dynamic atomic force spectroscopy, we measured the strength of incorporation of peptides in lipid bilayers. The peptides model the transmembrane parts of alpha-helical proteins and were studied in both ordered peptide-rich and unordered peptide-poor bilayers. Using gold-coated AFM tips and thiolated peptides, we were able to observe force events which are related to the removal of single peptide molecules out of the bilayer. The data demonstrate that the peptides are very stably integrated into the bilayer and that single barriers within the investigated region of loading rates resist their removal. The distance between the ground state and the barrier for peptide removal was found to be 0.75 +/- 0.15 nm in different systems. This distance falls within the thickness of the interfacial layer of the bilayer. We conclude that the bilayer interface region plays an important role in stably anchoring transmembrane proteins into membranes. PMID:15554706

  6. Alpha-helical hydrophobic polypeptides form proton-selective channels in lipid bilayers

    Science.gov (United States)

    Oliver, A. E.; Deamer, D. W.

    1994-01-01

    Proton translocation is important in membrane-mediated processes such as ATP-dependent proton pumps, ATP synthesis, bacteriorhodopsin, and cytochrome oxidase function. The fundamental mechanism, however, is poorly understood. To test the theoretical possibility that bundles of hydrophobic alpha-helices could provide a low energy pathway for ion translocation through the lipid bilayer, polyamino acids were incorporated into extruded liposomes and planar lipid membranes, and proton translocation was measured. Liposomes with incorporated long-chain poly-L-alanine or poly-L-leucine were found to have proton permeability coefficients 5 to 7 times greater than control liposomes, whereas short-chain polyamino acids had relatively little effect. Potassium permeability was not increased markedly by any of the polyamino acids tested. Analytical thin layer chromatography measurements of lipid content and a fluorescamine assay for amino acids showed that there were approximately 135 polyleucine or 65 polyalanine molecules associated with each liposome. Fourier transform infrared spectroscopy indicated that a major fraction of the long-chain hydrophobic peptides existed in an alpha-helical conformation. Single-channel recording in both 0.1 N HCl and 0.1 M KCl was also used to determine whether proton-conducting channels formed in planar lipid membranes (phosphatidylcholine/phosphatidylethanolamine, 1:1). Poly-L-leucine and poly-L-alanine in HCl caused a 10- to 30-fold increase in frequency of conductive events compared to that seen in KCl or by the other polyamino acids in either solution. This finding correlates well with the liposome observations in which these two polyamino acids caused the largest increase in membrane proton permeability but had little effect on potassium permeability. Poly-L-leucine was considerably more conductive than poly-L-alanine due primarily to larger event amplitudes and, to a lesser extent, a higher event frequency. Poly-L-leucine caused two

  7. Molecular organization in striated domains induced by transmembrane alpha-helical peptides in dipalmitoyl phosphatidylcholine bilayers.

    Science.gov (United States)

    Sparr, Emma; Ganchev, Dragomir N; Snel, Margot M E; Ridder, Anja N J A; Kroon-Batenburg, Loes M J; Chupin, Vladimir; Rijkers, Dirk T S; Killian, J Antoinette; de Kruijff, Ben

    2005-01-11

    Transmembrane (TM) alpha-helical peptides with neutral flanking residues such as tryptophan form highly ordered striated domains when incorporated in gel-state 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) bilayers and inspected by atomic force microscopy (AFM) (1). In this study, we analyze the molecular organization of these striated domains using AFM, photo-cross-linking, fluorescence spectroscopy, nuclear magnetic resonance (NMR), and X-ray diffraction techniques on different functionalized TM peptides. The results demonstrate that the striated domains consist of linear arrays of single TM peptides with a dominantly antiparallel organization in which the peptides interact with each other and with lipids. The peptide arrays are regularly spaced by +/-8.5 nm and are separated by somewhat perturbed gel-state lipids with hexagonally organized acyl chains, which have lost their tilt. This system provides an example of how domains of peptides and lipids can be formed in membranes as a result of a combination of specific peptide-peptide and peptide-lipid interactions. PMID:15628840

  8. TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles.

    Science.gov (United States)

    Ou, Yu-Yen; Gromiha, M Michael; Chen, Shu-An; Suwa, Makiko

    2008-06-01

    Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. We have developed a method based on radial basis function networks and position specific scoring matrix (PSSM) profiles generated by PSI-BLAST and non-redundant protein database. Our approach with PSSM profiles has correctly predicted the OMPs with a cross-validated accuracy of 96.4% in a set of 1251 proteins, which contain 206 OMPs, 667 globular proteins and 378 alpha-helical inner membrane proteins. Furthermore, we applied our method on a dataset containing 114 OMPs, 187 TMH proteins and 195 globular proteins obtained with less than 20% sequence identity and obtained the cross-validated accuracy of 95%. This accuracy of discriminating OMPs is higher than other methods in the literature and our method could be used as an effective tool for dissecting OMPs from genomic sequences. We have developed a prediction server, TMBETADISC-RBF, which is available at http://rbf.bioinfo.tw/~sachen/OMP.html.

  9. Design of a minimal protein oligomerization domain by a structural approach.

    OpenAIRE

    Burkhard, P.; Meier, M; Lustig, A.

    2000-01-01

    Because of the simplicity and regularity of the alpha-helical coiled coil relative to other structural motifs, it can be conveniently used to clarify the molecular interactions responsible for protein folding and stability. Here we describe the de novo design and characterization of a two heptad-repeat peptide stabilized by a complex network of inter- and intrahelical salt bridges. Circular dichroism spectroscopy and analytical ultracentrifugation show that this peptide is highly alpha-helica...

  10. Assembly and Structure of alpha-helical Peptide Films on Hydrophobic Fluorocarbon Surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Weidner, T.; Samual, N; McCrea, K; Gamble, L; Ward, R; Castner, D

    2010-01-01

    The structure, orientation, and formation of amphiphilic {alpha}-helix model peptide films on fluorocarbon surfaces has been monitored with sum frequency generation (SFG) vibrational spectroscopy, near-edge x-ray absorption fine structure (NEXAFS) spectroscopy, and x-ray photoelectron spectroscopy (XPS). The {alpha}-helix peptide is a 14-mer of hydrophilic lysine and hydrophobic leucine residues with a hydrophobic periodicity of 3.5. This periodicity yields a rigid amphiphilic peptide with leucine and lysine side chains located on opposite sides. XPS composition analysis confirms the formation of a peptide film that covers about 75% of the surface. NEXAFS data are consistent with chemically intact adsorption of the peptides. A weak linear dichroism of the amide {pi}* is likely due to the broad distribution of amide bond orientations inherent to the {alpha}-helical secondary structure. SFG spectra exhibit strong peaks near 2865 and 2935 cm{sup -1} related to aligned leucine side chains interacting with the hydrophobic surface. Water modes near 3200 and 3400 cm{sup -1} indicate ordering of water molecules in the adsorbed-peptide fluorocarbon surface interfacial region. Amide I peaks observed near 1655 cm{sup -1} confirm that the secondary structure is preserved in the adsorbed peptide. A kinetic study of the film formation process using XPS and SFG showed rapid adsorption of the peptides followed by a longer assembly process. Peptide SFG spectra taken at the air-buffer interface showed features related to well-ordered peptide films. Moving samples through the buffer surface led to the transfer of ordered peptide films onto the substrates.

  11. Introduction of all-hydrocarbon i,i+3 staples into alpha-helices via ring-closing olefin metathesis.

    Science.gov (United States)

    Kim, Young-Woo; Kutchukian, Peter S; Verdine, Gregory L

    2010-07-01

    The introduction of all-hydrocarbon i,i+3 staples into alpha-helical peptide scaffolds via ring-closing olefin metathesis (RCM) between two alpha-methyl,alpha-pentenylglycine residues incorporated at i and i+3 positions, which lie on the same face of the helix, has been investigated. The reactions were found to be highly dependent upon the side-chain stereochemistry of the amino acids undergoing RCM. The i,i+3 stapling system established here provides a potentially useful alternative to the well-established i,i+4 stapling system now in widespread use.

  12. Detection of alpha-rod protein repeats using a neural network and application to huntingtin.

    Directory of Open Access Journals (Sweden)

    Gareth A Palidwor

    2009-03-01

    Full Text Available A growing number of solved protein structures display an elongated structural domain, denoted here as alpha-rod, composed of stacked pairs of anti-parallel alpha-helices. Alpha-rods are flexible and expose a large surface, which makes them suitable for protein interaction. Although most likely originating by tandem duplication of a two-helix unit, their detection using sequence similarity between repeats is poor. Here, we show that alpha-rod repeats can be detected using a neural network. The network detects more repeats than are identified by domain databases using multiple profiles, with a low level of false positives (<10%. We identify alpha-rod repeats in approximately 0.4% of proteins in eukaryotic genomes. We then investigate the results for all human proteins, identifying alpha-rod repeats for the first time in six protein families, including proteins STAG1-3, SERAC1, and PSMD1-2 & 5. We also characterize a short version of these repeats in eight protein families of Archaeal, Bacterial, and Fungal species. Finally, we demonstrate the utility of these predictions in directing experimental work to demarcate three alpha-rods in huntingtin, a protein mutated in Huntington's disease. Using yeast two hybrid analysis and an immunoprecipitation technique, we show that the huntingtin fragments containing alpha-rods associate with each other. This is the first definition of domains in huntingtin and the first validation of predicted interactions between fragments of huntingtin, which sets up directions toward functional characterization of this protein. An implementation of the repeat detection algorithm is available as a Web server with a simple graphical output: http://www.ogic.ca/projects/ard. This can be further visualized using BiasViz, a graphic tool for representation of multiple sequence alignments.

  13. Proteins Are the Body's Worker Molecules

    Science.gov (United States)

    ... to Top Parts of Some Proteins Fold Into Corkscrews A mix of alpha helices and beta sheets. ... shapes. Where a protein chain curves into a corkscrew, that section is called an alpha helix. Where ...

  14. Spectral affinity in protein networks

    OpenAIRE

    Teng Shang-Hua; Voevodski Konstantin; Xia Yu

    2009-01-01

    Abstract Background Protein-protein interaction (PPI) networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise...

  15. Combining Single-Molecule Optical Trapping and Small-Angle X-Ray Scattering Measurements to Compute the Persistence Length of a Protein ER/K alpha-Helix

    DEFF Research Database (Denmark)

    Sivaramakrishnan, S.; Sung, J.; Ali, M.;

    2009-01-01

    A relatively unknown protein structure motif forms stable isolated single alpha-helices, termed ER/K alpha-helices, in a wide variety of proteins and has been shown to be essential for the function of some molecular motors. The flexibility of the ER/K alpha-helix determines whether it behaves as a...

  16. Consequences of non-uniformity in the stoichiometry of component fractions within one and two loops models of alpha-helical peptides

    Science.gov (United States)

    Atoms in biomolecular structures like alpha helices contain an array of distances and angles which include abundant multiple patterns of redundancies. Thus all peptides backbones contain the three atom sequence N-C*C, whereas the repeating set of a four atom sequences (N-C*C-N, C*-C-N-C*, and C-N-C...

  17. Alpha-Synuclein Binds to the Inner Membrane of Mitochondria in an alpha-Helical Conformation

    NARCIS (Netherlands)

    Robotta, M.; Gerding, H.R.; Vogel, A.; Hauser, K.; Schildknecht, S.; Karreman, C.; Leist, M.; Subramaniam, V.; Drescher, M.

    2014-01-01

    The human alpha-Synuclein (alphaS) protein is of significant interest because of its association with Parkinson's disease and related neurodegenerative disorders. The intrinsically disordered protein (140 amino acids) is characterized by the absence of a well-defined structure in solution. It displa

  18. Functional and genomic analyses of alpha-solenoid proteins.

    Directory of Open Access Journals (Sweden)

    David Fournier

    Full Text Available Alpha-solenoids are flexible protein structural domains formed by ensembles of alpha-helical repeats (Armadillo and HEAT repeats among others. While homology can be used to detect many of these repeats, some alpha-solenoids have very little sequence homology to proteins of known structure and we expect that many remain undetected. We previously developed a method for detection of alpha-helical repeats based on a neural network trained on a dataset of protein structures. Here we improved the detection algorithm and updated the training dataset using recently solved structures of alpha-solenoids. Unexpectedly, we identified occurrences of alpha-solenoids in solved protein structures that escaped attention, for example within the core of the catalytic subunit of PI3KC. Our results expand the current set of known alpha-solenoids. Application of our tool to the protein universe allowed us to detect their significant enrichment in proteins interacting with many proteins, confirming that alpha-solenoids are generally involved in protein-protein interactions. We then studied the taxonomic distribution of alpha-solenoids to discuss an evolutionary scenario for the emergence of this type of domain, speculating that alpha-solenoids have emerged in multiple taxa in independent events by convergent evolution. We observe a higher rate of alpha-solenoids in eukaryotic genomes and in some prokaryotic families, such as Cyanobacteria and Planctomycetes, which could be associated to increased cellular complexity. The method is available at http://cbdm.mdc-berlin.de/~ard2/.

  19. Spectral affinity in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-11-01

    Full Text Available Abstract Background Protein-protein interaction (PPI networks enable us to better understand the functional organization of the proteome. We can learn a lot about a particular protein by querying its neighborhood in a PPI network to find proteins with similar function. A spectral approach that considers random walks between nodes of interest is particularly useful in evaluating closeness in PPI networks. Spectral measures of closeness are more robust to noise in the data and are more precise than simpler methods based on edge density and shortest path length. Results We develop a novel affinity measure for pairs of proteins in PPI networks, which uses personalized PageRank, a random walk based method used in context-sensitive search on the Web. Our measure of closeness, which we call PageRank Affinity, is proportional to the number of times the smaller-degree protein is visited in a random walk that restarts at the larger-degree protein. PageRank considers paths of all lengths in a network, therefore PageRank Affinity is a precise measure that is robust to noise in the data. PageRank Affinity is also provably related to cluster co-membership, making it a meaningful measure. In our experiments on protein networks we find that our measure is better at predicting co-complex membership and finding functionally related proteins than other commonly used measures of closeness. Moreover, our experiments indicate that PageRank Affinity is very resilient to noise in the network. In addition, based on our method we build a tool that quickly finds nodes closest to a queried protein in any protein network, and easily scales to much larger biological networks. Conclusion We define a meaningful way to assess the closeness of two proteins in a PPI network, and show that our closeness measure is more biologically significant than other commonly used methods. We also develop a tool, accessible at http://xialab.bu.edu/resources/pnns, that allows the user to

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

    OpenAIRE

    Sayed Mohammad Ebrahim Sahraeian; Byung-Jun Yoon

    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 ca...

  1. Controlling allosteric networks in proteins

    Science.gov (United States)

    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.

  2. Novel delivery system for T-oligo using a nanocomplex formed with an alpha helical peptide for melanoma therapy

    Directory of Open Access Journals (Sweden)

    Uppada SB

    2013-12-01

    Full Text Available Srijayaprakash B Uppada,1,* Terrianne Erickson,1 Luke Wojdyla,1 David N Moravec,1 Ziyuan Song,2 Jianjun Cheng,2 Neelu Puri1,* 1Department of Biomedical Sciences, University of Illinois College of Medicine at Rockford, Rockford, 2Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA *These authors contributed equally to this work Abstract: Oligonucleotides homologous to 3'-telomere overhang (T-oligos trigger inherent telomere-based DNA damage responses mediated by p53 and/or ATM and induce senescence or apoptosis in various cancerous cells. However, T-oligo has limited stability in vivo due to serum and intracellular nucleases. To develop T-oligo as an innovative, effective therapeutic drug and to understand its mechanism of action, we investigated the antitumor effects of T-oligo or T-oligo complexed with a novel cationic alpha helical peptide, PVBLG-8 (PVBLG, in a p53 null melanoma cell line both in vitro and in vivo. The uptake of T-oligo by MM-AN cells was confirmed by immunofluorescence, and fluorescence-activated cell sorting analysis indicated that the T-oligo-PVBLG nanocomplex increased uptake by 15-fold. In vitro results showed a 3-fold increase in MM-AN cell growth inhibition by the T-oligo-PVBLG nanocomplex compared with T-oligo alone. Treatment of preformed tumors in immunodeficient mice with the T-oligo-PVBLG nanocomplex resulted in a 3-fold reduction in tumor volume compared with T-oligo alone. This reduction in tumor volume was associated with decreased vascular endothelial growth factor expression and induction of thrombospondin-1 expression and apoptosis. Moreover, T-oligo treatment downregulated procaspase-3 and procaspase-7 and increased catalytic activity of caspase-3 by 4-fold in MM-AN cells. Furthermore, T-oligo induced a 10-fold increase of senescence and upregulated the melanoma tumor-associated antigens MART-1, tyrosinase, and thrombospondin-1 in MM-AN cells, which

  3. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  4. Protein evolution on a human signaling network

    OpenAIRE

    Purisima Enrico O; Cui Qinghua; Wang Edwin

    2009-01-01

    Abstract Background The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling networ...

  5. Toxoplasma gondii: Biochemical and biophysical characterization of recombinant soluble dense granule proteins GRA2 and GRA6

    Energy Technology Data Exchange (ETDEWEB)

    Bittame, Amina [CNRS, UMR 5163, 38042 Grenoble (France); Université Grenoble Alpes, 38042 Grenoble (France); Effantin, Grégory [Université Grenoble Alpes, Institut de Biologie Structurale (IBS), 38044 Grenoble (France); CNRS, IBS, 38044 Grenoble (France); CEA, IBS, 38044 Grenoble (France); Unit for Virus Host-Cell Interactions (UVHCI), UMI 3265 (UJF-EMBL-CNRS), 38027 Grenoble (France); Pètre, Graciane; Ruffiot, Pauline; Travier, Laetitia [CNRS, UMR 5163, 38042 Grenoble (France); Université Grenoble Alpes, 38042 Grenoble (France); Schoehn, Guy; Weissenhorn, Winfried [Université Grenoble Alpes, Institut de Biologie Structurale (IBS), 38044 Grenoble (France); CNRS, IBS, 38044 Grenoble (France); CEA, IBS, 38044 Grenoble (France); Unit for Virus Host-Cell Interactions (UVHCI), UMI 3265 (UJF-EMBL-CNRS), 38027 Grenoble (France); Cesbron-Delauw, Marie-France; Gagnon, Jean [CNRS, UMR 5163, 38042 Grenoble (France); Université Grenoble Alpes, 38042 Grenoble (France); Mercier, Corinne, E-mail: corinne.mercier@ujf-grenoble.fr [CNRS, UMR 5163, 38042 Grenoble (France); Université Grenoble Alpes, 38042 Grenoble (France)

    2015-03-27

    The most prominent structural feature of the parasitophorous vacuole (PV) in which the intracellular parasite Toxoplasma gondii proliferates is a membranous nanotubular network (MNN), which interconnects the parasites and the PV membrane. The MNN function remains unclear. The GRA2 and GRA6 proteins secreted from the parasite dense granules into the PV have been implicated in the MNN biogenesis. Amphipathic alpha-helices (AAHs) predicted in GRA2 and an alpha-helical hydrophobic domain predicted in GRA6 have been proposed to be responsible for their membrane association, thereby potentially molding the MMN in its structure. Here we report an analysis of the recombinant proteins (expressed in detergent-free conditions) by circular dichroism, which showed that full length GRA2 displays an alpha-helical secondary structure while recombinant GRA6 and GRA2 truncated of its AAHs are mainly random coiled. Dynamic light scattering and transmission electron microscopy showed that recombinant GRA6 and truncated GRA2 constitute a homogenous population of small particles (6–8 nm in diameter) while recombinant GRA2 corresponds to 2 populations of particles (∼8–15 nm and up to 40 nm in diameter, respectively). The unusual properties of GRA2 due to its AAHs are discussed. - Highlights: • Toxoplasma gondii: soluble GRA2 forms 2 populations of particles. • T. gondii: the dense granule protein GRA2 folds intrinsically as an alpha-helix. • T. gondii: monomeric soluble GRA6 forms particles of 6–8 nm in diameter. • T. gondii: monomeric soluble GRA6 is random coiled. • Unusual biophysical properties of the dense granule protein GRA2 from T. gondii.

  6. Preferential attachment in the protein network evolution

    OpenAIRE

    Eisenberg, Eli; Levanon, Erez Y.

    2003-01-01

    The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields this universal topology. However, it is not clear that the model assumptions hold for the protein interaction network. Using a cross genome comparison we show that (a) the older a protein, the better connected it is, and (b) The number of interactions a prote...

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

    OpenAIRE

    Yang Zhihao; Lin Hongfei; Xu Bo

    2011-01-01

    Abstract 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 metho...

  8. Deducing topology of protein-protein interaction networks from experimentally measured sub-networks

    Directory of Open Access Journals (Sweden)

    MacLellan W Robb

    2008-07-01

    Full Text Available Abstract Background Protein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear. Results By analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks. Conclusion The topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network.

  9. Hub Promiscuity in Protein-Protein Interaction Networks

    OpenAIRE

    Haruki Nakamura; Kengo Kinoshita; Ashwini Patil

    2010-01-01

    Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ...

  10. Geometric De-noising of Protein-Protein Interaction Networks

    OpenAIRE

    Kuchaiev, Oleksii; Rasajski, Marija; Higham, Desmond J.; Przul, Natasa; Przytycka, Teresa Maria

    2009-01-01

    Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, i...

  11. Protein interaction networks from literature mining

    Science.gov (United States)

    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. Ontology integration to identify protein complex in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Yang Zhihao

    2011-10-01

    Full Text Available Abstract 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.

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

    Science.gov (United States)

    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

  14. Characterization of dry globular proteins and protein fibrils by synchrotron radiation vacuum UV circular dichroism

    DEFF Research Database (Denmark)

    Nesgaard, Lise W.; Hoffmann, Søren Vrønning; Andersen, Christian Beyschau;

    2008-01-01

    Circular dichroism using synchrotron radiation (SRCD) can extend the spectral range down to approximately 130 nm for dry proteins, potentially providing new structural information. Using a selection of dried model proteins, including alpha-helical, beta-sheet, and mixed-structure proteins, we obs...

  15. Identifying hubs in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Ravishankar R Vallabhajosyula

    Full Text Available BACKGROUND: In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs, it is common to define an ad hoc degree scale that defines "hub" proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust. METHODOLOGY: In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction. CONCLUSIONS: We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.

  16. The structure of aquaporin-1 at 4.5-A resolution reveals short alpha-helices in the center of the monomer.

    Science.gov (United States)

    Mitsuoka, K; Murata, K; Walz, T; Hirai, T; Agre, P; Heymann, J B; Engel, A; Fujiyoshi, Y

    1999-12-01

    Aquaporin-1 is a water channel found in mammalian red blood cells that is responsible for high water permeability of its membrane. Our electron crystallographic analysis of the three-dimensional structure of aquaporin-1 at 4.5-A resolution confirms the previous finding that each subunit consists of a right-handed bundle of six highly tilted transmembrane helices that surround a central X-shaped structure. In our new potential map, the rod-like densities for the transmembrane helices show helically arranged protrusions, indicating the positions of side chains. Thus, in addition to the six transmembrane helices, observation of helically arranged side-chain densities allowed the identification of two short alpha-helices representing the two branches of the central X-shaped structure that extend to the extracellular and cytoplasmic membrane surfaces. The other two branches are believed to be loops connecting the short alpha-helix to a neighboring transmembrane helix. A pore found close to the center of the aquaporin-1 monomer is suggested to be the course of water flow with implications for the water selectivity. PMID:10600556

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

    Directory of Open Access Journals (Sweden)

    Luciani Davide

    2012-05-01

    Full Text Available Abstract 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.

  18. NAPS: Network Analysis of Protein Structures.

    Science.gov (United States)

    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. Light-activated DNA binding in a designed allosteric protein

    Energy Technology Data Exchange (ETDEWEB)

    Strickland, Devin; Moffat, Keith; Sosnick, Tobin R. (UC)

    2008-09-03

    An understanding of how allostery, the conformational coupling of distant functional sites, arises in highly evolvable systems is of considerable interest in areas ranging from cell biology to protein design and signaling networks. We reasoned that the rigidity and defined geometry of an {alpha}-helical domain linker would make it effective as a conduit for allosteric signals. To test this idea, we rationally designed 12 fusions between the naturally photoactive LOV2 domain from Avena sativa phototropin 1 and the Escherichia coli trp repressor. When illuminated, one of the fusions selectively binds operator DNA and protects it from nuclease digestion. The ready success of our rational design strategy suggests that the helical 'allosteric lever arm' is a general scheme for coupling the function of two proteins.

  20. Finding local communities in protein networks

    OpenAIRE

    Teng Shang-Hua; Voevodski Konstantin; Xia Yu

    2009-01-01

    Abstract Background Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein ne...

  1. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  2. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    OpenAIRE

    Xiaomin Wang; Zhengzhi Wang; Jun Ye

    2011-01-01

    With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly use...

  3. The centrality of cancer proteins in human protein-protein interaction network: a revisit.

    Science.gov (United States)

    Xiong, Wei; Xie, Luyu; Zhou, Shuigeng; Liu, Hui; Guan, Jihong

    2014-01-01

    Topological analysis of protein-protein interaction (PPI) networks has been widely applied to the investigation on cancer mechanisms. However, there is still a debate on whether cancer proteins exhibit more topological centrality compared to the other proteins in the human PPI network. To resolve this debate, we first identified four sets of human proteins, and then mapped these proteins into the yeast PPI network by homologous genes. Finally, we compared these proteins' properties in human and yeast PPI networks. Experiments over two real datasets demonstrated that cancer proteins tend to have higher degree and smaller clustering coefficient than non-cancer proteins. Experimental results also validated that cancer proteins have larger betweenness centrality compared to the other proteins on the STRING dataset. However, on the BioGRID dataset, the average betweenness centrality of cancer proteins is larger than that of disease and control proteins, but smaller than that of essential proteins. PMID:24878726

  4. The effects of regularly spaced glutamine substitutions on alpha-helical peptide structures: A DFT/ONIOM study

    Science.gov (United States)

    Roy, Dipankar; Dannenberg, J. J.

    2011-08-01

    The side-chains of the residues of glutamine (Q) and asparagine (N) contain amide groups. These can H-bond to each other in patterns similar to those of the backbone amides in α-helices. We show that mutating multiple Q's for alanines (A's) in a polyalanine helix stabilizes the helical structure, while similar mutations with multiple N's do not. We suggest that modification of peptides by incorporating Q's in such positions can make more robust helices that can be used to test the effects of secondary structures in biochemical experiments linked to proteins with variable structures such as tau and α-synuclein.

  5. Secondary structure prediction of protein constructs using random incremental truncation and vacuum-ultraviolet CD spectroscopy

    CERN Document Server

    Pukáncsik, M; Matsuo, K; Gekko, K; Hart, D; Kézsmárki, I; Vértessy, B G

    2014-01-01

    A novel uracil-DNA degrading protein factor (termed UDE) was identified in Drosophila melanogaster with no significant structural and functional homology to other uracil-DNA binding or processing factors. Determination of the 3D structure of UDE will be a true breakthrough in description of the molecular mechanism of action of UDE catalysis, as well as in general uracil-recognition and nuclease action. The revolutionary ESPRIT technology was applied to the novel protein UDE to overcome problems in identifying soluble expressing constructs given the absence of precise information on domain content and arrangement. Nine specimen from the created numerous truncated constructs of UDE were choosen to dechiper structural and functional relationships. VUVCD with neural network was performed to define the secondary structure content and location of UDE and its truncated variants. The quantitative analysis demonstrated exclusive {\\alpha}-helical content for the full-length protein, which is preserved in the truncated ...

  6. Reconstruction of human protein interolog network using evolutionary conserved network

    Directory of Open Access Journals (Sweden)

    Lin Chung-Yen

    2007-05-01

    Full Text Available Abstract Background The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog. This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. Results This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast. Conclusion Evaluation results of the proposed method using functional keyword and Gene Ontology (GO annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.

  7. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

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

    Science.gov (United States)

    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.

  9. Multiplexed imaging of intracellular protein networks.

    Science.gov (United States)

    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

  10. Multifunctional proteins revealed by overlapping clustering in protein interaction network

    OpenAIRE

    Becker, Emmanuelle; Robisson, Benoît; Chapple, Charles E.; Guénoche, Alain; Brun, Christine

    2011-01-01

    Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters. Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overla...

  11. Geometric de-noising of protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Oleksii Kuchaiev

    2009-08-01

    Full Text Available Understanding complex networks of protein-protein interactions (PPIs is one of the foremost challenges of the post-genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H, tandem affinity purification (TAP and other high-throughput methods for protein-protein interaction (PPI detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise.We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.

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

    Science.gov (United States)

    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.

  13. Inferring protein function by domain context similarities in protein-protein interaction networks

    OpenAIRE

    Sun Zhirong; Liu Ke; Chen Hu; Zhang Song

    2009-01-01

    Abstract Background Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to pre...

  14. Network compression as a quality measure for protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Loic Royer

    Full Text Available 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.

  15. Modelling Protein Dynamics on the Microsecond Time Scale

    DEFF Research Database (Denmark)

    Siuda, Iwona Anna

    in atomistic simulations. During her PhD studies, Iwona Siuda used MARTINI CG models to study the dynamics of different globular and membrane proteins. In several cases, the MARTINI model was sufficient to study conformational changes of small, purely alpha-helical proteins. However, in studies of larger...

  16. Data management of protein interaction networks

    CERN Document Server

    Cannataro, Mario

    2012-01-01

    Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate

  17. Finding local communities in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-09-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

  18. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    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

  19. Detecting overlapping protein complexes in protein-protein interaction networks

    OpenAIRE

    Nepusz, Tamás; Yu, Haiyuan; Paccanaro, Alberto

    2012-01-01

    We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a...

  20. An Algorithm for Finding Functional Modules and Protein Complexes in Protein-Protein Interaction Networks

    OpenAIRE

    Guangyu Cui; Yu Chen; De-Shuang Huang; Kyungsook Han

    2008-01-01

    Biological processes are often performed by a group of proteins rather than by individual proteins, and proteins in a same biological group form a densely connected subgraph in a protein-protein interaction network. Therefore, finding a densely connected subgraph provides useful information to predict the function or protein complex of uncharacterized proteins in the highly connected subgraph. We have developed an efficient algorithm and program for finding cliques and near-cliques in a prote...

  1. Slow alpha helix formation during folding of a membrane protein.

    Science.gov (United States)

    Riley, M L; Wallace, B A; Flitsch, S L; Booth, P J

    1997-01-01

    Very little is known about the folding of proteins within biological membranes. A "two-stage" model has been proposed on thermodynamic grounds for the folding of alpha helical, integral membrane proteins, the first stage of which involves formation of transmembrane alpha helices that are proposed to behave as autonomous folding domains. Here, we investigate alpha helix formation in bacteriorhodopsin and present a time-resolved circular dichroism study of the slow in vitro folding of this protein. We show that, although some of the protein's alpha helices form early, a significant part of the protein's secondary structure appears to form late in the folding process. Over 30 amino acids, equivalent to at least one of bacteriorhodopsin's seven transmembrane segments, slowly fold from disordered structures to alpha helices with an apparent rate constant of about 0.012 s-1 at pH 6 or 0.0077 s-1 at pH 8. This is a rate-limiting step in protein folding, which is dependent on the pH and the composition of the lipid bilayer. PMID:8993333

  2. Analysis of protein folds using protein contact networks

    Indian Academy of Sciences (India)

    Pankaj Barah; Somdatta Sinha

    2008-08-01

    Proteins are important biomolecules, which perform diverse structural and functional roles in living systems. Starting from a linear chain of amino acids, proteins fold to different secondary structures, which then fold through short- and long-range interactions to give rise to the final three-dimensional shapes useful to carry out the biophysical and biochemical functions. Proteins are defined as having a common `fold' if they have major secondary structural elements with same topological connections. It is known that folding mechanisms are largely determined by a protein's topology rather than its interatomic interactions. The native state protein structures can, thus, be modelled, using a graph-theoretical approach, as coarse-grained networks of amino acid residues as `nodes' and the inter-residue interactions/contacts as `links'. Using the network representation of protein structures and their 2D contact maps, we have identified the conserved contact patterns (groups of contacts) representing two typical folds – the EF-hand and the ubiquitin-like folds. Our results suggest that this direct and computationally simple methodology can be used to infer about the presence of specific folds from the protein's contact map alone.

  3. Extracting protein regulatory networks with graphical models.

    Science.gov (United States)

    Grzegorczyk, Marco

    2007-09-01

    During the last decade the development of high-throughput biotechnologies has resulted in the production of exponentially expanding quantities of biological data, such as genomic and proteomic expression data. One fundamental problem in systems biology is to learn the architecture of biochemical pathways and regulatory networks in an inferential way from such postgenomic data. Along with the increasing amount of available data, a lot of novel statistical methods have been developed and proposed in the literature. This article gives a non-mathematical overview of three widely used reverse engineering methods, namely relevance networks, graphical Gaussian models, and Bayesian networks, whereby the focus is on their relative merits and shortcomings. In addition the reverse engineering results of these graphical methods on cytometric protein data from the RAF-signalling network are cross-compared via AUROC scatter plots. PMID:17893851

  4. Probabilistic methods for predicting protein functions in protein-protein interaction networks

    OpenAIRE

    Best, Christoph; Zimmer, Ralf; Apostolakis, Joannis

    2005-01-01

    We discuss probabilistic methods for predicting protein functions from protein-protein interaction networks. Previous work based on Markov Randon Fields is extended and compared to a general machine-learning theoretic approach. Using actual protein interaction networks for yeast from the MIPS database and GO-SLIM function assignments, we compare the predictions of the different probabilistic methods and of a standard support vector machine. It turns out that, with the currently available netw...

  5. Modularity detection in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Narayanan Tejaswini

    2011-12-01

    Full Text Available Abstract Background Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. Results We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. Conclusions We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the

  6. HRTEM in protein crystallography

    International Nuclear Information System (INIS)

    Electron microscopy/diffraction (ED/D) using spot-scan and low-dose imaging has been successfully applied to investigate microcrystals of an alpha-helical coiled-coil protein extracted from ootheca of the praying mantis. Fourier transforms of the images show resolution out to 4 Angstroems and can be used to phase the corresponding ED data which shows reflections out to 2 Aangstroems. 5 refs., 3 figs

  7. The Effects of Network Neighbours on Protein Evolution

    OpenAIRE

    Guang-Zhong Wang; Martin J Lercher

    2011-01-01

    Interacting proteins may often experience similar selection pressures. Thus, we may expect that neighbouring proteins in biological interaction networks evolve at similar rates. This has been previously shown for protein-protein interaction networks. Similarly, we find correlated rates of evolution of neighbours in networks based on co-expression, metabolism, and synthetic lethal genetic interactions. While the correlations are statistically significant, their magnitude is small, with network...

  8. CPL:Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

    Institute of Scientific and Technical Information of China (English)

    代启国; 郭茂祖; 刘晓燕; 滕志霞; 王春宇

    2014-01-01

    Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework (CPL) that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. The CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.

  9. WD40 proteins propel cellular networks.

    Science.gov (United States)

    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.

  10. Monosaccharide templates for de novo designed 4-alpha-helix bundle proteins: template effects in carboproteins

    DEFF Research Database (Denmark)

    Brask, Jesper; Dideriksen, J.M.; Nielsen, John;

    2003-01-01

    De novo design and total chemical synthesis of proteins provide powerful approaches to critically test our understanding of protein folding, structure, and stability. The 4-alpha-helix bundle is a frequently studied structure in which four amphiphilic alpha-helical peptide strands form a hydropho......)) and melting points in chemical and thermal denaturation experiments....

  11. Structure of a C-terminal fragment of its Vps53 subunit suggests similarity of Golgi-associated retrograde protein (GARP) complex to a family of tethering complexes

    Energy Technology Data Exchange (ETDEWEB)

    Vasan, Neil; Hutagalung, Alex; Novick, Peter; Reinisch, Karin M. (Yale); (UCLJ)

    2010-08-13

    The Golgi-associated retrograde protein (GARP) complex is a membrane-tethering complex that functions in traffic from endosomes to the trans-Golgi network. Here we present the structure of a C-terminal fragment of the Vps53 subunit, important for binding endosome-derived vesicles, at a resolution of 2.9 {angstrom}. We show that the C terminus consists of two {alpha}-helical bundles arranged in tandem, and we identify a highly conserved surface patch, which may play a role in vesicle recognition. Mutations of the surface result in defects in membrane traffic. The fold of the Vps53 C terminus is strongly reminiscent of proteins that belong to three other tethering complexes - Dsl1, conserved oligomeric Golgi, and the exocyst - thought to share a common evolutionary origin. Thus, the structure of the Vps53 C terminus suggests that GARP belongs to this family of complexes.

  12. Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study

    OpenAIRE

    Jiang Jonathan Q; Wu Maoying

    2012-01-01

    Abstract Background Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard. Results We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably shar...

  13. Evolutionarily conserved herpesviral protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Even Fossum

    2009-09-01

    Full Text Available Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV and Kaposi's sarcoma-associated herpesvirus (KSHV. In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1, murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H, and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.

  14. Stabilized helical peptides: a strategy to target protein-protein interactions.

    Science.gov (United States)

    Klein, Mark A

    2014-08-14

    Protein-protein interactions are critical for cell proliferation, differentiation, and function. Peptides hold great promise for clinical applications focused on targeting protein-protein interactions. Advantages of peptides include a large chemical space and potential diversity of sequences and structures. However, peptides do present well-known challenges for drug development. Progress has been made in the development of stabilizing alpha helices for potential therapeutic applications. Advantages and disadvantages of different methods of helical peptide stabilization are discussed.

  15. Connecting the dots in Huntington's disease with protein interaction networks

    OpenAIRE

    Giorgini, Flaviano; Muchowski, Paul J.

    2005-01-01

    Analysis of protein-protein interaction networks is becoming important for inferring the function of uncharacterized proteins. A recent study using this approach has identified new proteins and interactions that might be involved in the pathogenesis of the neurodegenerative disorder Huntington's disease, including a GTPase-activating protein that co-localizes with protein aggregates in Huntington's disease patients.

  16. Mac-2 binding protein is a cell-adhesive protein of the extracellular matrix which self-assembles into ring-like structures and binds beta1 integrins, collagens and fibronectin

    DEFF Research Database (Denmark)

    Sasaki, T; Brakebusch, C; Engel, J;

    1998-01-01

    Human Mac-2 binding protein (M2BP) was prepared in recombinant form from the culture medium of 293 kidney cells and consisted of a 92 kDa subunit. The protein was obtained in a native state as indicated by CD spectroscopy, demonstrating alpha-helical and beta-type structure, and by protease resis...

  17. The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction.

    Science.gov (United States)

    Fang, Yi; Sun, Mengtian; Dai, Guoxian; Ramain, Karthik

    2016-01-01

    Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods. PMID:26886733

  18. Modularity in the evolution of yeast protein interaction network

    OpenAIRE

    Ogishima, Soichi; Tanaka, Hiroshi; Nakaya, Jun

    2015-01-01

    Protein interaction networks are known to exhibit remarkable structures: scale-free and small-world and modular structures. To explain the evolutionary processes of protein interaction networks possessing scale-free and small-world structures, preferential attachment and duplication-divergence models have been proposed as mathematical models. Protein interaction networks are also known to exhibit another remarkable structural characteristic, modular structure. How the protein interaction netw...

  19. Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

    OpenAIRE

    Tan Kai; Kim Jongkwang

    2010-01-01

    Abstract Background Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data. Results We present an algorithm, miPALM (Module Inference by Parametric Local Modularity), to infer protein complexes in a protein-pr...

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

    Science.gov (United States)

    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. Protein interaction network related to Helicobacter pylori infection response

    Institute of Scientific and Technical Information of China (English)

    Kyu Kwang Kim; Han Bok Kim

    2009-01-01

    AIM: To understand the complex reaction of gastric inflammation induced by Helicobacter pylori (H pylori ) in a systematic manner using a protein interaction network. METHODS: The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins. A network of protein interactions was constructed by searching the primary interactions of selected proteins. The constructed network was mathematically analyzed and its biological function was examined. In addition, the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them.RESULTS: The scale-free network showing the relationship between inflammation and carcinogenesis was constructed. Mathematical analysis showed hub and bottleneck proteins, and these proteins were mostly related to immune response. The network contained pathways and proteins related to H pylori infection, such as the JAK-STAT pathway triggered by interleukins. Activation of nuclear factor (NF)-kB, TLR4, and other proteins known to function as core proteins of immune response were also found.These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle, cell maintenance and proliferation, and transcription regulators such as BRCA1, FOS, REL, and zinc finger proteins. The extension of nodes showed interactions of the immune proteins with cancerrelated proteins. One extended network, the core network, a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION: Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins. The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

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

    OpenAIRE

    Farid Amir-Ghiasvand; Abbas Nowzari-Dalini; Vida Momenzadeh

    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...

  3. Identifying drug-target proteins based on network features

    Institute of Scientific and Technical Information of China (English)

    ZHU MingZhu; GAO Lei; LI Xia; LIU ZhiCheng

    2009-01-01

    Proteins rarely function in isolation Inside and outside cells, but operate as part of a highly Intercon-nected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action In terms of informatice. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interac-tion network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins In the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been dem-onstrated to be drug-target proteins in the literature.

  4. Identifying drug-target proteins based on network features

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Proteins rarely function in isolation inside and outside cells, but operate as part of a highly intercon- nected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action in terms of informatics. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interac- tion network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins in the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been dem- onstrated to be drug-target proteins in the literature.

  5. Ribo-Proteomics Approach to Profile RNA-Protein and Protein-Protein Interaction Networks.

    Science.gov (United States)

    Yeh, Hsin-Sung; Chang, Jae-Woong; Yong, Jeongsik

    2016-01-01

    Characterizing protein-protein and protein-RNA interaction networks is a fundamental step to understanding the function of an RNA-binding protein. In many cases, these interactions are transient and highly dynamic. Therefore, capturing stable as well as transient interactions in living cells for the identification of protein-binding partners and the mapping of RNA-binding sequences is key to a successful establishment of the molecular interaction network. In this chapter, we will describe a method for capturing the molecular interactions in living cells using formaldehyde as a crosslinker and enriching a specific RNA-protein complex from cell extracts followed by mass spectrometry and Next-Gen sequencing analyses. PMID:26965265

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

    Directory of Open Access Journals (Sweden)

    Hao Wu

    Full Text Available 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.

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

    Science.gov (United States)

    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. PMID:24642838

  8. Dynamical Analysis of Protein Regulatory Network in Budding Yeast Nucleus

    Institute of Scientific and Technical Information of China (English)

    LI Fang-Ting; JIA Xun

    2006-01-01

    @@ Recent progresses in the protein regulatory network of budding yeast Saccharomyces cerevisiae have provided a global picture of its protein network for further dynamical research. We simplify and modularize the protein regulatory networks in yeast nucleus, and study the dynamical properties of the core 37-node network by a Boolean network model, especially the evolution steps and final fixed points. Our simulation results show that the number of fixed points N(k) for a given size of the attraction basin k obeys a power-law distribution N(k)∝k-2.024. The yeast network is more similar to a scale-free network than a random network in the above dynamical properties.

  9. Evaluation of clustering algorithms for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    van Helden Jacques

    2006-11-01

    Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

  10. Protein Co-Expression Network Analysis (ProCoNA)

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  14. Towards a matrix mechanics framework for dynamic protein network

    OpenAIRE

    Bhattacharya, Sanjoy K.

    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 interac...

  15. Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

    Science.gov (United States)

    Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming

    2016-07-01

    Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences. PMID:27267620

  16. Enhancing the functional content of eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Gaurav Pandey

    Full Text Available Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.

  17. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    OpenAIRE

    Peng Liu; Lei Yang; Daming Shi; Xianglong Tang

    2015-01-01

    A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction net...

  18. Connectivity of neutral networks and structural conservation in protein evolution

    OpenAIRE

    Bastolla, Ugo; Porto, Markus; Roman, H. Eduardo; Vendruscolo, Michele

    2001-01-01

    Protein structures are much more conserved than sequences during evolution. Based on this observation, we investigate the consequences of structural conservation on protein evolution. We study seven of the most studied protein folds, finding out that an extended neutral network in sequence space is associated to each of them. Within our model, neutral evolution leads to a non-Poissonian substitution process, due to the broad distribution of connectivities in neutral networks. The observation ...

  19. Functional organization and its implication in evolution of the human protein-protein interaction network

    OpenAIRE

    Zhao Yiqiang; Mooney Sean D

    2012-01-01

    Abstract Background Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolv...

  20. Profiling of Protein Interaction Networks of Protein Complexes Using Affinity Purification and Quantitative Mass Spectrometry*

    OpenAIRE

    Kaake, Robyn M; Wang, Xiaorong; Huang, Lan

    2010-01-01

    Protein-protein interactions are important for nearly all biological processes, and it is known that aberrant protein-protein interactions can lead to human disease and cancer. Recent evidence has suggested that protein interaction interfaces describe a new class of attractive targets for drug development. Full characterization of protein interaction networks of protein complexes and their dynamics in response to various cellular cues will provide essential information for us to understand ho...

  1. Adding protein context to the human protein-protein interaction network to reveal meaningful interactions.

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer

    Full Text Available Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs, which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the

  2. Evidence of probabilistic behaviour in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Reifman Jaques

    2008-01-01

    Full Text Available Abstract Background Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour. Results We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links. Conclusion The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.

  3. TMRPres2D: high quality visual representation of transmembrane protein models.

    Science.gov (United States)

    Spyropoulos, Ioannis C; Liakopoulos, Theodore D; Bagos, Pantelis G; Hamodrakas, Stavros J

    2004-11-22

    The 'TransMembrane protein Re-Presentation in 2-Dimensions' (TMRPres2D) tool, automates the creation of uniform, two-dimensional, high analysis graphical images/models of alpha-helical or beta-barrel transmembrane proteins. Protein sequence data and structural information may be acquired from public protein knowledge bases, emanate from prediction algorithms, or even be defined by the user. Several important biological and physical sequence attributes can be embedded in the graphical representation. PMID:15201184

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    G Jack Peterson

    Full Text Available 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.

  6. Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

    OpenAIRE

    G Jack Peterson; Steve Pressé; 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 t...

  7. Fluorescently labeled pulmonary surfactant protein C in spread phospholipid monolayers.

    OpenAIRE

    Nag, K.; Perez-Gil, J.; Cruz, A; Keough, K M

    1996-01-01

    Pulmonary surfactant, a lipid-protein complex, secreted into the fluid lining of lungs prevents alveolar collapse at low lung volumes. Pulmonary surfactant protein C (SP-C), an acylated, hydrophobic, alpha-helical peptide, enhances the surface activity of pulmonary surfactant lipids. Fluorescein-labeled SP-C (F-SP-C) (3, 6, 12 wt%) in dipalmitoylphosphatidylcholine (DPPC), and DPPC:dipalmitoylphosphatidylglycerol (DPPG) [DPPC:DPPG 7:3 mol/mol] in spread monolayers was studied by epifluorescen...

  8. PANADA: protein association network annotation, determination and analysis.

    Directory of Open Access Journals (Sweden)

    Alberto J M Martin

    Full Text Available 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/.

  9. Protein interaction networks--more than mere modules.

    Directory of Open Access Journals (Sweden)

    Stefan Pinkert

    2010-01-01

    Full Text Available It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a "module" in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a "module". In a self-consistent manner, proteins are grouped into "functional roles" if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network's structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function

  10. Dissipative electro-elastic network model of protein electrostatics

    CERN Document Server

    Martin, Daniel R; Matyushov, Dmitry V

    2011-01-01

    We propose a dissipative electro-elastic network model (DENM) to describe the dynamics and statistics of electrostatic fluctuations at active sites of proteins. The model combines the harmonic network of residue beads with overdamped dynamics of the normal modes of the network characterized by two friction coefficients. The electrostatic component is introduced to the model through atomic charges of the protein force field. The overall effect of the electrostatic fluctuations of the network is recorded through the frequency-dependent response functions of the electrostatic potential and electric field at the active site. We also consider the dynamics of displacements of individual residues in the network and the dynamics of distances between pairs of residues. The model is tested against loss spectra of residue displacements and the electrostatic potential and electric field at the heme's iron from all-atom molecular dynamics simulations of three hydrated globular proteins.

  11. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  12. Network based approaches reveal clustering in protein point patterns

    Science.gov (United States)

    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.

  13. Finding finer functions for partially characterized proteins by protein-protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins' novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

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

    Science.gov (United States)

    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.

  15. A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

    Directory of Open Access Journals (Sweden)

    Jordán Ferenc

    2010-07-01

    Full Text Available Abstract Background Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases. Results Based on the i2d and OMIM databases, we have constructed (i a network of proteins causing five selected diseases (DP, disease proteins plus their interacting partners (IP, non-disease proteins, the DPIP network and (ii a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1 various cancers, (2 heart diseases, (3 obesity, (4 diabetes and (5 autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins. Conclusions We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand

  16. The Membrane- and Soluble-Protein Helix-Helix Interactome: Similar Geometry via Different Interactions

    OpenAIRE

    Zhang, Shao-Qing; Kulp, Daniel W.; Schramm, Chaim A.; Mravic, Marco; Samish, Ilan; DeGrado, William F

    2015-01-01

    Alpha-helices are a basic unit of protein secondary structure and therefore the interaction between helices is crucial to understanding tertiary and higher-order folds. Comparing subtle variations in the structural and sequence motifs between membrane and soluble proteins sheds light on the different constraints faced by each environment and elucidates the complex puzzle of membrane protein folding. Here, we demonstrate that membrane and water-soluble helix pairs share a small number of simil...

  17. An analysis pipeline for the inferenceof protein-protein interaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Taylor, Ronald C. [Pacific Northwest National Laboratory (PNNL); Singhal, Mudita [Pacific Northwest National Laboratory (PNNL); Daly, Don S. [Pacific Northwest National Laboratory (PNNL); Gilmore, Jason [Pacific Northwest National Laboratory (PNNL); Cannon, Bill [Pacific Northwest National Laboratory (PNNL); Domico, Kelly [Pacific Northwest National Laboratory (PNNL); White, Amanda M. [Pacific Northwest National Laboratory (PNNL); Auberry, Deanna L [ORNL; Auberry, Kenneth J [ORNL; Hooker, Brian [Pacific Northwest National Laboratory (PNNL); Hurst, Gregory {Greg} B [ORNL; McDermott, Jason [Pacific Northwest National Laboratory (PNNL); McDonald, W Hayes [ORNL; Pelletier, Dale A [ORNL; Schmoyer, Denise D [ORNL; Wiley, Steven [Pacific Northwest National Laboratory (PNNL)

    2009-09-01

    We present an integrated platform that is used for the reconstruction and analysis of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey experiment data. At the heart of this pipeline is 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. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. For integration, comparison and analysis of the inferred protein-protein interactions with interaction evidence obtained from multiple public sources, the pipeline connects to the Collective Analysis of Biological Interaction Networks (CABIN) software. Incorporating BEPro3 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 MS bait-prey experiments.

  18. Bias tradeoffs in the creation and analysis of protein-protein interaction networks

    OpenAIRE

    Gillis, Jesse; Ballouz, Sara; Pavlidis, Paul

    2014-01-01

    Networks constructed from aggregated protein-protein interaction data are commonplace in biology. But the studies these data are derived from were conducted with their own hypotheses and foci. Focusing on data from budding yeast present in BioGRID, we determine that many of the downstream signals present in network data are significantly impacted by biases in the original data. We determine the degree to which selection bias in favor of biologically interesting bait proteins goes down with st...

  19. Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

    OpenAIRE

    Chiam Tak; Cho Young-Rae

    2012-01-01

    Abstract Background Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein co...

  20. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  1. Properties of Fibrillar Protein Assemblies and their Percolating Networks

    NARCIS (Netherlands)

    Veerman, C.

    2004-01-01

    Properties of Fibrillar Protein Assemblies and their Percolating Networks. PhD thesis, Wageningen University, The Netherlands Keywords: bovine serum albumin, complex fluids, excluded volume, fibrils, gels, innovation, b-lactoglobulin, ovalbumin, percolation, proteins, rheology, rheo-optics, self-ass

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

    Science.gov (United States)

    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

  3. Membrane Protein Crystallisation: Current Trends and Future Perspectives.

    Science.gov (United States)

    Parker, Joanne L; Newstead, Simon

    2016-01-01

    Alpha helical membrane proteins are the targets for many pharmaceutical drugs and play important roles in physiology and disease processes. In recent years, substantial progress has been made in determining their atomic structure using X-ray crystallography. However, a major bottleneck still remains; the identification of conditions that give crystals that are suitable for structure determination. Over the past 10 years we have been analysing the crystallisation conditions reported for alpha helical membrane proteins with the aim to facilitate a rational approach to the design and implementation of successful crystallisation screens. The result has been the development of MemGold, MemGold2 and the additive screen MemAdvantage. The associated analysis, summarised and updated in this chapter, has revealed a number of surprisingly successfully strategies for crystallisation and detergent selection. PMID:27553235

  4. Alpha-helical destabilization of the Bcl-2-BH4-domain peptide abolishes its ability to inhibit the IP3 receptor.

    Directory of Open Access Journals (Sweden)

    Giovanni Monaco

    Full Text Available The anti-apoptotic Bcl-2 protein is the founding member and namesake of the Bcl-2-protein family. It has recently been demonstrated that Bcl-2, apart from its anti-apoptotic role at mitochondrial membranes, can also directly interact with the inositol 1,4,5-trisphosphate receptor (IP3R, the primary Ca(2+-release channel in the endoplasmic reticulum (ER. Bcl-2 can thereby reduce pro-apoptotic IP3R-mediated Ca(2+ release from the ER. Moreover, the Bcl-2 homology domain 4 (Bcl-2-BH4 has been identified as essential and sufficient for this IP3R-mediated anti-apoptotic activity. In the present study, we investigated whether the reported inhibitory effect of a Bcl-2-BH4 peptide on the IP 3R1 was related to the distinctive α-helical conformation of the BH4 domain peptide. We therefore designed a peptide with two glycine "hinges" replacing residues I14 and V15, of the wild-type Bcl-2-BH4 domain (Bcl-2-BH4-IV/GG. By comparing the structural and functional properties of the Bcl-2-BH4-IV/GG peptide with its native counterpart, we found that the variant contained reduced α-helicity, neither bound nor inhibited the IP 3R1 channel, and in turn lost its anti-apoptotic effect. Similar results were obtained with other substitutions in Bcl-2-BH4 that destabilized the α-helix with concomitant loss of IP3R inhibition. These results provide new insights for the further development of Bcl-2-BH4-derived peptides as specific inhibitors of the IP3R with significant pharmacological implications.

  5. Topological features of proteins from amino acid residue networks

    CERN Document Server

    Alves, N A; Alves, Nelson Augusto; Martinez, Alexandre Souto

    2006-01-01

    Topological properties of native folds are obtained from statistical analysis of 160 low homology proteins covering the four structural classes. This is done analysing one, two and three-vertex joint distribution of quantities related to the corresponding network of amino acid residues. Emphasis on the amino acid residue hydrophobicity leads to the definition of their center of mass as vertices in this contact network model with interactions represented by edges. The network analysis helps us to interpret experimental results such as hydrophobic scales and fraction of buried accessible surface area in terms of the network connectivity. To explore the vertex type dependent correlations, we build a network of hydrophobic and polar vertices. This procedure presents the wiring diagram of the topological structure of globular proteins leading to the following attachment probabilities between hydrophobic-hydrophobic 0.424(5), hydrophobic-polar 0.419(2) and polar-polar 0.157(3) residues.

  6. SLIDER: Mining correlated motifs in protein-protein interaction networks

    NARCIS (Netherlands)

    Boyen, P.; Dijk, van A.D.J.; Ham, van R.C.H.J.; Neven, F.

    2009-01-01

    Abstract—Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a

  7. Construction of a chloroplast protein interaction network and functional mining of photosynthetic proteins in Arabidopsis thaliana

    Institute of Scientific and Technical Information of China (English)

    Qing-Bo Yu; Yong-Lan Cui; Kang Chong; Yi-Xue Li; Yu-Hua Li; Zhongming Zhao; Tie-Liu Shi; Zhong-Nan Yang; Guang Li; Guan Wang; Jing-Chun Sun; Peng-Cheng Wang; Chen Wang; Hua-Ling Mi; Wei-Min Ma; Jian Cui

    2008-01-01

    Chloroplast is a typical plant cell organeUe where photosynthesis takes place.In this study,a total of 1 808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions.We then constructed a chloroplast protein interaction network primarily based on these core protein interactions.The network had 22 925 protein interaction pairs which involved 2 214 proteins.A total of 160 previously uncharacterized proteins were annotated in this network.The subunits of the photosynthetic complexes were modularized,and the functional relationships among photosystem Ⅰ (PSI),photosystem Ⅱ (PSII),light harvesting complex of photosystem Ⅰ (LHC Ⅰ) and light harvesting complex of photosystem Ⅰ (LHC Ⅱ) could be deduced from the predicted protein interactions in this network.We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis.Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.

  8. The PAM domain, a multi-protein complex-associated module with an all-alpha-helix fold

    Directory of Open Access Journals (Sweden)

    Izaurralde Elisa

    2003-12-01

    Full Text Available Abstract Background Multimeric protein complexes have a role in many cellular pathways and are highly interconnected with various other proteins. The characterization of their domain composition and organization provides useful information on the specific role of each region of their sequence. Results We identified a new module, the PAM domain (PCI/PINT associated module, present in single subunits of well characterized multiprotein complexes, like the regulatory lid of the 26S proteasome, the COP-9 signalosome and the Sac3-Thp1 complex. This module is an around 200 residue long domain with a predicted TPR-like all-alpha-helical fold. Conclusions The occurrence of the PAM domain in specific subunits of multimeric protein complexes, together with the role of other all-alpha-helical folds in protein-protein interactions, suggest a function for this domain in mediating transient binding to diverse target proteins.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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

  11. Creative elements: network-based predictions of active centres in proteins, cellular and social networks

    CERN Document Server

    Csermely, Peter

    2008-01-01

    Active centres and hot spots of proteins have a paramount importance in enzyme action, protein complex formation and drug design. Recently a number of publications successfully applied the analysis of residue networks to predict active centres in proteins. Most real-world networks show a number of properties, such as small-worldness or scale-free degree distribution, which are rather general features of networks from molecules to the society. Based on extensive analogies I propose that the existing findings and methodology enable us to detect active centres in cells, social networks and ecosystems. Members of these active centres are creative elements of the respective networks, which may help them to survive unprecedented, novel challenges, and play a key role in the development, survival and evolvability of complex systems.

  12. Assessment of interactions between four proteins and benzothiazole derivatives by DSC and CD

    Energy Technology Data Exchange (ETDEWEB)

    Hassan, Natalia [Soft Matter and Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, Campus Vida, University of Santiago de Compostela, E-15782 Santiago de Compostela (Spain); Verdes, Pedro V., E-mail: pedro.vazquez@usc.e [Soft Matter and Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, Campus Vida, University of Santiago de Compostela, E-15782 Santiago de Compostela (Spain); Ruso, Juan M. [Soft Matter and Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, Campus Vida, University of Santiago de Compostela, E-15782 Santiago de Compostela (Spain)

    2011-03-15

    The thermal denaturation of ovalbumin, lysozyme, myoglobin and fibrinogen at different BTS concentrations have been investigated using differential scanning calorimetry (DSC) and circular dichroism (CD) spectroscopy. Thermodynamic parameters: melting temperatures (T{sub m}), calorimetric enthalpy ({Delta}H), van't Hoff enthalpy ({Delta}H{sub v}) were obtained for all the systems under study. Thermal denaturation of the four proteins was completely irreversible. Changes in the protein conformation due to the adsorption of BTS molecules have been monitored by using UV-CD spectra. Greater changes in {alpha}-helical contents correspond with the BTS higher concentrations. The lysozyme denaturation temperature increases at low concentrations BTS indicating that BTS acts as a structure stabilizer; meanwhile it acts as a destabilizer at higher concentrations in all the proteins studied. The major effect is observed in the case of myoglobin, the protein with the highest {alpha}-helical secondary structure (75%).

  13. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  14. Response of the mosquito protein interaction network to dengue infection

    Directory of Open Access Journals (Sweden)

    Pike Andrew D

    2010-06-01

    Full Text Available Abstract Background Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions. Results We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 Aedes aegypti proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT, immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0% randomly selected genes. Conclusions Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission.

  15. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  16. Robustness of indispensable nodes in controlling protein-protein interaction network

    CERN Document Server

    Zhang, Xizhe; Yang, Yunyi

    2016-01-01

    Recently, the structural controllability theory has been introduced to analyze the Protein-Protein Interaction (PPI) network. The indispensable nodes, which their removal increase the number of driver nodes to control the network, are found essential in PPI network. However, the PPI network is far from complete and there may exist many false-positive or false-negative interactions, which promotes us to question: are these indispensable nodes robust to structural change? Here we systematically investigate the robustness of indispensable nodes of PPI network by removing and adding possible interactions. We found that the indispensable nodes are sensitive to the structural change and very few edges can change the type of many indispensable nodes. The finding may promote our understanding to the control principle of PPI network.

  17. Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

    OpenAIRE

    Ru Shen; Xiaosheng Wang; Chittibabu Guda

    2015-01-01

    Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological networ...

  18. Lists2Networks: Integrated analysis of gene/protein lists

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2010-02-01

    Full Text Available Abstract Background Systems biologists are faced with the difficultly of analyzing results from large-scale studies that profile the activity of many genes, RNAs and proteins, applied in different experiments, under different conditions, and reported in different publications. To address this challenge it is desirable to compare the results from different related studies such as mRNA expression microarrays, genome-wide ChIP-X, RNAi screens, proteomics and phosphoproteomics experiments in a coherent global framework. In addition, linking high-content multilayered experimental results with prior biological knowledge can be useful for identifying functional themes and form novel hypotheses. Results We present Lists2Networks, a web-based system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system. Conclusions Lists2Networks is a user friendly web-based software system expected to significantly ease the computational analysis process for experimental systems biologists employing high-throughput experiments at multiple layers of regulation. The system is freely available at http://www.lists2networks.org.

  19. Topological Properties of Protein-Protein and Metabolic Interaction Networks of Drosophila melanogaster

    Institute of Scientific and Technical Information of China (English)

    Thanigaimani Rajarathinam; Yen-Han Lin

    2006-01-01

    The underlying principle governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. A key feature of the scale-free architecture is the vitality of the most connected nodes (hubs). The major objective of this article was to analyze the protein-protein and metabolic interaction networks of Drosophila melanogaster by considering the architectural patterns and the consequence of removal of hubs on the topological parameter of the two interaction systems. Analysis showed that both interaction networks follow a scale-free model, establishing the fact that most real world networks,from varied situations, conform to the small world pattern. The average path length showed a two-fold and a three-fold increase (changing from 9.42 to 20.93 and from 5.29 to 17.75, respectively) for the protein-protein and metabolic interaction networks, respectively, due to the deletion of hubs. On the contrary, the arbitrary elimination of nodes did not show any remarkable disparity in the topological parameter of the protein-protein and metabolic interaction networks (average path length: 9.42±0.02 and 5.27±0.01, respectively). This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks.

  20. Context-specific protein network miner - an online system for exploring context-specific protein interaction networks from the literature

    KAUST Repository

    Chowdhary, Rajesh

    2012-04-06

    Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/. © 2012 Chowdhary et al.

  1. Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics.

    Science.gov (United States)

    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

  2. The topology of the bacterial co-conserved protein network and its implications for predicting protein function

    Directory of Open Access Journals (Sweden)

    Leach Sonia M

    2008-06-01

    Full Text Available Abstract Background Protein-protein interactions networks are most often generated from physical protein-protein interaction data. Co-conservation, also known as phylogenetic profiles, is an alternative source of information for generating protein interaction networks. Co-conservation methods generate interaction networks among proteins that are gained or lost together through evolution. Co-conservation is a particularly useful technique in the compact bacteria genomes. Prior studies in yeast suggest that the topology of protein-protein interaction networks generated from physical interaction assays can offer important insight into protein function. Here, we hypothesize that in bacteria, the topology of protein interaction networks derived via co-conservation information could similarly improve methods for predicting protein function. Since the topology of bacteria co-conservation protein-protein interaction networks has not previously been studied in depth, we first perform such an analysis for co-conservation networks in E. coli K12. Next, we demonstrate one way in which network connectivity measures and global and local function distribution can be exploited to predict protein function for previously uncharacterized proteins. Results Our results showed, like most biological networks, our bacteria co-conserved protein-protein interaction networks had scale-free topologies. Our results indicated that some properties of the physical yeast interaction network hold in our bacteria co-conservation networks, such as high connectivity for essential proteins. However, the high connectivity among protein complexes in the yeast physical network was not seen in the co-conservation network which uses all bacteria as the reference set. We found that the distribution of node connectivity varied by functional category and could be informative for function prediction. By integrating of functional information from different annotation sources and using the

  3. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.

    Science.gov (United States)

    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

  4. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.

    Directory of Open Access Journals (Sweden)

    Eileen Marie Hanna

    Full Text Available 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.

  5. Identifying functional modules in protein-protein interaction networks: An integrated exact approach

    NARCIS (Netherlands)

    Dittrich, M.; Klau, G.W.; Rosenwald, A.; Dandekar, T.; et al, not CWI

    2008-01-01

    Motivation: With the exponential growth of expression and protein-protein interaction (PPI) data, the frontier of research in system biology shifts more and more to the integrated analysis of these large datasets. Of particular interest is the identification of functional modules in PPI networks, sh

  6. P-Finder: Reconstruction of Signaling Networks from Protein-Protein Interactions and GO Annotations.

    Science.gov (United States)

    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.

  7. Simple Protein Complex Purification and Identification Method Suitable for High- throughput Mapping of Protein Interaction Networks

    Energy Technology Data Exchange (ETDEWEB)

    Markillie, Lye Meng; Lin, Chiann Tso; Adkins, Joshua N.; Auberry, Deanna L.; Hill, Eric A.; Hooker, Brian S.; Moore, Priscilla A.; Moore, Ronald J.; Shi, Liang; Wiley, H. S.; Kery, Vladimir

    2005-04-11

    Most of the current methods for purification and identification of protein complexes use endogenous expression of affinity tagged bait, tandem affinity tag purification of protein complexes followed by specific elution of complexes from beads, gel separation, in-gel digestion and mass spectrometric analysis of protein interactors. We propose a single affinity tag in vitro pulldown assay with denaturing elution, trypsin digestion in organic solvent and LC ESI MS/MS protein identification using SEQUEST analysis. Our method is simple, easy to scale up and automate thus suitable for high throughput mapping of protein interaction networks and functional proteomics.

  8. Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters

    OpenAIRE

    Eileen Marie Hanna; Nazar Zaki; Amr Amin

    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, ...

  9. Constructing a robust protein-protein interaction network by integrating multiple public databases

    OpenAIRE

    Ding Don; Tong Weida; Fang Hong; Ye Yanbin; Su Zhenqiang; Guo Li; Liu Zhichao; Martha Venkata-Swamy; Xu Xiaowei

    2011-01-01

    Abstract Background Protein-protein interactions (PPIs) are a critical component for many underlying biological processes. A PPI network can provide insight into the mechanisms of these processes, as well as the relationships among different proteins and toxicants that are potentially involved in the processes. There are many PPI databases publicly available, each with a specific focus. The challenge is how to effectively combine their contents to generate a robust and biologically relevant P...

  10. Domain-Domain Interactions Underlying Herpesvirus-Human Protein-Protein Interaction Networks

    OpenAIRE

    Zohar Itzhaki

    2011-01-01

    Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms ...

  11. Systematic discovery of new recognition peptides mediating protein interaction networks

    DEFF Research Database (Denmark)

    Neduva, Victor; Linding, Rune; Su-Angrand, Isabelle;

    2005-01-01

    Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains...... molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based...... that binds Translin with a KD of 43 microM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.Many aspects of cell signalling, trafficking, and targeting are governed...

  12. Predicting the Secondary Structure of Proteins by Cascading Neural Networks

    Directory of Open Access Journals (Sweden)

    Maryam Alirezaee

    2012-11-01

    Full Text Available Protein Secondary Structure Prediction (PSSP is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle this problem, Artificial Neural Networks (ANNs are considered as one of the successful tools that are widely used in this field. Recently, many efforts have been devoted to modify, improve and combine this methodology with other machine learning methods in order to get better results. In this work, we have proposed a two-stage feed forward neural network for prediction of protein secondary structures. To evaluate our approach, it is applied on RS126 dataset and its results are compared with some other NN-based methods.

  13. Coiled-coil protein composition of 22 proteomes – differences and common themes in subcellular infrastructure and traffic control

    Directory of Open Access Journals (Sweden)

    Meier Iris

    2005-11-01

    Full Text Available Abstract Background Long alpha-helical coiled-coil proteins are involved in diverse organizational and regulatory processes in eukaryotic cells. They provide cables and networks in the cyto- and nucleoskeleton, molecular scaffolds that organize membrane systems and tissues, motors, levers, rotating arms, and possibly springs. Mutations in long coiled-coil proteins have been implemented in a growing number of human diseases. Using the coiled-coil prediction program MultiCoil, we have previously identified all long coiled-coil proteins from the model plant Arabidopsis thaliana and have established a searchable Arabidopsis coiled-coil protein database. Results Here, we have identified all proteins with long coiled-coil domains from 21 additional fully sequenced genomes. Because regions predicted to form coiled-coils interfere with sequence homology determination, we have developed a sequence comparison and clustering strategy based on masking predicted coiled-coil domains. Comparing and grouping all long coiled-coil proteins from 22 genomes, the kingdom-specificity of coiled-coil protein families was determined. At the same time, a number of proteins with unknown function could be grouped with already characterized proteins from other organisms. Conclusion MultiCoil predicts proteins with extended coiled-coil domains (more than 250 amino acids to be largely absent from bacterial genomes, but present in archaea and eukaryotes. The structural maintenance of chromosomes proteins and their relatives are the only long coiled-coil protein family clearly conserved throughout all kingdoms, indicating their ancient nature. Motor proteins, membrane tethering and vesicle transport proteins are the dominant eukaryote-specific long coiled-coil proteins, suggesting that coiled-coil proteins have gained functions in the increasingly complex processes of subcellular infrastructure maintenance and trafficking control of the eukaryotic cell.

  14. AtPIN: Arabidopsis thaliana Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Silva-Filho Marcio C

    2009-12-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C3 which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS (AT5G26710 we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630, a disease resistance protein (AT3G50950 and a zinc finger protein (AT5G24930, which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

  15. Conventional and novel Gγ protein families constitute the heterotrimeric G-protein signaling network in soybean.

    Directory of Open Access Journals (Sweden)

    Swarup Roy Choudhury

    Full Text Available Heterotrimeric G-proteins comprised of Gα, Gβ and Gγ proteins are important signal transducers in all eukaryotes. The Gγ protein of the G-protein heterotrimer is crucial for its proper targeting at the plasma membrane and correct functioning. Gγ proteins are significantly smaller and more diverse than the Gα and Gβ proteins. In model plants Arabidopsis and rice that have a single Gα and Gβ protein, the presence of two canonical Gγ proteins provide some diversity to the possible heterotrimeric combinations. Our recent analysis of the latest version of the soybean genome has identified ten Gγ proteins which belong to three distinct families based on their C-termini. We amplified the full length cDNAs, analyzed their detailed expression profile by quantitative PCR, assessed their localization and performed yeast-based interaction analysis to evaluate interaction specificity with different Gβ proteins. Our results show that ten Gγ genes are retained in the soybean genome and have interesting expression profiles across different developmental stages. Six of the newly identified proteins belong to two plant-specific Gγ protein families. Yeast-based interaction analyses predict some degree of interaction specificity between different Gβ and Gγ proteins. This research thus identifies a highly diverse G-protein network from a plant species. Homologs of these novel proteins have been previously identified as QTLs for grain size and yield in rice.

  16. Graphical Features of Functional Genes in Human Protein Interaction Network.

    Science.gov (United States)

    Wang, Pei; Chen, Yao; Lu, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  17. Locus heterogeneity disease genes encode proteins with high interconnectivity in the human protein interaction network.

    Science.gov (United States)

    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.

  18. Differential Protein Network Analysis of the Immune Cell Lineage

    Directory of Open Access Journals (Sweden)

    Trevor Clancy

    2014-01-01

    Full Text Available 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.

  19. Integrated cellular network of transcription regulations and protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2010-03-01

    Full Text Available Abstract Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

  20. Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

    Energy Technology Data Exchange (ETDEWEB)

    Goudarzi, Atta, E-mail: atta.goudarzi@utoronto.ca [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Merico, Daniele [The Centre for Applied Genomics, The Hospital for Sick Children, MaRS Centre-East Tower, 101 College Street Rm.14-701, Toronto, ON M5G 1L7 (Canada); Wunder, Jay S. [Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada); Andrulis, Irene L. [Department of Molecular Genetics, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8 (Canada); Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, ON M5G 1X5 (Canada)

    2013-04-08

    Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation.

  1. Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma Metastasis

    International Nuclear Information System (INIS)

    Osteosarcoma (OS) is the most common primary malignant tumor of the bone, and pulmonary metastasis is the most frequent cause of OS mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Expression profiling of OS tumors, and subsequent supervised network analysis, was performed to discover genetic networks differentially activated or organized in metastatic OS compared to localized OS. Broad trends among the profiles of metastatic tumors include aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was also found to be differentially expressed among OS cell lines with differing metastatic capacity in xenograft models. PRKCε transcript was more abundant in some metastatic OS tumors; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation

  2. Evolution versus "intelligent design": comparing the topology of protein-protein interaction networks to the Internet.

    Science.gov (United States)

    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.

  3. Correlating the ability of VP24 protein from Ebola and Marburg viruses to bind human karyopherin to their immune suppression mechanism and pathogenicity using computational methods

    OpenAIRE

    Sandeep Chakraborty; Rao, Basuthkar J.; Bjarni _Asgeirsson; Abhaya Dandekar

    2015-01-01

    Ebola, considered till recently as a rare and endemic disease, has dramatically transformed into a potentially global humanitarian crisis. The genome of Ebola, a member of the Filoviridae family, encodes seven proteins. Based on the recently implemented software (PAGAL) for analyzing the hydrophobicity and amphipathicity properties of alpha helices (AH) in proteins, we characterize the helices in the Ebola proteome. We demonstrate that AHs with characteristically unique features are involved ...

  4. Exploring virus relationships based on virus-host protein-protein interaction network

    OpenAIRE

    Xu Feng; Zhao Chen; Li Yuhua; Li Jiang; Deng Youping; Shi Tieliu

    2011-01-01

    Abstract Background Currently, several systems have been proposed to classify viruses and indicate the relationships between different ones, though each system has its limitations because of the complexity of viral origins and their rapid evolution rate. We hereby propose a new method to explore the relationships between different viruses. Method A new method, which is based on the virus-host protein-protein interaction network, is proposed in this paper to categorize viruses. The distances b...

  5. Identification of Candidate Genes related to Bovine Marbling using Protein-Protein Interaction Networks

    OpenAIRE

    Lim, Dajeong; Kim, Nam-Kuk; Park, Hye-Sun; Lee, Seung-Hwan; Cho, Yong-Min; Oh, Sung Jong; Kim, Tae-Hun; Kim, Heebal

    2011-01-01

    Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein intera...

  6. Improving Protein Fold Recognition by Deep Learning Networks

    Science.gov (United States)

    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.

  7. Prediction and systematic study of protein-protein interaction networks of Leptospira interrogans

    Institute of Scientific and Technical Information of China (English)

    SUN Jingchun; XU Jinlin; CAO Jianping; LIU Qi; GUO Xiaokui; SHI Tieliu; LI Yixue

    2006-01-01

    Leptospira interrogans serovar Lai is a pathogenic bacterium that causes a spirochetal zoonosis in humans and some animals. With its complete genome sequence available, it is possible to analyze protein-protein interactions from a whole- genome standpoint. Here we combine four recently developed computational approaches (gene fusion method, gene neighbor method, phylogenetic profiles method, and operon method) to predict protein-pro- tein interaction networks of Leptospira interrogans strain Lai. Through comprehensive analysis on in- teractions among proteins of motility and chemotaxis system, signal transduction, lipopolysaccaride bio- synthesis and a series of proteins related to adhesion and invasion, we provided information for further studying on its pathogenic mechanism. In addition, we also assigned 203 previously uncharacterized proteins with possible functions based on the known functions of its interacting partners. This work is helpful for further investigating L. interrogans strain Lai.

  8. A second-generation protein-protein interaction network of Helicobacter pylori.

    Science.gov (United States)

    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.

  9. Amyloid Beta-Protein and Neural Network Dysfunction

    Directory of Open Access Journals (Sweden)

    Fernando Peña-Ortega

    2013-01-01

    Full Text Available 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.

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

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available 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

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

    Science.gov (United States)

    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

  12. Information theory in systems biology. Part II: protein-protein interaction and signaling networks.

    Science.gov (United States)

    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.

  13. De novo backbone and sequence design of an idealized alpha/beta-barrel protein: Evidence of stable tertiary structure

    OpenAIRE

    Offredi, Fabrice; Dubail, Fabien; Kischel, Philippe; Sarinski, K.; Stern, A S; Van de Weerdt, Cécile; Hoch, J. C.; Prosperi, Christelle; François, Jean-Marie; Mayo, S. L.; Martial, Joseph

    2003-01-01

    We have designed, synthesized, and characterized a 216 amino acid residue sequence encoding a putative idealized alpha/beta-barrel protein. The design was elaborated in two steps. First, the idealized backbone was defined with geometric parameters representing our target fold: a central eight parallel-stranded beta-sheet surrounded by eight parallel alpha-helices, connected together with short structural turns on both sides of the barrel. An automated sequence selection algorithm, based on th...

  14. Phospho-tyrosine dependent protein–protein interaction network

    Science.gov (United States)

    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

  15. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    DEFF Research Database (Denmark)

    Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

    2010-01-01

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

  16. Predicting protein functions from redundancies in large-scale protein interaction networks

    Science.gov (United States)

    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.

  17. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    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

  18. Molecular Architecture of Spinal Cord Injury Protein Interaction Network.

    Directory of Open Access Journals (Sweden)

    Ali Alawieh

    Full Text Available Spinal cord injury (SCI is associated with complex pathophysiological processes that follow the primary traumatic event and determine the extent of secondary damage and functional recovery. Numerous reports have used global and hypothesis-driven approaches to identify protein changes that contribute to the overall pathology of SCI in an effort to identify potential therapeutic interventions. In this study, we use a semi-automatic annotation approach to detect terms referring to genes or proteins dysregulated in the SCI literature and develop a curated SCI interactome. Network analysis of the SCI interactome revealed the presence of a rich-club organization corresponding to a "powerhouse" of highly interacting hub-proteins. Studying the modular organization of the network have shown that rich-club proteins cluster into modules that are specifically enriched for biological processes that fall under the categories of cell death, inflammation, injury recognition and systems development. Pathway analysis of the interactome and the rich-club revealed high similarity indicating the role of the rich-club proteins as hubs of the most prominent pathways in disease pathophysiology and illustrating the centrality of pro-and anti-survival signal competition in the pathology of SCI. In addition, evaluation of centrality measures of single nodes within the rich-club have revealed that neuronal growth factor (NGF, caspase 3, and H-Ras are the most central nodes and potentially an interesting targets for therapy. Our integrative approach uncovers the molecular architecture of SCI interactome, and provide an essential resource for evaluating significant therapeutic candidates.

  19. Quantitation, network and function of protein phosphorylation in plant cell

    Directory of Open Access Journals (Sweden)

    Lin eZHU

    2013-01-01

    Full Text Available Protein phosphorylation is one of the most important post-translational modifications (PTMs as it participates in regulating various cellular processes and biological functions. It is therefore crucial to identify phosphorylated proteins to construct a phosphor-relay network, and eventually to understand the underlying molecular regulatory mechanism in response to both internal and external stimuli. The changes in phosphorylation status at these novel phosphosites can be accurately measured using a 15N-stable isotopic labeling in Arabidopsis (SILIA quantitative proteomic approach in a high-throughput manner. One of the unique characteristics of the SILIA quantitative phosphoproteomic approach is the preservation of native PTM status on protein during the entire peptide preparation procedure. Evolved from SILIA is another quantitative PTM proteomic approach, AQUIP (absolute quantitation of isoforms of post-translationally modified proteins, which was developed by combining the advantages of targeted proteomics with SILIA. Bioinformatics-based phosphorylation site prediction coupled with an MS-based in vitro kinase assay is an additional way to extend the capability of phosphosite identification from the total cellular protein. The combined use of SILIA and AQUIP provides a novel strategy for molecular systems biological study and for investigation of in vivo biological functions of these phosphoprotein isoforms and combinatorial codes of PTMs.

  20. Gene, protein, and network of male sterility in rice.

    Science.gov (United States)

    Wang, Kun; Peng, Xiaojue; Ji, Yanxiao; Yang, Pingfang; Zhu, Yingguo; Li, Shaoqing

    2013-01-01

    Rice is one of the most important model crop plants whose heterosis has been well-exploited in commercial hybrid seed production via a variety of types of male-sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility (CMS), photoperiod-sensitive male sterility, self-incompatibility, and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein, and integrated network levels, and also, present a perspective on the engineering of male-sterile lines for hybrid rice production.

  1. Gene, protein and network of male sterility in rice

    Directory of Open Access Journals (Sweden)

    Wang eKun

    2013-04-01

    Full Text Available Rice is one of the most important model crop plants whose heterosis has been well exploited in commercial hybrid seed production via a variety of types of male sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and proteins related to cytoplasmic male sterility, photoperiod sensitive male sterility, self-incompatibility and other types of microspores deterioration have been characterized through genetics or proteomics. Especially the latter, offers us a powerful and high throughput approach to discern the novel proteins involving in male-sterile pathways which may help us to breed artificial male-sterile system. This represents an alternative tool to meet the critical challenge of further development of hybrid rice. In this paper, we reviewed the recent developments in our understanding of male sterility in rice hybrid production across gene, protein and integrated network levels, and also, present a perspective on the engineering of male sterile lines for hybrid rice production.

  2. Identification of Candidate Genes related to Bovine Marbling using Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Dajeong Lim, Nam-Kuk Kim, Hye-Sun Park, Seung-Hwan Lee, Yong-Min Cho, Sung Jong Oh, Tae-Hun Kim, Heebal Kim

    2011-01-01

    Full Text Available Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The present study systemically analyzed genes associated with bovine marbling score and identified their relationships. The candidate nodes were obtained using MedScan text-mining tools and linked by protein-protein interaction (PPI from the Human Protein Reference Database (HPRD. To determine key node of marbling, the degree and betweenness centrality (BC were used. The hub nodes and biological pathways of our network are consistent with the previous reports about marbling traits, and also suggest unknown candidate genes associated with intramuscular fat. Five nodes were identified as hub genes, which was consistent with the network analysis using quantitative reverse-transcription PCR (qRT-PCR. Key nodes of the PPI network have positive roles (PPARγ, C/EBPα, and RUNX1T1 and negative roles (RXRA, CAMK2A in the development of intramuscular fat by several adipogenesis-related pathways. This study provides genetic information for identifying candidate genes for the marbling trait in bovine.

  3. Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

    Science.gov (United States)

    Simonis, Nicolas; Rual, Jean-François; Carvunis, Anne-Ruxandra; Tasan, Murat; Lemmens, Irma; Hirozane-Kishikawa, Tomoko; Hao, Tong; Sahalie, Julie M; Venkatesan, Kavitha; Gebreab, Fana; Cevik, Sebiha; Klitgord, Niels; Fan, Changyu; Braun, Pascal; Li, Ning; Ayivi-Guedehoussou, Nono; Dann, Elizabeth; Bertin, Nicolas; Szeto, David; Dricot, Amélie; Yildirim, Muhammed A; Lin, Chenwei; de Smet, Anne-Sophie; Kao, Huey-Ling; Simon, Christophe; Smolyar, Alex; Ahn, Jin Sook; Tewari, Muneesh; Boxem, Mike; Milstein, Stuart; Yu, Haiyuan; Dreze, Matija; Vandenhaute, Jean; Gunsalus, Kristin C; Cusick, Michael E; Hill, David E; Tavernier, Jan; Roth, Frederick P; Vidal, Marc

    2009-01-01

    To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins PMID:19123269

  4. Category theoretic analysis of hierarchical protein materials and social networks.

    Directory of Open Access Journals (Sweden)

    David I Spivak

    Full Text Available Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a "concept web" or "semantic network" except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.

  5. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    Directory of Open Access Journals (Sweden)

    Nielsen Jens

    2010-05-01

    Full Text Available Abstract Background Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. Results Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes and for all the interactions between them (edges. The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. Conclusions The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in S. cerevisiae. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.

  6. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  7. Protein networks in induced sputum from smokers and COPD patients

    Directory of Open Access Journals (Sweden)

    Baraniuk JN

    2015-09-01

    Full Text Available James N Baraniuk,1 Begona Casado,1 Lewis K Pannell,2 Peter B McGarvey,3 Piera Boschetto,4 Maurizio Luisetti,5,† Paolo Iadarola6 1Division of Rheumatology, Immunology and Allergy, Georgetown University, Washington, DC, 2Proteomics and Mass Spectrometry Laboratory, Mitchell Cancer Center, University of South Alabama, Mobile, AL, 3Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA; 4Department of Medical Sciences, University of Ferrara, Ferrara, 5SC Pneumologia, Dipartimento Medicina Molecolare, Fondazione IRCCS Policlinico San Matteo, 6Lazzaro Spallanzani Department of Biology and Biotechnology, University of Pavia, Pavia, Italy †Maurizio Luisetti passed away on October 20, 2014 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

  8. Protein-spanning water networks and implications for prediction of protein-protein interactions mediated through hydrophobic effects.

    Science.gov (United States)

    Cui, Di; Ou, Shuching; Patel, Sandeep

    2014-12-01

    Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self-assembly processes. Protein-protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein-protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy-based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue-based water density and single-linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces.

  9. The role of exon shuffling in shaping protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    França Gustavo S

    2010-12-01

    Full Text Available Abstract Background Physical protein-protein interaction (PPI is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains, self-interacting (able to interact with another copy of themselves and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

  10. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Directory of Open Access Journals (Sweden)

    Thomas Wallach

    2013-03-01

    Full Text Available Essentially all biological processes depend on protein-protein interactions (PPIs. Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc. contributing to temporal organization of cellular physiology in an unprecedented manner.

  11. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Science.gov (United States)

    Wallach, Thomas; Schellenberg, Katja; Maier, Bert; Kalathur, Ravi Kiran Reddy; Porras, Pablo; Wanker, Erich E; Futschik, Matthias E; Kramer, Achim

    2013-03-01

    Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner. PMID:23555304

  12. A membrane protein / signaling protein interaction network for Arabidopsis version AMPv2

    Directory of Open Access Journals (Sweden)

    Sylvie Lalonde

    2010-09-01

    Full Text Available Interactions between membrane proteins and the soluble fraction are essential for signal transduction and for regulating nutrient transport. To gain insights into the membrane-based interactome, 3,852 open reading frames (ORFs out of a target list of 8,383 representing membrane and signaling proteins from Arabidopsis thaliana were cloned into a Gateway compatible vector. The mating-based split-ubiquitin system was used to screen for potential protein-protein interactions (pPPIs among 490 Arabidopsis ORFs. A binary robotic screen between 142 receptor-like kinases, 72 transporters, 57 soluble protein kinases and phosphatases, 40 glycosyltransferases, 95 proteins of various functions and 89 proteins with unknown function detected 387 out of 90,370 possible PPIs. A secondary screen confirmed 343 (of 387 pPPIs between 179 proteins, yielding a scale-free network (r2=0.863. Eighty of 142 transmembrane receptor-like kinases (RLK tested positive, identifying three homomers, 63 heteromers and 80 pPPIs with other proteins. Thirty-one out of 142 RLK interactors (including RLKs had previously been found to be phosphorylated; thus interactors may be substrates for respective RLKs. None of the pPPIs described here had been reported in the major interactome databases, including potential interactors of G protein-coupled receptors, phospholipase C, and AMT ammonium transporters. Two RLKs found as putative interactors of AMT1;1 were independently confirmed using a split luciferase assay in Arabidopsis protoplasts. These RLKs may be involved in ammonium-dependent phosphorylation of the C-terminus and regulation of ammonium uptake activity. The robotic screening method established here will enable a systematic analysis of membrane protein interactions in fungi, plants and metazoa.

  13. Visualization of protein interaction networks: problems and solutions

    Directory of Open Access Journals (Sweden)

    Agapito Giuseppe

    2013-01-01

    Full Text Available Abstract Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins and edges (interactions, the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i technology, i.e. availability/license of the software and supported OS (Operating System platforms; (ii interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the

  14. Electron transfer in proteins.

    Science.gov (United States)

    Gray, H B; Winkler, J R

    1996-01-01

    Electron-transfer (ET) reactions are key steps in a diverse array of biological transformations ranging from photosynthesis to aerobic respiration. A powerful theoretical formalism has been developed that describes ET rates in terms of two parameters: the nuclear reorganization energy (lambda) and the electronic-coupling strength (HAB). Studies of ET reactions in ruthenium-modified proteins have probed lambda and HAB in several metalloproteins (cytochrome c, myoglobin, azurin). This work has shown that protein reorganization energies are sensitive to the medium surrounding the redox sites and that an aqueous environment, in particular, leads to large reorganization energies. Analyses of electronic-coupling strengths suggest that the efficiency of long-range ET depends on the protein secondary structure: beta sheets appear to mediate coupling more efficiently than alpha-helical structures, and hydrogen bonds play a critical role in both. PMID:8811189

  15. A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks.

    Science.gov (United States)

    Li, Min; Lu, Yu; Wang, Jianxin; Wu, Fang-Xiang; Pan, Yi

    2015-01-01

    Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

  16. Analysis of membrane proteins in metagenomics: networks of correlated environmental features and protein families.

    Science.gov (United States)

    Patel, Prianka V; Gianoulis, Tara A; Bjornson, Robert D; Yip, Kevin Y; Engelman, Donald M; Gerstein, Mark B

    2010-07-01

    Recent metagenomics studies have begun to sample the genomic diversity among disparate habitats and relate this variation to features of the environment. Membrane proteins are an intuitive, but thus far overlooked, choice in this type of analysis as they directly interact with the environment, receiving signals from the outside and transporting nutrients. Using global ocean sampling (GOS) data, we found nearly approximately 900,000 membrane proteins in large-scale metagenomic sequence, approximately a fifth of which are completely novel, suggesting a large space of hitherto unexplored protein diversity. Using GPS coordinates for the GOS sites, we extracted additional environmental features via interpolation from the World Ocean Database, the National Center for Ecological Analysis and Synthesis, and empirical models of dust occurrence. This allowed us to study membrane protein variation in terms of natural features, such as phosphate and nitrate concentrations, and also in terms of human impacts, such as pollution and climate change. We show that there is widespread variation in membrane protein content across marine sites, which is correlated with changes in both oceanographic variables and human factors. Furthermore, using these data, we developed an approach, protein families and environment features network (PEN), to quantify and visualize the correlations. PEN identifies small groups of covarying environmental features and membrane protein families, which we call "bimodules." Using this approach, we find that the affinity of phosphate transporters is related to the concentration of phosphate and that the occurrence of iron transporters is connected to the amount of shipping, pollution, and iron-containing dust.

  17. Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

    Directory of Open Access Journals (Sweden)

    Yashna Paul

    2016-01-01

    Full Text Available Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

  18. Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

    OpenAIRE

    Frank Emmert-Streib

    2012-01-01

    It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that the...

  19. Towards a map of the Populus biomass protein-protein interaction network

    Energy Technology Data Exchange (ETDEWEB)

    Beers, Eric; Brunner, Amy; Helm, Richard; Dickerman, Allan

    2016-08-31

    -depth characterizations. Characterizations involved both in vivo and in vitro independent methods to confirm protein-protein interactions and the evaluation of novel phenotypes resulting from creation of transgenic poplar and Arabidopsis plants engineered for increased or decreased expression of the selected genes. Transgenic poplar trees were studied in growth chamber, greenhouse, and two separate replicated field trials involving over 25 distinct wood-associated proteins. In-depth characterizations yielding positive results include the following. First, a NAC domain transcription factor (NAC154) that is a promoter of stress response and dormancy in trees was discovered. Increasing expression of NAC154 caused stunted growth and premature senescence, while decreasing expression led to both delayed bud and leaf expansion in spring and delayed leaf drop (i.e., prolonged leaf retention) in fall. Second, we discovered and characterized a new connection between a negative regulator of wood formation, the NAC domain transcription factor XND1, and an important regulator of cell division and cell differentiation, RBR. Third, we identified a new network of interacting wood-associated transcription factors belonging to the MYB and HD families. One of the HD family proteins, WOX13, was used to prepare transgenic poplar for high-level expression, resulting in significantly increased lateral branch growth. Finally, we modeled and performed in vitro analyses of the insect protein rubber resilin and we prepared transgenic Arabidopsis plants for expression of resilin to test the feasibility of using resilin to modify lignin cross-linking in wood and reduce recalcitrance and improve yield of fermentable sugars for biofuels production. Analysis of these and additional transgenics created with this support is continuing.

  20. Towards a map of the Populus biomass protein-protein interaction network

    Energy Technology Data Exchange (ETDEWEB)

    Beers, Eric [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Brunner, Amy [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Helm, Richard [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Dickerman, Allan [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

    2015-07-31

    -depth characterizations. Characterizations involved both in vivo and in vitro independent methods to confirm protein-protein interactions and the evaluation of novel phenotypes resulting from creation of transgenic poplar and Arabidopsis plants engineered for increased or decreased expression of the selected genes. Transgenic poplar trees were studied in growth chamber, greenhouse, and two separate replicated field trials involving over 25 distinct wood-associated proteins. In-depth characterizations yielding positive results include the following. First, a NAC domain transcription factor (NAC154) that is a promoter of stress response and dormancy in trees was discovered. Increasing expression of NAC154 caused stunted growth and premature senescence, while decreasing expression led to both delayed bud and leaf expansion in spring and delayed leaf drop (i.e., prolonged leaf retention) in fall. Second, we discovered and characterized a new connection between a negative regulator of wood formation, the NAC domain transcription factor XND1, and an important regulator of cell division and cell differentiation, RBR. Third, we identified a new network of interacting wood-associated transcription factors belonging to the MYB and HD families. One of the HD family proteins, WOX13, was used to prepare transgenic poplar for high-level expression, resulting in significantly increased lateral branch growth. Finally, we modeled and performed in vitro analyses of the insect protein rubber resilin and we prepared transgenic Arabidopsis plants for expression of resilin to test the feasibility of using resilin to modify lignin cross-linking in wood and reduce recalcitrance and improve yield of fermentable sugars for biofuels production. Analysis of these and additional transgenics created with this support is continuing.

  1. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis

    OpenAIRE

    Stevens, Adam; Meyer, Stefan; Hanson, Daniel; Clayton, Peter; Donn, Rachelle

    2014-01-01

    Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83...

  2. Illuminating spatial and temporal organization of protein interaction networks by mass spectrometry-based proteomics

    OpenAIRE

    Jiwen eYang; Sebastian Alexander Wagner; Petra eBeli

    2015-01-01

    Protein-protein interactions are at the core of all cellular functions and dynamic alterations in protein interactions regulate cellular signaling. In the last decade, mass spectrometry-based proteomics has delivered unprecedented insights into human protein interaction networks. Affinity purification-mass spectrometry has been extensively employed for focused and high-throughput studies of steady state protein-protein interactions. Future challenges remain in mapping transient protein intera...

  3. A novel functional module detection algorithm for protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zhang Aidong

    2006-12-01

    Full Text Available Abstract Background The sparse connectivity of protein-protein interaction data sets makes identification of functional modules challenging. The purpose of this study is to critically evaluate a novel clustering technique for clustering and detecting functional modules in protein-protein interaction networks, termed STM. Results STM selects representative proteins for each cluster and iteratively refines clusters based on a combination of the signal transduced and graph topology. STM is found to be effective at detecting clusters with a diverse range of interaction structures that are significant on measures of biological relevance. The STM approach is compared to six competing approaches including the maximum clique, quasi-clique, minimum cut, betweeness cut and Markov Clustering (MCL algorithms. The clusters obtained by each technique are compared for enrichment of biological function. STM generates larger clusters and the clusters identified have p-values that are approximately 125-fold better than the other methods on biological function. An important strength of STM is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches. Conclusion STM outperforms competing approaches and is capable of effectively detecting both densely and sparsely connected, biologically relevant functional modules with fewer discards.

  4. Palmitoylation and amyloid fibril formation of lung surfactant protein C

    OpenAIRE

    Gustafsson, Magnus

    2000-01-01

    Lung surfactant is a mixture of lipids and a few proteins, of which surfactant proteins (SP)-B and SP-C are lipophilic. Surfactant is essential for the reduction of surface tension at the alveolar air/liquid interface. The extremely hydrophobic SP-C is a 35-residue transmembraneous [alpha]-helical peptide containing a poly-Val stretch and two palmitoylated Cys residues. In this thesis the structural and functional importance of the SP-C palmitoyl groups and the poly-Val heli...

  5. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.

    Science.gov (United States)

    Basha, Omer; Flom, Dvir; Barshir, Ruth; Smoly, Ilan; Tirman, Shoval; Yeger-Lotem, Esti

    2015-07-01

    The identification of the molecular pathways active in specific contexts, such as disease states or drug responses, often requires an extensive view of the potential interactions between a subset of proteins. This view is not easily obtained: it requires the integration of context-specific protein list or expression data with up-to-date data of protein interactions that are typically spread across multiple databases. The MyProteinNet web server allows users to easily create such context-sensitive protein interaction networks. Users can automatically gather and consolidate data from up to 11 different databases to create a generic protein interaction network (interactome). They can score the interactions based on reliability and filter them by user-defined contexts including molecular expression and protein annotation. The output of MyProteinNet includes the generic and filtered interactome files, together with a summary of their network attributes. MyProteinNet is particularly geared toward building human tissue interactomes, by maintaining tissue expression profiles from multiple resources. The ability of MyProteinNet to facilitate the construction of up-to-date, context-specific interactomes and its applicability to 11 different organisms and to tens of human tissues, make it a powerful tool in meaningful analysis of protein networks. MyProteinNet is available at http://netbio.bgu.ac.il/myproteinnet.

  6. Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

    OpenAIRE

    H Billur Engin; Emre Guney; Ozlem Keskin; Baldo Oliva; Attila Gursoy

    2013-01-01

    Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer H. Billur Engin1, Emre Guney2, Ozlem Keskin1, Baldo Oliva2, Attila Gursoy1* 1 Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey, 2 Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra Abstract Blocking specific protein interactions can lead to human diseases. Accordingly, protein i...

  7. JiffyNet: a web-based instant protein network modeler for newly sequenced species.

    Science.gov (United States)

    Kim, Eiru; Kim, Hanhae; Lee, Insuk

    2013-07-01

    Revolutionary DNA sequencing technology has enabled affordable genome sequencing for numerous species. Thousands of species already have completely decoded genomes, and tens of thousands more are in progress. Naturally, parallel expansion of the functional parts list library is anticipated, yet genome-level understanding of function also requires maps of functional relationships, such as functional protein networks. Such networks have been constructed for many sequenced species including common model organisms. Nevertheless, the majority of species with sequenced genomes still have no protein network models available. Moreover, biologists might want to obtain protein networks for their species of interest on completion of the genome projects. Therefore, there is high demand for accessible means to automatically construct genome-scale protein networks based on sequence information from genome projects only. Here, we present a public web server, JiffyNet, specifically designed to instantly construct genome-scale protein networks based on associalogs (functional associations transferred from a template network by orthology) for a query species with only protein sequences provided. Assessment of the networks by JiffyNet demonstrated generally high predictive ability for pathway annotations. Furthermore, JiffyNet provides network visualization and analysis pages for wide variety of molecular concepts to facilitate network-guided hypothesis generation. JiffyNet is freely accessible at http://www.jiffynet.org.

  8. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    Directory of Open Access Journals (Sweden)

    Lei Hua

    2016-01-01

    Full Text Available The state-of-the-art methods for protein-protein interaction (PPI extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN and propose a shortest dependency path based CNN (sdpCNN model. The proposed method (1 only takes the sdp and word embedding as input and (2 could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

  9. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction.

    Science.gov (United States)

    Hua, Lei; Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

  10. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    Science.gov (United States)

    Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  11. Perturbation waves in proteins and protein networks: Applications of percolation and game theories in signaling and drug design

    CERN Document Server

    Antal, Miklos A; Csermely, Peter

    2008-01-01

    The network paradigm is increasingly used to describe the dynamics of complex systems. Here we review the current results and propose future development areas in the assessment of perturbation waves, i.e. propagating structural changes in amino acid networks building individual protein molecules and in protein-protein interaction networks (interactomes). We assess the possibilities and critically review the initial attempts for the application of game theory to the often rather complicated process, when two protein molecules approach each other, mutually adjust their conformations via multiple communication steps and finally, bind to each other. We also summarize available data on the application of percolation theory for the prediction of amino acid network- and interactome-dynamics. Furthermore, we give an overview of the dissection of signals and noise in the cellular context of various perturbations. Finally, we propose possible applications of the reviewed methodologies in drug design.

  12. Proteomic shifts in embryonic stem cells with gene dose modifications suggest the presence of balancer proteins in protein regulatory networks.

    Directory of Open Access Journals (Sweden)

    Lei Mao

    Full Text Available Large numbers of protein expression changes are usually observed in mouse models for neurodegenerative diseases, even when only a single gene was mutated in each case. To study the effect of gene dose alterations on the cellular proteome, we carried out a proteomic investigation on murine embryonic stem cells that either overexpressed individual genes or displayed aneuploidy over a genomic region encompassing 14 genes. The number of variant proteins detected per cell line ranged between 70 and 110, and did not correlate with the number of modified genes. In cell lines with single gene mutations, up and down-regulated proteins were always in balance in comparison to parental cell lines regarding number as well as concentration of differentially expressed proteins. In contrast, dose alteration of 14 genes resulted in an unequal number of up and down-regulated proteins, though the balance was kept at the level of protein concentration. We propose that the observed protein changes might partially be explained by a proteomic network response. Hence, we hypothesize the existence of a class of "balancer" proteins within the proteomic network, defined as proteins that buffer or cushion a system, and thus oppose multiple system disturbances. Through database queries and resilience analysis of the protein interaction network, we found that potential balancer proteins are of high cellular abundance, possess a low number of direct interaction partners, and show great allelic variation. Moreover, balancer proteins contribute more heavily to the network entropy, and thus are of high importance in terms of system resilience. We propose that the "elasticity" of the proteomic regulatory network mediated by balancer proteins may compensate for changes that occur under diseased conditions.

  13. ModuleRole: A Tool for Modulization, Role Determination and Visualization in Protein-Protein Interaction Networks

    OpenAIRE

    Guipeng Li; Ming Li; Yiwei Zhang; Dong Wang; Rong Li; Roger Guimerà; Juntao Tony Gao; Zhang, Michael Q

    2014-01-01

    Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network...

  14. Topology association analysis in weighted protein interaction network for gene prioritization

    Science.gov (United States)

    Wu, Shunyao; Shao, Fengjing; Zhang, Qi; Ji, Jun; Xu, Shaojie; Sun, Rencheng; Sun, Gengxin; Du, Xiangjun; Sui, Yi

    2016-11-01

    Although lots of algorithms for disease gene prediction have been proposed, the weights of edges are rarely taken into account. In this paper, the strengths of topology associations between disease and essential genes are analyzed in weighted protein interaction network. Empirical analysis demonstrates that compared to other genes, disease genes are weakly connected with essential genes in protein interaction network. Based on this finding, a novel global distance measurement for gene prioritization with weighted protein interaction network is proposed in this paper. Positive and negative flow is allocated to disease and essential genes, respectively. Additionally network propagation model is extended for weighted network. Experimental results on 110 diseases verify the effectiveness and potential of the proposed measurement. Moreover, weak links play more important role than strong links for gene prioritization, which is meaningful to deeply understand protein interaction network.

  15. Radioresistance related genes screened by protein-protein interaction network analysis in nasopharyngeal carcinoma

    International Nuclear Information System (INIS)

    Objective: To discover radioresistance associated molecular biomarkers and its mechanism in nasopharyngeal carcinoma by protein-protein interaction network analysis. Methods: Whole genome expression microarray was applied to screen out differentially expressed genes in two cell lines CNE-2R and CNE-2 with different radiosensitivity. Four differentially expressed genes were randomly selected for further verification by the semi-quantitative RT-PCR analysis with self-designed primers. The common differentially expressed genes from two experiments were analyzed with the SNOW online database in order to find out the central node related to the biomarkers of nasopharyngeal carcinoma radioresistance. The expression of STAT1 in CNE-2R and CNE-2 cells was measured by Western blot. Results: Compared with CNE-2 cells, 374 genes in CNE-2R cells were differentially expressed while 197 genes showed significant differences. Four randomly selected differentially expressed genes were verified by RT-PCR and had same change trend in consistent with the results of chip assay. Analysis with the SNOW database demonstrated that those 197 genes could form a complicated interaction network where STAT1 and JUN might be two key nodes. Indeed, the STAT1-α expression in CNE-2R was higher than that in CNE-2 (t=4.96, P<0.05). Conclusions: The key nodes of STAT1 and JUN may be the molecular biomarkers leading to radioresistance in nasopharyngeal carcinoma, and STAT1-α might have close relationship with radioresistance. (authors)

  16. Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer.

    Science.gov (United States)

    Latysheva, Natasha S; Oates, Matt E; Maddox, Louis; Flock, Tilman; Gough, Julian; Buljan, Marija; Weatheritt, Robert J; Babu, M Madan

    2016-08-18

    Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer. PMID:27540857

  17. Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

    Science.gov (United States)

    Lin, Peng-Lin; Yu, Ya-Wen; Chung, Ren-Hua

    2016-01-01

    Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn's disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn's disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net. PMID:27622767

  18. Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics*

    OpenAIRE

    Huang, Yuanpeng Janet; Hang, Dehua; Lu, Long Jason; Tong, Liang; Gerstein, Mark B; Montelione, Gaetano T.

    2008-01-01

    Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as “hubs” or “b...

  19. The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

    Directory of Open Access Journals (Sweden)

    Walther Dirk

    2008-11-01

    Full Text Available Abstract Background The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN. It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Results Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Conclusion Our results reveal topological equivalences between the protein

  20. Protein co-expression network analysis (ProCoNA)

    OpenAIRE

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

    2013-01-01

    Background 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. Results We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show...

  1. "Hot cores" in proteins: Comparative analysis of the apolar contact area in structures from hyper/thermophilic and mesophilic organisms

    Directory of Open Access Journals (Sweden)

    Bossa Francesco

    2008-02-01

    Full Text Available Abstract Background A wide variety of stabilizing factors have been invoked so far to elucidate the structural basis of protein thermostability. These include, amongst the others, a higher number of ion-pairs interactions and hydrogen bonds, together with a better packing of hydrophobic residues. It has been frequently observed that packing of hydrophobic side chains is improved in hyperthermophilic proteins, when compared to their mesophilic counterparts. In this work, protein crystal structures from hyper/thermophilic organisms and their mesophilic homologs have been compared, in order to quantify the difference of apolar contact area and to assess the role played by the hydrophobic contacts in the stabilization of the protein core, at high temperatures. Results The construction of two datasets was carried out so as to satisfy several restrictive criteria, such as minimum redundancy, resolution and R-value thresholds and lack of any structural defect in the collected structures. This approach allowed to quantify with relatively high precision the apolar contact area between interacting residues, reducing the uncertainty due to the position of atoms in the crystal structures, the redundancy of data and the size of the dataset. To identify the common core regions of these proteins, the study was focused on segments that conserve a similar main chain conformation in the structures analyzed, excluding the intervening regions whose structure differs markedly. The results indicated that hyperthermophilic proteins underwent a significant increase of the hydrophobic contact area contributed by those residues composing the alpha-helices of the structurally conserved regions. Conclusion This study indicates the decreased flexibility of alpha-helices in proteins core as a major factor contributing to the enhanced termostability of a number of hyperthermophilic proteins. This effect, in turn, may be due to an increased number of buried methyl groups in

  2. Modeling the two-hybrid detector: experimental bias on protein interaction networks.

    Science.gov (United States)

    Stibius, Karin B; Sneppen, Kim

    2007-10-01

    This work was done to investigate the two-hybrid experiment for finding protein-protein interactions to explain the asymmetry found in the experimental data, and to help screen the data for high confidence interactions. By looking at the bait-prey experimental setup the resulting protein interaction network can be examined as a directed network (bait --> prey). We have investigated two possible scenarios for the asymmetry in the directed network by developing a biochemical model for the protein-DNA and protein-protein bindings inside the living yeast. One scenario assumes a background activity of bait proteins acting even without the prey, the other scenario explores the asymmetry in the chemistry associated with the bait being automatically located in the right position on the DNA. We conclude that the latter model gives the best description of the observed asymmetry. PMID:17557786

  3. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

    Science.gov (United States)

    Mannakee, Brian K; Gutenkunst, Ryan N

    2016-07-01

    The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

  4. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

    Directory of Open Access Journals (Sweden)

    Brian K Mannakee

    2016-07-01

    Full Text Available The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

  5. FunMod: A Cytoscape Plugin for Identifying Functional Modules in Undirected Protein–Protein Networks

    Directory of Open Access Journals (Sweden)

    Massimo Natale

    2014-08-01

    Full Text Available 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.

  6. Similar pathogen targets in Arabidopsis thaliana and homo sapiens protein networks.

    Directory of Open Access Journals (Sweden)

    Paulo Shakarian

    Full Text Available We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform [Formula: see text]-shell decomposition analysis on these networks - which groups the proteins into various "shells" based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked.

  7. Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks

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    Boucher Charles AB

    2010-07-01

    Full Text Available Abstract Background The National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics. Results Our analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems. Conclusions HIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another.

  8. The organisational structure of protein networks: revisiting the centrality-lethality hypothesis.

    Science.gov (United States)

    Raman, Karthik; Damaraju, Nandita; Joshi, Govind Krishna

    2014-03-01

    Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality-lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality-lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality-lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.

  9. p42.3 gene expression in gastric cancer cell and its protein regulatory network analysis

    Directory of Open Access Journals (Sweden)

    Zhang Jianhua

    2012-12-01

    Full Text Available Abstract Background To analyze the p42.3 gene expression in gastric cancer (GC cell, find the relationship between protein structure and function, establish the regulatory network of p42.3 protein molecule and then to obtain the optimal regulatory pathway. Methods The expression of p42.3 gene was analyzed by RT-PCR, Western Blot and other biotechnologies. The relationship between the spatial conformation of p42.3 protein molecule and its function was analyzed using bioinformatics, MATLAB and related knowledge about protein structure and function. Furthermore, based on similarity algorithm of spatial layered spherical coordinate, we compared p42.3 molecule with several similar structured proteins which are known for the function, screened the characteristic nodes related to tumorigenesis and development, and established the multi variable relational model between p42.3 protein expression, cell cycle regulation and biological characteristics in the level of molecular regulatory networks. Finally, the optimal regulatory network was found by using Bayesian network. Results (1 The expression amount of p42.3 in G1 and M phase was higher than that in S and G2 phase; (2 The space coordinate systems of different structural domains of p42.3 protein were established in Matlab7.0 software; (3 The optimal pathway of p42.3 gene in protein regulatory network in gastric cancer is Ras protein, Raf-1 protein, MEK, MAPK kinase, MAPK, tubulin, spindle protein, centromere protein and tumor. Conclusion It is of vital significance for mechanism research to find out the action pathway of p42.3 in protein regulatory network, since p42.3 protein plays an important role in the generation and development of GC.

  10. Methods for the Analysis of Protein Phosphorylation–Mediated Cellular Signaling Networks

    Science.gov (United States)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

    Protein phosphorylation–mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

  11. The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Cherkasov Artem

    2008-09-01

    Full Text Available Abstract Background Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets. Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions. Results A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy. A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species. Conclusion The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed

  12. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

    Directory of Open Access Journals (Sweden)

    Mazo Ilya

    2007-07-01

    Full Text Available Abstract Background Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets. Results We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology

  13. A local average connectivity-based method for identifying essential proteins from the network level.

    Science.gov (United States)

    Li, Min; Wang, Jianxin; Chen, Xiang; Wang, Huan; Pan, Yi

    2011-06-01

    Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting protein essentiality from the network level. Essential proteins have been found to be more abundant among those highly connected proteins. However, there exist a number of highly connected proteins which are not essential. By analyzing these proteins, we find that few of their neighbors interact with each other. Thus, we propose a new local method, named LAC, to determine a protein's essentiality by evaluating the relationship between a protein and its neighbors. The performance of LAC is validated based on the yeast protein interaction networks obtained from two different databases: DIP and BioGRID. The experimental results of the two networks show that the number of essential proteins predicted by LAC clearly exceeds that explored by Degree Centrality (DC). More over, LAC is also compared with other seven measures of protein centrality (Neighborhood Component (DMNC), Betweenness Centrality (BC), Closeness Centrality (CC), Bottle Neck (BN), Information Centrality (IC), Eigenvector Centrality (EC), and Subgraph Centrality (SC)) in identifying essential proteins. The comparison results based on the validations of sensitivity, specificity, F-measure, positive predictive value, negative predictive value, and accuracy consistently show that LAC outweighs these seven previous methods. PMID:21704260

  14. Context-specific protein network miner--an online system for exploring context-specific protein interaction networks from the literature.

    Directory of Open Access Journals (Sweden)

    Rajesh Chowdhary

    Full Text Available BACKGROUND: Protein interaction networks (PINs specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation, location of interaction (i.e. tissues, cells, and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. RESULTS: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM, which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality, their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. CONCLUSIONS: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.

  15. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-12-01

    One of the core tasks of the emerging discipline of systems biology is the reconstruction of the various biological networks in an organism. The importance of understanding such regulatory, interaction, and signaling networks has fueled the development by bioinformatics researchers of many inference algorithms for determining their structure. The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment, testing, and improvement of algorithms used to reconstruct the structures of regulatory and interaction networks from high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN) tool, a software package for exploratory data analysis that allows basic integration and analysis of protein-protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. Thus, the combined SEBINI–CABIN platform aids in the more accurate determination of biological networks, in less time, with less effort. In this paper, we present a case study demonstrating the use of the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein-protein interaction networks from sets of mass spectrometry bait-prey experiment data. To the best of our knowledge the pipeline so designed is the first to be publicly available for such use. A demonstration web site for SEBINI can be accessed from https://www.emsl.pnl.gov/NIT/NIT.html. Source code and PostgreSQL database schema are available under open source license. Contact: ronald.taylor@pnl.gov. For commercial use, some algorithms included in SEBINI require licensing from the original developers. The

  16. On the Variability of Neural Network Classification Measures in the Protein Secondary Structure Prediction Problem

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    Eric Sakk

    2013-01-01

    Full Text Available We revisit the protein secondary structure prediction problem using linear and backpropagation neural network architectures commonly applied in the literature. In this context, neural network mappings are constructed between protein training set sequences and their assigned structure classes in order to analyze the class membership of test data and associated measures of significance. We present numerical results demonstrating that classifier performance measures can vary significantly depending upon the classifier architecture and the structure class encoding technique. Furthermore, an analytic formulation is introduced in order to substantiate the observed numerical data. Finally, we analyze and discuss the ability of the neural network to accurately model fundamental attributes of protein secondary structure.

  17. Crystal Structure of Menin Reveals Binding Site for Mixed Lineage Leukemia (MLL) Protein

    Energy Technology Data Exchange (ETDEWEB)

    Murai, Marcelo J.; Chruszcz, Maksymilian; Reddy, Gireesh; Grembecka, Jolanta; Cierpicki, Tomasz (Michigan); (UV)

    2014-10-02

    Menin is a tumor suppressor protein that is encoded by the MEN1 (multiple endocrine neoplasia 1) gene and controls cell growth in endocrine tissues. Importantly, menin also serves as a critical oncogenic cofactor of MLL (mixed lineage leukemia) fusion proteins in acute leukemias. Direct association of menin with MLL fusion proteins is required for MLL fusion protein-mediated leukemogenesis in vivo, and this interaction has been validated as a new potential therapeutic target for development of novel anti-leukemia agents. Here, we report the first crystal structure of menin homolog from Nematostella vectensis. Due to a very high sequence similarity, the Nematostella menin is a close homolog of human menin, and these two proteins likely have very similar structures. Menin is predominantly an {alpha}-helical protein with the protein core comprising three tetratricopeptide motifs that are flanked by two {alpha}-helical bundles and covered by a {beta}-sheet motif. A very interesting feature of menin structure is the presence of a large central cavity that is highly conserved between Nematostella and human menin. By employing site-directed mutagenesis, we have demonstrated that this cavity constitutes the binding site for MLL. Our data provide a structural basis for understanding the role of menin as a tumor suppressor protein and as an oncogenic co-factor of MLL fusion proteins. It also provides essential structural information for development of inhibitors targeting the menin-MLL interaction as a novel therapeutic strategy in MLL-related leukemias.

  18. Structure and inhibition of the SARS coronavirus envelope protein ion channel.

    Directory of Open Access Journals (Sweden)

    Konstantin Pervushin

    2009-07-01

    Full Text Available The envelope (E protein from coronaviruses is a small polypeptide that contains at least one alpha-helical transmembrane domain. Absence, or inactivation, of E protein results in attenuated viruses, due to alterations in either virion morphology or tropism. Apart from its morphogenetic properties, protein E has been reported to have membrane permeabilizing activity. Further, the drug hexamethylene amiloride (HMA, but not amiloride, inhibited in vitro ion channel activity of some synthetic coronavirus E proteins, and also viral replication. We have previously shown for the coronavirus species responsible for severe acute respiratory syndrome (SARS-CoV that the transmembrane domain of E protein (ETM forms pentameric alpha-helical bundles that are likely responsible for the observed channel activity. Herein, using solution NMR in dodecylphosphatidylcholine micelles and energy minimization, we have obtained a model of this channel which features regular alpha-helices that form a pentameric left-handed parallel bundle. The drug HMA was found to bind inside the lumen of the channel, at both the C-terminal and the N-terminal openings, and, in contrast to amiloride, induced additional chemical shifts in ETM. Full length SARS-CoV E displayed channel activity when transiently expressed in human embryonic kidney 293 (HEK-293 cells in a whole-cell patch clamp set-up. This activity was significantly reduced by hexamethylene amiloride (HMA, but not by amiloride. The channel structure presented herein provides a possible rationale for inhibition, and a platform for future structure-based drug design of this potential pharmacological target.

  19. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

    Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

  20. Semantic integration to identify overlapping functional modules in protein interaction networks

    Directory of Open Access Journals (Sweden)

    Ramanathan Murali

    2007-07-01

    Full Text Available Abstract Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules 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 functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, 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. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.

  1. CCHCR1 Interacts Specifically with the E2 Protein of Human Papillomavirus Type 16 on a Surface Overlapping BRD4 Binding

    OpenAIRE

    Mandy Muller; Caroline Demeret

    2014-01-01

    The Human Papillomavirus E2 proteins are key regulators of the viral life cycle, whose functions are commonly mediated through protein-protein interactions with the host cell proteome. We identified an interaction between E2 and a cellular protein called CCHCR1, which proved highly specific for the HPV16 genotype, the most prevalent in HPV-associated cancers. Further characterization of the interaction revealed that CCHCR1 binds the N-terminal alpha helices of HPV16 E2 N-terminal domain. On t...

  2. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

    Science.gov (United States)

    Mannakee, Brian K.; Gutenkunst, Ryan N.

    2016-01-01

    The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

  3. Landscape mapping of functional proteins in insulin signal transduction and insulin resistance: a network-based protein-protein interaction analysis.

    Directory of Open Access Journals (Sweden)

    Chiranjib Chakraborty

    Full Text Available The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins.

  4. Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Kyunghyun Park

    Full Text Available As pharmacodynamic drug-drug interactions (PD DDIs could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

  5. Defining the protein interaction network of human malaria parasite Plasmodium falciparum

    KAUST Repository

    Ramaprasad, Abhinay

    2012-02-01

    Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.

  6. Convolutional LSTM Networks for Subcellular Localization of Proteins

    DEFF Research Database (Denmark)

    Nielsen, Henrik; Sønderby, Søren Kaae; Sønderby, Casper Kaae;

    Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model...

  7. Convolutional LSTM Networks for Subcellular Localization of Proteins

    DEFF Research Database (Denmark)

    Sønderby, Søren Kaae; Sønderby, Casper Kaae; Nielsen, Henrik;

    2015-01-01

    Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) mod...

  8. Rigidity of transmembrane proteins determines their cluster shape

    CERN Document Server

    Jafarinia, Hamidreza; Jalali, Mir Abbas

    2015-01-01

    Protein aggregation in cell membrane is vital for majority of biological functions. Recent experimental results suggest that transmembrane domains of proteins such as $\\alpha$-helices and $\\beta$-sheets have different structural rigidity. We use molecular dynamics simulation of a coarse-grained model of protein-embedded lipid membranes to investigate the mechanisms of protein clustering. For a variety of protein concentrations, our simulations in thermal equilibrium conditions reveal that the structural rigidity of transmembrane domains dramatically affects interactions and changes the shape of the cluster. We have observed stable large aggregates even in the absence of hydrophobic mismatch which has been previously proposed as the mechanism of protein aggregation. According to our results, semi-flexible proteins aggregate to form two-dimensional clusters while rigid proteins, by contrast, form one-dimensional string-like structures. By assuming two probable scenarios for the formation of a two-dimensional tr...

  9. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    Energy Technology Data Exchange (ETDEWEB)

    Ba, Qian [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Li, Junyang; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wu, Yongning, E-mail: wuyongning@cfsa.net.cn [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

    2015-03-01

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.

  10. Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

    International Nuclear Information System (INIS)

    Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong to the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified

  11. [The study on the characters of membrane protein interaction and its network based on integrated intelligence method].

    Science.gov (United States)

    Shen, Yizhen; Ding, Yongsheng; Hao, Kuangrong

    2011-08-01

    Membrane protein and its interaction network have become a novel research direction in bioinformatics. In this paper, a novel membrane protein interaction network simulator is proposed for system biology studies by integrated intelligence method including spectrum analysis, fuzzy K-Nearest Neighbor(KNN) algorithm and so on. We consider biological system as a set of active computational components interacting with each other and with the external environment. Then we can use the network simulator to construct membrane protein interaction networks. Based on the proposed approach, we found that the membrane protein interaction network almost has some dynamic and collective characteristics, such as small-world network, scale free distributing, and hierarchical module structure. These properties are similar to those of other extensively studied protein interaction networks. The present studies on the characteristics of the membrane protein interaction network will be valuable for its relatively biological and medical studies. PMID:21936357

  12. [Interconnection between architecture of protein globule and disposition of conformational conservative oligopeptides in proteins from one protein family].

    Science.gov (United States)

    Batianovskiĭ, A V; Filatov, I V; Namiot, V A; Esipova, N G; Volotovskiĭ, I D

    2012-01-01

    It was shown that selective interactions between helical segments of macromolecules can realize in globular proteins in the segments characterized by the same periodicities of charge distribution i.e. between conformationally conservative oligopeptides. It was found that in the macromolecules of alpha-helical proteins conformationally conservative oligopeptides are disposed at a distance being characteristic of direct interactions. For representatives of many structural families of alpha-type proteins specific disposition of conformationally conservative segments is observed. This disposition is inherent to a particular structural family. Disposition of conformationally conservative segments is not related to homology of the amino acid sequence but reflects peculiarities of native 3D-architectures of protein globules.

  13. Small-World Effect of Complex Network and Its Application toProtein Folding

    Institute of Scientific and Technical Information of China (English)

    卢全国; 陈宝方; 彭华魁; 祖巧红

    2004-01-01

    The famous "six letters" experiment carried out by Milgram demonstrated the existence of small-world effect in a complex network. One vertex tends to be connected to another by a shortest path through network because of the small-world effect. This paper uses the small-world effect to study protein folding pathway.

  14. Emergence of Complexity in Protein Functions and Metabolic Networks

    Science.gov (United States)

    Pohorille, Andzej

    2009-01-01

    In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis of very large libraries of random amino acid sequences and subsequently subjecting them to in vitro evolution. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions, important clues have been uncovered. Considerable progress has been also achieved in understanding the origins of membrane proteins. We will address this issue in the example of ion channels - proteins that mediate transport of ions across cell walls. Remarkably, despite overall complexity of these proteins in contemporary cells, their structural motifs are quite simple, with -helices being most common. By combining results of experimental and computer simulation studies on synthetic models and simple, natural channels, I will show that, even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during

  15. Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution.

    Science.gov (United States)

    Shao, Mingyu; Zhou, Shuigeng; Guan, Jihong

    2015-08-01

    Protein-protein interaction (PPI) networks are crucial for organisms. Many research efforts have thus been devoted to the study on the topological properties and models of PPI networks. However, existing studies did not always report consistent results on the topological properties of PPI networks. Although a number of PPI network models have been introduced, yet in the literature there is no convincing conclusion on which model is best for describing PPI networks. This situation is primarily caused by the incompleteness of current PPI datasets. To solve this problem, in this study, the authors propose to revisit the topological properties and models of PPI networks from the perspective of PPI dataset evolution. Concretely, they used 12 PPI datasets of Arabidopsis thaliana and 10 PPI datasets of Saccharomyces cerevisiae from different Biological General Repository for Interaction Datasets (BioGRID) database versions, and compared the topological properties of these datasets and the fitting capabilities of five typical PPI network models over these datasets. PMID:26243826

  16. The Effect of Edge Definition of Complex Networks on Protein Structure Identification

    Directory of Open Access Journals (Sweden)

    Jing Sun

    2013-01-01

    Full Text Available The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when was around 5.0–7.5 Å, and the optimal cutoff value for constructing the protein structure networks was 5.0 Å ( distances while the ideal community division method was community structure detection based on edge betweenness in this study.

  17. Small-angle x-ray scattering studies of calmodulin mutants with deletions in the linker region of the central helix indicate that the linker region retains a predominantly. alpha. -helical conformation

    Energy Technology Data Exchange (ETDEWEB)

    Kataoka, Mikio; Engelman, D.M. (Yale Univ., New Haven, CT (USA)); Head, J.F. (Boston Univ., MA (USA)); Persechini, A.; Kretsinger, R.H. (Univ. of Virginia, Charlottesville (USA))

    1991-02-05

    Two mutant forms of calmodulin were examined by small-angle X-ray scattering in solution and compared with the wild-type protein. Each mutant has deletions in the linker region of the central helix: one lacks residues Glu-83 and Glu-84 (Des2) and the other lacks residues Ser-81 through Glu-84 (Des4). The deletions change both the radii of gyration and the maximum dimensions of the molecules. In the presence of Ca{sup 2+}, the observed radii of gyration are 22.4 {angstrom} for wild-type bacterially expressed calmodulin, 19.5 {angstrom} for Des2 calmodulin, and 20.3 {angstrom} for Des4 calmodulin. A reduction in the radius of gyration by 1-2 {angstrom} on removal of calcium, previously observed in the native protein, was also found in the wild type and the Des4 mutant; however, no significant size change was observed in the Des2 mutant. The large calcium-dependent conformational change in calmodulin induced by the binding of melittin was observed in all the bacterially expressed proteins. Each protein appears to undergo a transition from a dumbbell shape to a more globular conformation on binding melittin in the presence of calcium, although quantitatively the changes in the wild-type and Des4 proteins greatly exceed those in Des2. Modeling shows that the structural properties of the deletion mutants are well described by modifications of an {alpha} helix in the central linker region of the molecule. Thus, the structure of the linker region is stable enough to maintain the average orientation and separation of the lobes yet flexible enough to permit the lobes to approach each other upon binding a peptide.

  18. Protein network signatures associated with exogenous biofuels treatments in cyanobacterium Synechocystis sp. PCC 6803

    Directory of Open Access Journals (Sweden)

    Guangsheng ePei

    2014-11-01

    Full Text Available Although recognized as a promising microbial cell factory for producing biofuels, current productivity in cyanobacterial systems is low. To make the processes economically feasible, one of the hurdles which needs to be overcome is the low tolerance of hosts to toxic biofuels. Meanwhile, little information is available regarding the cellular responses to biofuels stress in cyanobacteria, which makes it challenging for tolerance engineering. Using large proteomic datasets of Synechocystis under various biofuels stress and environmental perturbation, a protein co-expression network was first constructed and then combined with the experimentally determined protein-protein interaction (PPI network. Proteins with statistically higher topological overlap in the integrated network were identified as common responsive proteins to both biofuels stress and environmental perturbations. In addition, a WGCNA network analysis was performed to distinguish unique responses to biofuels from those to environmental perturbations and to uncover metabolic modules and proteins uniquely associated with biofuels stress. The results showed that biofuel-specific proteins and modules were enriched in several functional categories, including photosynthesis, carbon fixation and amino acid metabolism, which may represent potential key signatures for biofuels stress responses in Synechocystis. Network-based analysis allowed determination of the responses specifically related to biofuels stress, and the results constituted an important knowledge foundation for tolerance engineering against biofuels in Synechocystis.

  19. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo

    2013-06-21

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

  20. A Method for Community Detection in Protein Networks Using Spectral Optimization

    Directory of Open Access Journals (Sweden)

    Sminu Izudheen

    2011-12-01

    Full Text Available Identification of community structures in complex networks has been a challenge in many domain and discipline. In protein networks these community interactions play a vital role in identifying the outcome of many cellular mechanisms. This paper reports the use of spectral optimization of triangular modularity as an effective method to identify these community structures. The algorithm has been carefully tested on real biological data and the results acknowledge that this is a powerful method for extracting community structures from protein networks.

  1. Scoring protein relationships in functional interaction networks predicted from sequence data.

    Directory of Open Access Journals (Sweden)

    Gaston K Mazandu

    Full Text Available UNLABELLED: The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. AVAILABILITY: Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.

  2. Reuse of structural domain–domain interactions in protein networks

    OpenAIRE

    Bateman Alex; Schuster-Böckler Benjamin

    2007-01-01

    Abstract Background Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases. Results We find that known structural domain interactions can only explain a subset of 4–19% of the available...

  3. The function of communities in protein interaction networks at multiple scales

    Directory of Open Access Journals (Sweden)

    Jones Nick S

    2010-07-01

    Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

  4. A human phenome-interactome network of protein complexes implicated in genetic disorders

    DEFF Research Database (Denmark)

    Hansen, Kasper Lage; Karlberg, Erik, Olof, Linnart; Størling, Zenia, Marian;

    2007-01-01

    We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, co...

  5. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David;

    2009-01-01

    in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may...... to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences...... be useful as an indicator of breast cancer prognosis....

  6. Redundancies in Large-scale Protein Interaction Networks

    CERN Document Server

    Samanta, M P; Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Understanding functional associations among genes discovered in sequencing projects is a key issue in post-genomic biology. However, reliable interpretation of the protein interaction data has been difficult. In this work, we show 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 more than 2800 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.

  7. Topological and functional properties of the small GTPases protein interaction network.

    Directory of Open Access Journals (Sweden)

    Anna Delprato

    Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

  8. Controlling for gene expression changes in transcription factor protein networks.

    Science.gov (United States)

    Banks, Charles A S; Lee, Zachary T; Boanca, Gina; Lakshminarasimhan, Mahadevan; Groppe, Brad D; Wen, Zhihui; Hattem, Gaye L; Seidel, Chris W; Florens, Laurence; Washburn, Michael P

    2014-06-01

    The development of affinity purification technologies combined with mass spectrometric analysis of purified protein mixtures has been used both to identify new protein-protein interactions and to define the subunit composition of protein complexes. Transcription factor protein interactions, however, have not been systematically analyzed using these approaches. Here, we investigated whether ectopic expression of an affinity tagged transcription factor as bait in affinity purification mass spectrometry experiments perturbs gene expression in cells, resulting in the false positive identification of bait-associated proteins when typical experimental controls are used. Using quantitative proteomics and RNA sequencing, we determined that the increase in the abundance of a set of proteins caused by overexpression of the transcription factor RelA is not sufficient for these proteins to then co-purify non-specifically and be misidentified as bait-associated proteins. Therefore, typical controls should be sufficient, and a number of different baits can be compared with a common set of controls. This is of practical interest when identifying bait interactors from a large number of different baits. As expected, we found several known RelA interactors enriched in our RelA purifications (NFκB1, NFκB2, Rel, RelB, IκBα, IκBβ, and IκBε). We also found several proteins not previously described in association with RelA, including the small mitochondrial chaperone Tim13. Using a variety of biochemical approaches, we further investigated the nature of the association between Tim13 and NFκB family transcription factors. This work therefore provides a conceptual and experimental framework for analyzing transcription factor protein interactions.

  9. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.;

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA...

  10. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E;

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA...

  11. Virus host protein interaction network analysis reveals that the HEV ORF3 protein may interrupt the blood coagulation process.

    Directory of Open Access Journals (Sweden)

    Yansheng Geng

    Full Text Available Hepatitis E virus (HEV is endemic worldwide and a major cause of acute liver disease in developing countries. However, the molecular mechanisms of liver pathology and clinical disease are not well understood for HEV infection. Open reading frame 3 (ORF3 of HEV encodes a small phosphoprotein, which is assumed to be involved in liver pathology and clinical disease. In this study, the interactions between the HEV ORF3 protein and human proteins were investigated using a stringent, high-throughput yeast two-hybrid (Y2H analysis. Thirty two proteins were shown to interact with genotype 1 ORF3, 28 of which have not been reported previously. These novel interactions were evaluated by coimmunoprecipitation of protein complexes from transfected cells. We found also that the ORF3 proteins of genotype 4 and rabbit HEV interacted with all of the human proteins identified by the genotype 1 ORF3 protein. However, the putative ORF3 protein derived from avian HEV did not interact with the majority of these human proteins. The identified proteins were used to infer an overall interaction map linking the ORF3 protein with components of the host cellular networks. Analysis of this interaction map, based on functional annotation with the Gene Ontology features and KEGG pathways, revealed an enrichment of host proteins involved in complement coagulation, cellular iron ion homeostasis and oxidative stress. Additional canonical pathway analysis highlighted the enriched biological pathways relevant to blood coagulation and hemostasis. Consideration of the clinical manifestations of hepatitis E reported previously and the results of biological analysis from this study suggests that the ORF3 protein is likely to lead to an imbalance of coagulation and fibrinolysis by interacting with host proteins and triggering the corresponding pathological processes. These results suggest critical approaches to further study of the pathogenesis of the HEV ORF3 protein.

  12. Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality.

    Directory of Open Access Journals (Sweden)

    Elena Zotenko

    Full Text Available The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein-protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model.

  13. Intracellular Trafficking Network of Protein Nanocapsules: Endocytosis, Exocytosis and Autophagy

    Science.gov (United States)

    Zhang, Jinxie; Zhang, Xudong; Liu, Gan; Chang, Danfeng; Liang, Xin; Zhu, Xianbing; Tao, Wei; Mei, Lin

    2016-01-01

    The inner membrane vesicle system is a complex transport system that includes endocytosis, exocytosis and autophagy. However, the details of the intracellular trafficking pathway of nanoparticles in cells have been poorly investigated. Here, we investigate in detail the intracellular trafficking pathway of protein nanocapsules using more than 30 Rab proteins as markers of multiple trafficking vesicles in endocytosis, exocytosis and autophagy. We observed that FITC-labeled protein nanoparticles were internalized by the cells mainly through Arf6-dependent endocytosis and Rab34-mediated micropinocytosis. In addition to this classic pathway: early endosome (EEs)/late endosome (LEs) to lysosome, we identified two novel transport pathways: micropinocytosis (Rab34 positive)-LEs (Rab7 positive)-lysosome pathway and EEs-liposome (Rab18 positive)-lysosome pathway. Moreover, the cells use slow endocytosis recycling pathway (Rab11 and Rab35 positive vesicles) and GLUT4 exocytosis vesicles (Rab8 and Rab10 positive) transport the protein nanocapsules out of the cells. In addition, protein nanoparticles are observed in autophagosomes, which receive protein nanocapsules through multiple endocytosis vesicles. Using autophagy inhibitor to block these transport pathways could prevent the degradation of nanoparticles through lysosomes. Using Rab proteins as vesicle markers to investigation the detail intracellular trafficking of the protein nanocapsules, will provide new targets to interfere the cellular behaver of the nanoparticles, and improve the therapeutic effect of nanomedicine. PMID:27698943

  14. Reuse of structural domain–domain interactions in protein networks

    Directory of Open Access Journals (Sweden)

    Bateman Alex

    2007-07-01

    Full Text Available Abstract Background Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases. Results We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between E. coli, S. cerevisiae and H. sapiens. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor. Conclusion Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.

  15. Gene expression profiles on predicting protein interaction network and exploring of new treatments for lung cancer.

    Science.gov (United States)

    Yang, Zehui; Zheng, Rui; Gao, Yuan; Zhang, Qiang

    2014-12-01

    In the present study, we aimed to explore disease-associated genes and their functions in lung cancer. We downloaded the gene expression profile GSE4115 from Gene Expression Omnibus (GEO) database. Total 97 lung cancer and 90 adjacent non-tumor lung tissue (normal) samples were applied to identify the differentially expressed genes (DEGs) by paired t test and variance analysis in spectral angle mapper (SAM) package in R. Gene Ontology (GO) functional enrichment analysis of DEGs were performed with Database for Annotation Visualization and Integrated Discovery, followed by construction of protein-protein interaction (PPI) network from Human Protein Reference Database (HPRD). Finally, network modules were analyzed by the MCODE algorithm to detect protein complexes in the PPI network. Total 3,102 genes were identified as DEGs at FDR normal and cancer tissues, and exploring new treatments for lung cancer. PMID:25205123

  16. Mining protein interactomes to improve their reliability and support the advancement of network medicine

    KAUST Repository

    Alanis-Lobato, Gregorio

    2015-09-23

    High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.

  17. Prediction of Protein Thermostability by an Efficient Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Jalal Rezaeenour

    2016-10-01

    Full Text Available Introduction: Manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. Various data mining techniques exist for prediction of thermostable proteins. Furthermore, ANN methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate approach to mapping the non-linear input-output relationships and massive parallel computing. Method: An Extreme Learning Machine (ELM was applied to estimate thermal behavior of 1289 proteins. In the proposed algorithm, the parameters of ELM were optimized using a Genetic Algorithm (GA, which tuned a set of input variables, hidden layer biases, and input weights, to and enhance the prediction performance. The method was executed on a set of amino acids, yielding a total of 613 protein features. A number of feature selection algorithms were used to build subsets of the features. A total of 1289 protein samples and 613 protein features were calculated from UniProt database to understand features contributing to the enzymes’ thermostability and find out the main features that influence this valuable characteristic. Results:At the primary structure level, Gln, Glu and polar were the features that mostly contributed to protein thermostability. At the secondary structure level, Helix_S, Coil, and charged_Coil were the most important features affecting protein thermostability. These results suggest that the thermostability of proteins is mainly associated with primary structural features of the protein. According to the results, the influence of primary structure on the thermostabilty of a protein was more important than that of the secondary structure. It is shown that prediction accuracy of ELM (mean square error can improve dramatically using GA with error rates RMSE=0.004 and MAPE=0.1003. Conclusion: The proposed approach for forecasting problem

  18. Prediction of beta-turns in proteins using neural networks.

    Science.gov (United States)

    McGregor, M J; Flores, T P; Sternberg, M J

    1989-05-01

    The use of neural networks to improve empirical secondary structure prediction is explored with regard to the identification of the position and conformational class of beta-turns, a four-residue chain reversal. Recently an algorithm was developed for beta-turn predictions based on the empirical approach of Chou and Fasman using different parameters for three classes (I, II and non-specific) of beta-turns. In this paper, using the same data, an alternative approach to derive an empirical prediction method is used based on neural networks which is a general learning algorithm extensively used in artificial intelligence. Thus the results of the two approaches can be compared. The most severe test of prediction accuracy is the percentage of turn predictions that are correct and the neural network gives an overall improvement from 20.6% to 26.0%. The proportion of correctly predicted residues is 71%, compared to a chance level of about 58%. Thus neural networks provide a method of obtaining more accurate predictions from empirical data than a simpler method of deriving propensities. PMID:2748568

  19. The architectural network for protein secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Anindya Sundar Panja

    2016-07-01

    Full Text Available Over the past 25 years, the accuracy of proteins secondary structure prediction has improved substantially. Recently evolutionary information taken from the deviation of proteins in some structural family have again enhance prediction accuracy for all these residues predicted correctly is in one of the three sates helix, strands and others . The new methods developed over the past few years may be interesting in context of improvements which is achieved through combination of the existing methods. Evolutionary divergences profile posses’ adequate information to improve protein secondary structure prediction accuracy. These profiles can also able to correctly predict long stretches of identical residues in other secondary structure. This sequence structure relationship may help to help to developed tool which can efficiently predict the protein secondary structure from its amino acid sequence

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

    Science.gov (United States)

    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 non-incorporated protein in concert with numerous insect and plant proteins to regulate virus movem...

  1. Gene, protein, and network of male sterility in rice

    OpenAIRE

    WANG Kun; Peng, Xiaojue; Ji, Yanxiao; Yang, Pingfang; Zhu, Yingguo; LI, SHAOQING

    2013-01-01

    Rice is one of the most important model crop plants whose heterosis has been well-exploited in commercial hybrid seed production via a variety of types of male-sterile lines. Hybrid rice cultivation area is steadily expanding around the world, especially in Southern Asia. Characterization of genes and proteins related to male sterility aims to understand how and why the male sterility occurs, and which proteins are the key players for microspores abortion. Recently, a series of genes and prot...

  2. On relationships between surfactant type and globular proteins interactions in solution.

    Science.gov (United States)

    Blanco, Elena; Ruso, Juan M; Prieto, Gerardo; Sarmiento, Félix

    2007-12-01

    The binding of sodium perfluorooctanoate (C8FONa), sodium octanoate (C8HONa), lithium perfluorooctanoate (C8FOLi), and sodium dodecanoate (C12HONa) onto myoglobin, ovalbumin, and catalase in water has been characterized using electrophoretic mobility. The tendency of the protein-surfactant complexes to change their charge in the order catalase < ovalbumin < myoglobin was observed which was related to the contents of alpha-helices in the proteins. alpha-Helices are more hydrophobic than beta-sheets. The effect of surfactant on the zeta potentials follows C8HONa < C8FONa < C8FOLi < C12HONa for catalase and ovalbumin; and C8HONa < C8FOLi < C8FONa < C12HONa for myoglobin. The numbers of binding sites on the proteins were determined from the observed increases of the zeta-potential as a function of surfactant concentration in the regions where the binding was a consequence of the hydrophobic effect. The Gibbs energies of binding of the surfactants onto the proteins were evaluated. For all systems, Gibbs energies are negative and large at low concentrations (where binding to the high energy sites takes place) and become less negative at higher ones. This fact suggests a saturation process. Changes in Gibbs energies with the different proteins and surfactants under study have been found to follow same sequence than that found for the charge. The role of hydrophobic interactions in these systems has been demonstrated to be the predominant.

  3. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  4. Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation.

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Ma

    Full Text Available A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.

  5. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

  6. Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

    Directory of Open Access Journals (Sweden)

    Mingzhu Zhao

    2013-01-01

    Full Text Available The traditional Chinese medicine (TCM, which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

  7. A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.

    Directory of Open Access Journals (Sweden)

    Julie Baussand

    2009-09-01

    Full Text Available Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.

  8. Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

    Energy Technology Data Exchange (ETDEWEB)

    Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.; Zane, G.M.; Price, M.N.; Gaucher, S.; Reveco, S.A.; Fok, V.; Johanson, A.R.; Batth, T.S.; Singer, M.; Chandonia, J.M.; Joyner, D.; Hazen, T.C.; Arkin, A.P.; Wall, J.D.; Singh, A.K.; Keasling, J.D.

    2011-05-01

    Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to the carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

  9. Prediction of protein hydration sites from sequence by modular neural networks

    DEFF Research Database (Denmark)

    Ehrlich, L.; Reczko, M.; Bohr, Henrik;

    1998-01-01

    The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with t...... provide insight into the mutual interdependencies between the location of ordered water sites and the structural and chemical characteristics of the protein residues.......The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two...... structure and solvent accessibility and, using actual values of these properties, redidue hydration can be predicted to 77% accuracy with a Metthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed...

  10. Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

    Directory of Open Access Journals (Sweden)

    Shubhada R Hegde

    2008-11-01

    Full Text Available Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

  11. Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma.

    Science.gov (United States)

    Han, Yuewen; Niu, Jun; Wang, Dong; Li, Yuanyuan

    2016-01-01

    Epidemiological studies have validated the association between hepatitis C virus (HCV) infection and hepatocellular carcinoma (HCC). An increasing number of studies show that protein-protein interactions (PPIs) between HCV proteins and host proteins play a vital role in infection and mediate HCC progression. In this work, we collected all published interaction between HCV and human proteins, which include 455 unique human proteins participating in 524 HCV-human interactions. Then, we construct the HCV-human and HCV-HCC protein interaction networks, which display the biological knowledge regarding the mechanism of HCV pathogenesis, particularly with respect to pathogenesis of HCC. Through in-depth analysis of the HCV-HCC interaction network, we found that interactors are enriched in the JAK/STAT, p53, MAPK, TNF, Wnt, and cell cycle pathways. Using a random walk with restart algorithm, we predicted the importance of each protein in the HCV-HCC network and found that AKT1 may play a key role in the HCC progression. Moreover, we found that NS5A promotes HCC cells proliferation and metastasis by activating AKT/GSK3β/β-catenin pathway. This work provides a basis for a detailed map tracking new cellular interactions of HCV and identifying potential targets for HCV-related hepatocellular carcinoma treatment. PMID:27115606

  12. Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma.

    Directory of Open Access Journals (Sweden)

    Yuewen Han

    Full Text Available Epidemiological studies have validated the association between hepatitis C virus (HCV infection and hepatocellular carcinoma (HCC. An increasing number of studies show that protein-protein interactions (PPIs between HCV proteins and host proteins play a vital role in infection and mediate HCC progression. In this work, we collected all published interaction between HCV and human proteins, which include 455 unique human proteins participating in 524 HCV-human interactions. Then, we construct the HCV-human and HCV-HCC protein interaction networks, which display the biological knowledge regarding the mechanism of HCV pathogenesis, particularly with respect to pathogenesis of HCC. Through in-depth analysis of the HCV-HCC interaction network, we found that interactors are enriched in the JAK/STAT, p53, MAPK, TNF, Wnt, and cell cycle pathways. Using a random walk with restart algorithm, we predicted the importance of each protein in the HCV-HCC network and found that AKT1 may play a key role in the HCC progression. Moreover, we found that NS5A promotes HCC cells proliferation and metastasis by activating AKT/GSK3β/β-catenin pathway. This work provides a basis for a detailed map tracking new cellular interactions of HCV and identifying potential targets for HCV-related hepatocellular carcinoma treatment.

  13. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available BACKGROUND: Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners. METHODOLOGY: Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques. CONCLUSIONS: Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions. AVAILABILITY: We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  14. Insights into biological information processing: structural and dynamical analysis of a human protein signalling network

    Energy Technology Data Exchange (ETDEWEB)

    Fuente, Alberto de la; Fotia, Giorgio; Maggio, Fabio; Mancosu, Gianmaria; Pieroni, Enrico [CRS4 Bioinformatica, Parco Tecnologico POLARIS, Ed.1, Loc Piscinamanna, Pula (Italy)], E-mail: alf@crs4.it

    2008-06-06

    We present an investigation on the structural and dynamical properties of a 'human protein signalling network' (HPSN). This biological network is composed of nodes that correspond to proteins and directed edges that represent signal flows. In order to gain insight into the organization of cell information processing this network is analysed taking into account explicitly the edge directions. We explore the topological properties of the HPSN at the global and the local scale, further applying the generating function formalism to provide a suitable comparative model. The relationship between the node degrees and the distribution of signals through the network is characterized using degree correlation profiles. Finally, we analyse the dynamical properties of small sub-graphs showing high correlation between their occurrence and dynamic stability.

  15. Heteronuclear 2D-correlations in a uniformly [13C, 15N] labeled membrane-protein complex at ultra-high magnetic fields

    Energy Technology Data Exchange (ETDEWEB)

    Egorova-Zachernyuk, T.A.; Hollander, J. [Gorlaeus Laboratories (Netherlands); Fraser, N. [University of Glasgow, Division of Biochemistry and Molecular Biology (United Kingdom); Gast, P.; Hoff, A.J. [Leiden University, Huygens Laboratories (Netherlands); Cogdell, R. [University of Glasgow, Division of Biochemistry and Molecular Biology (United Kingdom); Groot, H.J.M. de; Baldus, M. [Gorlaeus Laboratories (Netherlands)

    2001-03-15

    One- and two-dimensional solid-state NMR experiments on a uniformly labeled intrinsic membrane-protein complex at ultra-high magnetic fields are presented. Two-dimensional backbone and side-chain correlations for a [U-{sup 13}C,{sup 15}N] labeled version of the LH2 light-harvesting complex indicate significant resolution at low temperatures and under Magic Angle Spinning. Tentative assignments of some of the observed correlations are presented and attributed to the {alpha}-helical segments of the protein, mostly found in the membrane interior.

  16. BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks

    OpenAIRE

    Oksana Kohutyuk; Fadi Towfic; M. Heather West Greenlee; Vasant Honavar

    2012-01-01

    Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We...

  17. Protein variants form a system of networks: microdiversity of IMP metallo-beta-lactamases.

    Directory of Open Access Journals (Sweden)

    Michael Widmann

    Full Text Available Genome and metagenome sequencing projects support the view that only a tiny portion of the total protein microdiversity in the biosphere has been sequenced yet, while the vast majority of existing protein variants is still unknown. By using a network approach, the microdiversity of 42 metallo-β-lactamases of the IMP family was investigated. In the networks, the nodes are formed by the variants, while the edges correspond to single mutations between pairs of variants. The 42 variants were assigned to 7 separate networks. By analyzing the networks and their relationships, the structure of sequence space was studied and existing, but still unknown, functional variants were predicted. The largest network consists of 10 variants with IMP-1 in its center and includes two ubiquitous mutations, V67F and S262G. By relating the corresponding pairs of variants, the networks were integrated into a single system of networks. The largest network also included a quartet of variants: IMP-1, two single mutants, and the respective double mutant. The existence of quartets indicates that if two mutations resulted in functional enzymes, the double mutant may also be active and stable. Therefore, quartet construction from triplets was applied to predict 15 functional variants. Further functional mutants were predicted by applying the two ubiquitous mutations in all networks. In addition, since the networks are separated from each other by 10-15 mutations on average, it is expected that a subset of the theoretical intermediates are functional, and therefore are supposed to exist in the biosphere. Finally, the network analysis helps to distinguish between epistatic and additive effects of mutations; while the presence of correlated mutations indicates a strong interdependency between the respective positions, the mutations V67F and S262G are ubiquitous and therefore background independent.

  18. ProtNet: a tool for stochastic simulations of protein interaction networks dynamics

    Science.gov (United States)

    Bernaschi, Massimo; Castiglione, Filippo; Ferranti, Alessandra; Gavrila, Caius; Tinti, Michele; Cesareni, Gianni

    2007-01-01

    Background Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell. Results To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell. We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and

  19. Relationships between predicted moonlighting proteins, human diseases and comorbidities from a network perspective.

    Directory of Open Access Journals (Sweden)

    Andreas eZanzoni

    2015-06-01

    Full Text Available Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i they are significantly involved in more than one disease, (ii they contribute to complex rather than monogenic diseases, (iii the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

  20. Functional protein networks unifying limb girdle muscular dystrophy

    NARCIS (Netherlands)

    Morrée, Antoine de

    2011-01-01

    Limb Girdle Muscular Dystrophy (LGMD) is a rare progressive heterogeneous disorder that can be caused by mutations in at least 21 different genes. These genes are often widely expressed and encode proteins with highly differing functions. And yet mutations in all of them give rise to a similar clini

  1. Mass-action equilibrium and non-specific interactions in protein binding networks

    Science.gov (United States)

    Maslov, Sergei

    2009-03-01

    Large-scale protein binding networks serve as a paradigm of complex properties of living cells. These networks are naturally weighted with edges characterized by binding strength and protein-nodes -- by their concentrations. However, the state-of-the-art high-throughput experimental techniques generate just a binary (yes or no) information about individual interactions. As a result, most of the previous research concentrated just on topology of these networks. In a series of recent publications [1-4] my collaborators and I went beyond purely topological studies and calculated the mass-action equilibrium of a genome-wide binding network using experimentally determined protein concentrations, localizations, and reliable binding interactions in baker's yeast. We then studied how this equilibrium responds to large perturbations [1-2] and noise [3] in concentrations of proteins. We demonstrated that the change in the equilibrium concentration of a protein exponentially decays (and sign-alternates) with its network distance away from the perturbed node. This explains why, despite a globally connected topology, individual functional modules in such networks are able to operate fairly independently. In a separate study [4] we quantified the interplay between specific and non-specific binding interactions under crowded conditions inside living cells. We show how the need to limit the waste of resources constrains the number of types and concentrations of proteins that are present at the same time and at the same place in yeast cells. [1] S Maslov, I. Ispolatov, PNAS 104:13655 (2007). [2] S. Maslov, K. Sneppen, I. Ispolatov, New J. of Phys. 9: 273 (2007). [3] K-K. Yan, D. Walker, S. Maslov, PRL accepted (2008). [4] J. Zhang, S. Maslov, and E. I. Shakhnovich, Mol Syst Biol 4, 210 (2008).

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Yongbin Dong

    Full Text Available 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.

  4. Building and analyzing protein interactome networks by cross-species comparisons

    Directory of Open Access Journals (Sweden)

    Blackman Barron

    2010-03-01

    Full Text Available Abstract Background A genomic catalogue of protein-protein interactions is a rich source of information, particularly for exploring the relationships between proteins. Numerous systems-wide and small-scale experiments have been conducted to identify interactions; however, our knowledge of all interactions for any one species is incomplete, and alternative means to expand these network maps is needed. We therefore took a comparative biology approach to predict protein-protein interactions across five species (human, mouse, fly, worm, and yeast and developed InterologFinder for research biologists to easily navigate this data. We also developed a confidence score for interactions based on available experimental evidence and conservation across species. Results The connectivity of the resultant networks was determined to have scale-free distribution, small-world properties, and increased local modularity, indicating that the added interactions do not disrupt our current understanding of protein network structures. We show examples of how these improved interactomes can be used to analyze a genome-scale dataset (RNAi screen and to assign new function to proteins. Predicted interactions within this dataset were tested by co-immunoprecipitation, resulting in a high rate of validation, suggesting the high quality of networks produced. Conclusions Protein-protein interactions were predicted in five species, based on orthology. An InteroScore, a score accounting for homology, number of orthologues with evidence of interactions, and number of unique observations of interactions, is given to each known and predicted interaction. Our website http://www.interologfinder.org provides research biologists intuitive access to this data.

  5. Membrane tubule formation by banana-shaped proteins with or without transient network structure

    Science.gov (United States)

    Noguchi, Hiroshi

    2016-02-01

    In living cells, membrane morphology is regulated by various proteins. Many membrane reshaping proteins contain a Bin/Amphiphysin/Rvs (BAR) domain, which consists of a banana-shaped rod. The BAR domain bends the biomembrane along the rod axis and the features of this anisotropic bending have recently been studied. Here, we report on the role of the BAR protein rods in inducing membrane tubulation, using large-scale coarse-grained simulations. We reveal that a small spontaneous side curvature perpendicular to the rod can drastically alter the tubulation dynamics at high protein density, whereas no significant difference is obtained at low density. A percolated network is intermediately formed depending on the side curvature. This network suppresses tubule protrusion, leading to the slow formation of fewer tubules. Thus, the side curvature, which is generated by protein-protein and membrane-protein interactions, plays a significant role in tubulation dynamics. We also find that positive surface tensions and the vesicle membrane curvature can stabilize this network structure by suppressing the tubulation.

  6. Revisiting date and party hubs: novel approaches to role assignment in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Sumeet Agarwal

    2010-06-01

    Full Text Available The idea of "date" and "party" hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here, we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins. We suggest that thinking in terms of a date/party dichotomy for hubs in protein interaction networks is not meaningful, and it might be more useful to conceive of roles for protein-protein interactions rather than for individual proteins.

  7. Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

    Science.gov (United States)

    Smith, Stephen E P; Neier, Steven C; Reed, Brendan K; Davis, Tessa R; Sinnwell, Jason P; Eckel-Passow, Jeanette E; Sciallis, Gabriel F; Wieland, Carilyn N; Torgerson, Rochelle R; Gil, Diana; Neuhauser, Claudia; Schrum, Adam G

    2016-08-02

    Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.

  8. Multiplex matrix network analysis of protein complexes in the human TCR signalosome.

    Science.gov (United States)

    Smith, Stephen E P; Neier, Steven C; Reed, Brendan K; Davis, Tessa R; Sinnwell, Jason P; Eckel-Passow, Jeanette E; Sciallis, Gabriel F; Wieland, Carilyn N; Torgerson, Rochelle R; Gil, Diana; Neuhauser, Claudia; Schrum, Adam G

    2016-01-01

    Multiprotein complexes transduce cellular signals through extensive interaction networks, but the ability to analyze these networks in cells from small clinical biopsies is limited. To address this, we applied an adaptable multiplex matrix system to physiologically relevant signaling protein complexes isolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR) signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposed surface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells, this system produced high-dimensional data that enabled visualization of network activity. A comprehensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchical clustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis, and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skin punch biopsies from control patients or patients with the autoimmune skin disease alopecia areata. This analysis distinguished disease patients from the controls, detected enhanced basal TCR signaling in the autoimmune patients, and identified a potential signaling network signature that may be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach that can provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies. PMID:27485017

  9. A Study on Protein Residue Contacts Prediction by Recurrent Neural Network

    Institute of Scientific and Technical Information of China (English)

    Liu Gui-xia; Zhu Yuan-xian; Zhou Wen-gang; Huang Yan-xin; Zhou Chun-guang; Wang Rong-xing

    2005-01-01

    A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins.The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar,acidic, basic and secondary structure information and residue separation between two residues. In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0. 29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.

  10. Dissecting spatio-temporal protein networks driving human heart development and related disorders

    DEFF Research Database (Denmark)

    Hansen, Kasper Lage; Mølgård, Kjeld; Greenway, Steven;

    2010-01-01

    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...... 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...

  11. ModuleRole: a tool for modulization, role determination and visualization in protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Guipeng Li

    Full Text Available Rapidly increasing amounts of (physical and genetic protein-protein interaction (PPI data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by "User Guide" in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user's own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data is also available at this website. API for ModuleRole used for this

  12. A mechanism of protein-mediated fusion: coupling between refolding of the influenza hemagglutinin and lipid rearrangements.

    OpenAIRE

    Kozlov, M M; Chernomordik, L V

    1998-01-01

    Although membrane fusion mediated by influenza virus hemagglutinin (HA) is the best characterized example of ubiquitous protein-mediated fusion, it is still not known how the low-pH-induced refolding of HA trimers causes fusion. This refolding involves 1) repositioning of the hydrophobic N-terminal sequence of the HA2 subunit of HA ("fusion peptide"), and 2) the recruitment of additional residues to the alpha-helical coiled coil of a rigid central rod of the trimer. We propose here a mechanis...

  13. Modification of gene duplicability during the evolution of protein interaction network.

    Directory of Open Access Journals (Sweden)

    Matteo D'Antonio

    2011-04-01

    Full Text Available Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.

  14. Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

    Science.gov (United States)

    Azevedo, Hátylas; Moreira-Filho, Carlos Alberto

    2015-11-01

    Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed functional modules related to DNA repair, immunity, apoptosis, cell stress, proliferation and migration. Subsequently, network vulnerability was assessed by means of centrality-based attacks based on the removal of node fractions in descending orders of degree, betweenness, or the product of degree and betweenness. This analysis revealed that removing nodes with high degree and high betweenness was more effective in altering networks’ robustness parameters, suggesting that their corresponding proteins may be particularly relevant to target temozolomide resistance. In silico data was used for validation and confirmed that central nodes are more relevant for altering proliferation rates in temozolomide-resistant glioma cell lines and for predicting survival in glioma patients. Altogether, these results demonstrate how the analysis of network vulnerability to topological attack facilitates target prioritization for overcoming cancer chemoresistance.

  15. Protein Secondary Structure Prediction using Pattern Recognition Neural Network

    Directory of Open Access Journals (Sweden)

    P.V. Nageswara Rao

    2010-06-01

    Full Text Available Proteins are key biological molecules with diverse functions. With newer technologies producing more data (genomics, proteomics than can be annotated manually, in silico methods of predicting their structure and thereafter their function has been christened the Holy Grail of structural bioinformatics. Successful secondary structureprediction provides a starting point for direct tertiary structure modeling; in addition it improves sequence analysis and sequence-structure binding for structure and function determination. Using machine learning and data mining process, we developed a pattern recognition technique based on statistical for predicting protein secondary structure from the component amino acid sequence. By applying this technique, a performance score of Q8=72.3% was achieved. This compares well with other established techniques, such as NN-I and GOR IV which achieved Q3 scores of 64.05% and 63.19% respectively when predictions are made on single sequence alone.

  16. Knowledge base and neural network approach for protein secondary structure prediction.

    Science.gov (United States)

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods.

  17. Functional protein networks unifying limb girdle muscular dystrophy

    OpenAIRE

    de Morrée, Antoine

    2011-01-01

    Limb Girdle Muscular Dystrophy (LGMD) is a rare progressive heterogeneous disorder that can be caused by mutations in at least 21 different genes. These genes are often widely expressed and encode proteins with highly differing functions. And yet mutations in all of them give rise to a similar clinical presentation: adult onset muscle weakness, with muscles of the pelvic and shoulder girdle as predominantly affected muscle groups. This thesis explores a potential molecular mechanism that unif...

  18. Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

    Science.gov (United States)

    Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

    2016-05-01

    A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

  19. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

    OpenAIRE

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in atte...

  20. Supervised maximum-likelihood weighting of composite protein networks for complex prediction

    Directory of Open Access Journals (Sweden)

    Yong Chern Han

    2012-12-01

    Full Text Available Abstract Background Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. Results We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Conclusions Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to

  1. A restricted set of apical proteins recycle through the trans-Golgi network in MDCK cells.

    OpenAIRE

    Brändli, A W; Simons, K

    1989-01-01

    Sorting of newly synthesized proteins destined for the apical plasma membrane takes place in the trans-Golgi network (TGN) in MDCK cells. This process is most likely receptor mediated and requires components that recycle between both compartments. We have developed an assay to detect apical proteins that recycle through the sialyltransferase-containing TGN. Cell surface glycoproteins were exogalactosylated apically using a mutant cell line derived from MDCK, MDCKII-RCAr. The mutant exhibits i...

  2. A network model to investigate structural and electrical properties of proteins

    CERN Document Server

    Alfinito, E; Reggiani, L

    2007-01-01

    One of the main trend in to date research and development is the miniaturization of electronic devices. In this perspective, integrated nanodevices based on proteins or biomolecules are attracting a major interest. In fact, it has been shown that proteins like bacteriorhodopsin and azurin, manifest electrical properties which are promising for the development of active components in the field of molecular electronics. Here we focus on two relevant kinds of proteins: The bovine rhodopsin, prototype of GPCR protein, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer disease. Both these proteins exert their functioning 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 electrical response associated with the diff...

  3. Metazoans evolved by taking domains from soluble proteins to expand intercellular communication network.

    Science.gov (United States)

    Nam, Hyun-Jun; Kim, Inhae; Bowie, James U; Kim, Sanguk

    2015-01-01

    A central question in animal evolution is how multicellular animals evolved from unicellular ancestors. We hypothesize that membrane proteins must be key players in the development of multicellularity because they are well positioned to form the cell-cell contacts and to provide the intercellular communication required for the creation of complex organisms. Here we find that a major mechanism for the necessary increase in membrane protein complexity in the transition from non-metazoan to metazoan life was the new incorporation of domains from soluble proteins. The membrane proteins that have incorporated soluble domains in metazoans are enriched in many of the functions unique to multicellular organisms such as cell-cell adhesion, signaling, immune defense and developmental processes. They also show enhanced protein-protein interaction (PPI) network complexity and centrality, suggesting an important role in the cellular diversification found in complex organisms. Our results expose an evolutionary mechanism that contributed to the development of higher life forms. PMID:25923201

  4. A multilayer protein-protein interaction network analysis of different life stages in Caenorhabditis elegans

    Science.gov (United States)

    Shinde, Pramod; Jalan, Sarika

    2015-12-01

    Molecular networks act as the backbone of cellular activities, providing an excellent opportunity to understand the developmental changes in an organism. While network data usually constitute only stationary network graphs, constructing a multilayer PPI network may provide clues to the particular developmental role at each stage of life and may unravel the importance of these developmental changes. The developmental biology model of Caenorhabditis elegans analyzed here provides a ripe platform to understand the patterns of evolution during the life stages of an organism. In the present study, the widely studied network properties exhibit overall similar statistics for all the PPI layers. Further, the analysis of the degree-degree correlation and spectral properties not only reveals crucial differences in each PPI layer but also indicates the presence of the varying complexity among them. The PPI layer of the nematode life stage exhibits various network properties different to the rest of the PPI layers, indicating the specific role of cellular diversity and developmental transitions at this stage. The framework presented here provides a direction to explore and understand the developmental changes occurring in the different life stages of an organism.

  5. Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

  6. Interplay between Chaperones and Protein Disorder Promotes the Evolution of Protein Networks

    OpenAIRE

    Sebastian Pechmann; Judith Frydman

    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 p...

  7. Integration of relational and hierarchical network information for protein function prediction

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    Jiang Xiaoyu

    2008-08-01

    Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

  8. Protein coalitions in a core mammalian biochemical network linked by rapidly evolving proteins

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    Tsoka Sophia

    2011-05-01

    Full Text Available Abstract Background Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine metabolic responses, such as glucose-stimulated insulin secretion (GSIS from the β-cells of pancreatic islets. We describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways: glycolysis, the TCA cycle and oxidative phosphorylation. Results We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway members and may serve as rapidly evolving adapters, linking groups of co-evolving genes. Conclusions Our results provide insights into the evolution of the involved proteins. We find evidence for two coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future research within a functional context.

  9. Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

    OpenAIRE

    Liu Wei-chung; Lin Wen-hsien; Hwang Ming-jing

    2009-01-01

    Abstract Background Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein ...

  10. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.

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    Oded Magger

    Full Text Available The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.

  11. SLIDER: A Generic Metaheuristic for the Discovery of Correlated Motifs in Protein-Protein Interaction Networks

    NARCIS (Netherlands)

    Boyen, P.; Dyck, van D.; Neven, F.; Ham, van R.C.H.J.; Dijk, van A.D.J.

    2011-01-01

    Correlated motif mining (CMM) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-d

  12. Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.

    Science.gov (United States)

    Corominas, Roser; Yang, Xinping; Lin, Guan Ning; Kang, Shuli; Shen, Yun; Ghamsari, Lila; Broly, Martin; Rodriguez, Maria; Tam, Stanley; Trigg, Shelly A; Fan, Changyu; Yi, Song; Tasan, Murat; Lemmens, Irma; Kuang, Xingyan; Zhao, Nan; Malhotra, Dheeraj; Michaelson, Jacob J; Vacic, Vladimir; Calderwood, Michael A; Roth, Frederick P; Tavernier, Jan; Horvath, Steve; Salehi-Ashtiani, Kourosh; Korkin, Dmitry; Sebat, Jonathan; Hill, David E; Hao, Tong; Vidal, Marc; Iakoucheva, Lilia M

    2014-04-11

    Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

  13. A quantitative analysis of contractility in active cytoskeletal protein networks.

    Science.gov (United States)

    Bendix, Poul M; Koenderink, Gijsje H; Cuvelier, Damien; Dogic, Zvonimir; Koeleman, Bernard N; Brieher, William M; Field, Christine M; Mahadevan, L; Weitz, David A

    2008-04-15

    Cells actively produce contractile forces for a variety of processes including cytokinesis and motility. Contractility is known to rely on myosin II motors which convert chemical energy from ATP hydrolysis into forces on actin filaments. However, the basic physical principles of cell contractility remain poorly understood. We reconstitute contractility in a simplified model system of purified F-actin, muscle myosin II motors, and alpha-actinin cross-linkers. We show that contractility occurs above a threshold motor concentration and within a window of cross-linker concentrations. We also quantify the pore size of the bundled networks and find contractility to occur at a critical distance between the bundles. We propose a simple mechanism of contraction based on myosin filaments pulling neighboring bundles together into an aggregated structure. Observations of this reconstituted system in both bulk and low-dimensional geometries show that the contracting gels pull on and deform their surface with a contractile force of approximately 1 microN, or approximately 100 pN per F-actin bundle. Cytoplasmic extracts contracting in identical environments show a similar behavior and dependence on myosin as the reconstituted system. Our results suggest that cellular contractility can be sensitively regulated by tuning the (local) activity of molecular motors and the cross-linker density and binding affinity. PMID:18192374

  14. Transmembrane protein topology prediction using support vector machines

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    Nugent Timothy

    2009-05-01

    Full Text Available Abstract Background Alpha-helical transmembrane (TM proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. Results We present a support vector machine-based (SVM TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/. Conclusion The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

  15. Elastic network model of allosteric regulation in protein kinase PDK1

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    Williams Gareth

    2010-05-01

    Full Text Available Abstract Background Structural switches upon binding of phosphorylated moieties underpin many signalling networks. The ligand activation is a form of allosteric modulation of the protein, where the binding site is remote from the structural change in the protein. Recently this structural switch has been elegantly demonstrated with the crystallisation of the activated form of 3-phosphoinositide-dependent protein kinase-1 (PDK1. The purpose of the present work is to determine whether the allosteric coupling in PDK1 emerges at the level of a simple coarse grained model of protein dynamics. Results It is shown here that the allosteric effects of the agonist binding to the small lobe upon the activation loop in the large lobe of PDK1 are explainable within a simple 'ball and spring' elastic network model (ENM of protein dynamics. In particular, the model shows that the bound phospho peptide mimetic fluctuations have a high degree of correlation with the activation loop of PDK1. Conclusions The ENM approach to small molecule activation of proteins may offer a first pass predictive methodology where affinity is encoded in residues remote from the active site, and aid in the design of specific protein agonists that enhance the allosteric coupling and antagonist that repress it.

  16. Salivary Defense Proteins: Their Network and Role in Innate and Acquired Oral Immunity

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    Gábor Fábián

    2012-04-01

    Full Text Available There are numerous defense proteins present in the saliva. Although some of these molecules are present in rather low concentrations, their effects are additive and/or synergistic, resulting in an efficient molecular defense network of the oral cavity. Moreover, local concentrations of these proteins near the mucosal surfaces (mucosal transudate, periodontal sulcus (gingival crevicular fluid and oral wounds and ulcers (transudate may be much greater, and in many cases reinforced by immune and/or inflammatory reactions of the oral mucosa. Some defense proteins, like salivary immunoglobulins and salivary chaperokine HSP70/HSPAs (70 kDa heat shock proteins, are involved in both innate and acquired immunity. Cationic peptides and other defense proteins like lysozyme, bactericidal/permeability increasing protein (BPI, BPI-like proteins, PLUNC (palate lung and nasal epithelial clone proteins, salivary amylase, cystatins, prolin-rich proteins, mucins, peroxidases, statherin and others are primarily responsible for innate immunity. In this paper, this complex system and function of the salivary defense proteins will be reviewed.

  17. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

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    Lv Jie

    2011-10-01

    Full Text Available Abstract Background As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. Results We constructed a weighted human PPI network (WHPN using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching Pub

  18. Scalable rule-based modelling of allosteric proteins and biochemical networks.

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    Julien F Ollivier

    Full Text Available Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  19. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    Science.gov (United States)

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-01-01

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer. PMID:27525863

  20. Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Bi-Qing Li

    2013-01-01

    Full Text Available Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC and nonsmall cell lung cancer (NSCLC. In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

  1. Transport vesicle tethering at the trans Golgi network: coiled coil proteins in action

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    Pak-yan Patricia Cheung

    2016-03-01

    Full Text Available The Golgi complex is decorated with so-called Golgin proteins that share a common feature: a large proportion of their amino acid sequences are predicted to form coiled-coil structures. The possible presence of extensive coiled coils implies that these proteins are highly elongated molecules that can extend a significant distance from the Golgi surface. This property would help them to capture or trap inbound transport vesicles and to tether Golgi mini-stacks together. This review will summarize our current understanding of coiled coil tethers that are needed for the receipt of transport vesicles at the trans Golgi network. How do long tethering proteins actually catch vesicles? Golgi-associated, coiled coil tethers contain numerous binding sites for small GTPases, SNARE proteins, and vesicle coat proteins. How are these interactions coordinated and are any or all of them important for the tethering process? Progress towards understanding these questions and remaining, unresolved mysteries will be discussed.

  2. Protein sequence for clustering DNA based on Artificial Neural Networks

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    Gamal. F. Elhadi

    2012-01-01

    Full Text Available DNA is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms and some viruses. Clustering is a process that groups a set of objects into clusters so that the similarity among objects in the same cluster is high, while that among the objects in different clusters is low. In this paper, we proposed an approach for clustering DNA sequences using Self-Organizing Map (SOM algorithm and Protein Sequence. The main objective is to analyze biological data and to bunch DNA to many clusters more easily and efficiently. We use the proposed approach to analyze both large and small amount of input DNA sequences. The results show that the similarity of the sequences does not depend on the amount of input sequences. Our approach depends on evaluating the degree of the DNA sequences similarity using the hierarchal representation Dendrogram. Representing large amount of data using hierarchal tree gives the ability to compare large sequences efficiently

  3. POINeT: protein interactome with sub-network analysis and hub prioritization

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    Lai Jin-Mei

    2009-04-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. Results We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3 to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. Conclusion The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to

  4. The G Protein-Coupled Receptor Heterodimer Network (GPCR-HetNet and Its Hub Components

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    Dasiel O. Borroto-Escuela

    2014-05-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  6. Local network topology in human protein interaction data predicts functional association.

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    Hua Li

    Full Text Available The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO and the Kyoto Encyclopedia of Genes and Genomes (KEGG as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-beta signaling pathway (P<10(-50. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.

  7. Fe2+-Tetracycline-Mediated Cleavage of the Tn10 Tetracycline Efflux Protein TetA Reveals a Substrate Binding Site near Glutamine 225 in Transmembrane Helix 7

    OpenAIRE

    McMurry, Laura M.; Aldema-Ramos, Mila L.; Levy, Stuart B.

    2002-01-01

    TetA specified by Tn10 is a class B member of a group of related bacterial transport proteins of 12 transmembrane alpha helices that mediate resistance to the antibiotic tetracycline. A tetracycline-divalent metal cation complex is expelled from the cell in exchange for a entering proton. The site(s) where tetracycline binds to this export pump is not known. We found that, when chelated to tetracycline, Fe2+ cleaved the backbone of TetA predominantly at a single position, glutamine 225 in tra...

  8. The Structure of the Human Centrin 2-Xeroderma Pigmentosum Group C Protein Complex

    Energy Technology Data Exchange (ETDEWEB)

    Thompson,J.; Ryan, Z.; Salisbury, J.; Kumar, R.

    2006-01-01

    Human centrin-2 plays a key role in centrosome function and stimulates nucleotide excision repair by binding to the xeroderma pigmentosum group C protein. To determine the structure of human centrin-2 and to develop an understanding of molecular interactions between centrin and xeroderma pigmentosum group C protein, we characterized the crystal structure of calcium-loaded full-length centrin-2 complexed with a xeroderma pigmentosum group C peptide. Our structure shows that the carboxyl-terminal domain of centrin-2 binds this peptide and two calcium atoms, whereas the amino-terminal lobe is in a closed conformation positioned distantly by an ordered {alpha}-helical linker. A stretch of the amino-terminal domain unique to centrins appears disordered. Two xeroderma pigmentosum group C peptides both bound to centrin-2 also interact to form an {alpha}-helical coiled-coil. The interface between centrin-2 and each peptide is predominantly nonpolar, and key hydrophobic residues of XPC have been identified that lead us to propose a novel binding motif for centrin.

  9. Comparative analysis of nanomechanics of protein filaments under lateral loading

    Science.gov (United States)

    Solar, Max; Buehler, Markus J.

    2012-02-01

    Using a combination of explicit solvent atomistic simulation and continuum theory, here we study the lateral deformation mechanics of three distinct protein structures: an amyloid fibril, a beta helix, and an alpha helix. We find that the two β-sheet rich structures - amyloid fibril and beta helix, with persistence lengths on the order of μm - are well described by continuum mechanical theory, but differ in the degree to which shear deformation affects the overall bending behavior. The alpha helical protein structure, however, with a persistence length on the order of one nanometer, does not conform to the continuum theory and its deformation is dominated by entropic elasticity due to significant fluctuations. This study provides fundamental insight into the nanomechanics of widely found protein motifs and insight into molecular-scale deformation mechanisms, as well as quantitative estimates of Young's modulus and shear modulus in agreement with experimental results.

  10. Getting to the Edge: Protein dynamical networks as a new frontier in plant-microbe interactions

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    Cassandra C Garbutt

    2014-06-01

    Full Text Available A systems perspective on diverse phenotypes, mechanisms of infection, and responses to environmental stresses can lead to considerable advances in agriculture and medicine. A significant promise of systems biology within plants is the development of disease-resistant crop varieties, which would maximize yield output for food, clothing, building materials and biofuel production. A systems or -omics perspective frames the next frontier in the search for enhanced knowledge of plant network biology. The functional understanding of network structure and dynamics s is vital to expanding our knowledge of how the intercellular communication processes are executed. . This review article will systematically discuss various levels of organization of systems biology beginning with the building blocks termed –omes and ending with complex transcriptional and protein-protein interaction networks. We will also highlight the prevailing computational modeling approaches of biological regulatory network dynamics. The latest developments in the -omics approach will be reviewed and discussed to underline and highlight novel technologies and research directions in plant network biology.

  11. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage;

    2009-01-01

    genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC...... of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D....

  12. The intriguing realm of protein biogenesis: Facing the green co-translational protein maturation networks.

    Science.gov (United States)

    Breiman, Adina; Fieulaine, Sonia; Meinnel, Thierry; Giglione, Carmela

    2016-05-01

    The ribosome is the cell's protein-making factory, a huge protein-RNA complex, that is essential to life. Determining the high-resolution structures of the stable "core" of this factory was among the major breakthroughs of the past decades, and was awarded the Nobel Prize in 2009. Now that the mysteries of the ribosome appear to be more traceable, detailed understanding of the mechanisms that regulate protein synthesis includes not only the well-known steps of initiation, elongation, and termination but also the less comprehended features of the co-translational events associated with the maturation of the nascent chains. The ribosome is a platform for co-translational events affecting the nascent polypeptide, including protein modifications, folding, targeting to various cellular compartments for integration into membrane or translocation, and proteolysis. These events are orchestrated by ribosome-associated protein biogenesis factors (RPBs), a group of a dozen or more factors that act as the "welcoming committee" for the nascent chain as it emerges from the ribosome. In plants these factors have evolved to fit the specificity of different cellular compartments: cytoplasm, mitochondria and chloroplast. This review focuses on the current state of knowledge of these factors and their interaction around the exit tunnel of dedicated ribosomes. Particular attention has been accorded to the plant system, highlighting the similarities and differences with other organisms.

  13. A mathematical model for generating bipartite graphs and its application to protein networks

    Energy Technology Data Exchange (ETDEWEB)

    Nacher, J C [Department of Complex Systems, Future University-Hakodate (Japan); Ochiai, T [Faculty of Engineering, Toyama Prefectural University (Japan); Hayashida, M; Akutsu, T [Bioinformatics Center, Institute for Chemical Research, Kyoto University (Japan)

    2009-12-04

    Complex systems arise in many different contexts from large communication systems and transportation infrastructures to molecular biology. Most of these systems can be organized into networks composed of nodes and interacting edges. Here, we present a theoretical model that constructs bipartite networks with the particular feature that the degree distribution can be tuned depending on the probability rate of fundamental processes. We then use this model to investigate protein-domain networks. A protein can be composed of up to hundreds of domains. Each domain represents a conserved sequence segment with specific functional tasks. We analyze the distribution of domains in Homo sapiens and Arabidopsis thaliana organisms and the statistical analysis shows that while (a) the number of domain types shared by k proteins exhibits a power-law distribution, (b) the number of proteins composed of k types of domains decays as an exponential distribution. The proposed mathematical model generates bipartite graphs and predicts the emergence of this mixing of (a) power-law and (b) exponential distributions. Our theoretical and computational results show that this model requires (1) growth process and (2) copy mechanism.

  14. A mathematical model for generating bipartite graphs and its application to protein networks

    International Nuclear Information System (INIS)

    Complex systems arise in many different contexts from large communication systems and transportation infrastructures to molecular biology. Most of these systems can be organized into networks composed of nodes and interacting edges. Here, we present a theoretical model that constructs bipartite networks with the particular feature that the degree distribution can be tuned depending on the probability rate of fundamental processes. We then use this model to investigate protein-domain networks. A protein can be composed of up to hundreds of domains. Each domain represents a conserved sequence segment with specific functional tasks. We analyze the distribution of domains in Homo sapiens and Arabidopsis thaliana organisms and the statistical analysis shows that while (a) the number of domain types shared by k proteins exhibits a power-law distribution, (b) the number of proteins composed of k types of domains decays as an exponential distribution. The proposed mathematical model generates bipartite graphs and predicts the emergence of this mixing of (a) power-law and (b) exponential distributions. Our theoretical and computational results show that this model requires (1) growth process and (2) copy mechanism.

  15. Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

    LENUS (Irish Health Repository)

    Casey, Fergal

    2011-08-22

    Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

  16. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

    Directory of Open Access Journals (Sweden)

    Kirouac Daniel C

    2012-05-01

    Full Text Available Abstract Background Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID, PANTHER, Reactome, I2D, and STRING. We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. Results We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless, we find a multiplicity of network topologies in which receptors couple to downstream

  17. Atomic resolution structure of cucurmosin, a novel type 1 ribosome-inactivating protein from the sarcocarp of Cucurbita moschata.

    Science.gov (United States)

    Hou, Xiaomin; Meehan, Edward J; Xie, Jieming; Huang, Mingdong; Chen, Minghuang; Chen, Liqing

    2008-10-01

    A novel type 1 ribosome-inactivating protein (RIP) designated cucurmosin was isolated from the sarcocarp of Cucurbita moschata (pumpkin). Besides rRNA N-glycosidase activity, cucurmosin exhibits strong cytotoxicities to three cancer cell lines of both human and murine origins, but low toxicity to normal cells. Plant genomic DNA extracted from the tender leaves was amplified by PCR between primers based on the N-terminal sequence and X-ray sequence of the C-terminal. The complete mature protein sequence was obtained from N-terminal protein sequencing and partial DNA sequencing, confirmed by high resolution crystal structure analysis. The crystal structure of cucurmosin has been determined at 1.04A, a resolution that has never been achieved before for any RIP. The structure contains two domains: a large N-terminal domain composed of seven alpha-helices and eight beta-strands, and a smaller C-terminal domain consisting of three alpha-helices and two beta-strands. The high resolution structure established a glycosylation pattern of GlcNAc(2)Man(3)Xyl. Asn225 was identified as a glycosylation site. Residues Tyr70, Tyr109, Glu158 and Arg161 define the active site of cucurmosin as an RNA N-glycosidase. The structural basis of cytotoxicity difference between cucurmosin and trichosanthin is discussed.

  18. Atomic resolution structure of cucurmosin, a novel type 1 ribosome-inactivating protein from the sarcocarp of Cucurbita moschata

    Energy Technology Data Exchange (ETDEWEB)

    Hou, Xiaomin; Meehan, Edward J.; Xie, Jieming; Huang, Mingdong; Chen, Minghuang; Chen, Liqing (UAH); (Fujian); (Chinese Aca. Sci.)

    2008-10-27

    A novel type 1 ribosome-inactivating protein (RIP) designated cucurmosin was isolated from the sarcocarp of Cucurbita moschata (pumpkin). Besides rRNA N-glycosidase activity, cucurmosin exhibits strong cytotoxicities to three cancer cell lines of both human and murine origins, but low toxicity to normal cells. Plant genomic DNA extracted from the tender leaves was amplified by PCR between primers based on the N-terminal sequence and X-ray sequence of the C-terminal. The complete mature protein sequence was obtained from N-terminal protein sequencing and partial DNA sequencing, confirmed by high resolution crystal structure analysis. The crystal structure of cucurmosin has been determined at 1.04 {angstrom}, a resolution that has never been achieved before for any RIP. The structure contains two domains: a large N-terminal domain composed of seven {alpha}-helices and eight {beta}-strands, and a smaller C-terminal domain consisting of three {alpha}-helices and two {beta}-strands. The high resolution structure established a glycosylation pattern of GlcNAc{sub 2}Man3Xyl. Asn225 was identified as a glycosylation site. Residues Tyr70, Tyr109, Glu158 and Arg161 define the active site of cucurmosin as an RNA N-glycosidase. The structural basis of cytotoxicity difference between cucurmosin and trichosanthin is discussed.

  19. Molecular studies on bromovirus capsid protein. VII. Selective packaging on BMV RNA4 by specific N-terminal arginine residuals.

    Science.gov (United States)

    Choi, Y G; Rao, A L

    2000-09-15

    An arginine-rich RNA-binding motif (ARM) found at the N-proximal region of Brome mosaic virus (BMV) coat protein (CP) adopts alpha-helical conformation and shares homology with CPs of plant and insect RNA viruses, HIV-Rev and Tat proteins, bacterial antiterminators, and ribosomal splicing factors. The ARM of BMV CP, consisting of amino acids 9 through 21 with six arginine residues, is essential for RNA binding and subsequent packaging. In this study analysis of the alpha-helical contents of wild-type and mutant peptides by circular dichroism spectra identified protein determinants required for such conformation. Electrophoretic mobility-shift assays between viral RNA and BMV CP peptides with either proline or alanine substitutions revealed that the interaction is nonspecific. Expression in vivo of mature full-length BMV CP subunits, having the same substitutions for each arginine within the ARM, derived from biologically active clones was found to be competent to assemble into infectious virions and cause visible symptom phenotypes in whole plants. However, analysis of virion progeny RNA profiles of CP variants and subsequent in vitro reassembly assays between mutant CP and four BMV RNAs unveiled the ability of arginine residues at positions 10, 13, or 14 of the ARM to confer selective packaging of BMV RNA4. Thus, BMV CP contains determinants that specifically interact with RNA4 to ensure selective packaging.

  20. Liposome-based assays to study membrane-associated protein networks.

    Science.gov (United States)

    Niehage, Christian; Stange, Christoph; Anitei, Mihaela; Hoflack, Bernard

    2014-01-01

    Transport carriers regulate the bidirectional flow of membrane between the compartments of the secretory and endocytic pathways. Their biogenesis relies on the recruitment of a number of cytosolic proteins and protein complexes on specific membrane microdomains with defined protein and lipid compositions. The timely assembly of these cellular machines onto membranes involves multiple protein-protein and protein-lipid interactions and is necessary to select membrane proteins and lipids into nascent carriers, to bend the flat membrane of the donor compartment, to change the shape of this nascent carrier into a tubular-vesicular structure, and to operate its scission from the donor compartment. A challenge in this field of membrane cell biology has been to identify these machineries and to understand their precise function, in particular by studying their spatial and temporal dynamics during carrier biogenesis. During the past years, liposome-based synthetic biology fully recapitulating the fidelity of carrier biogenesis as seen in vivo has proved to be instrumental to identify these key cytosolic components using mass spectrometry and their dynamics using fluorescence microscopy. We describe here the methods to isolate on synthetic membranes the protein networks needed for carrier biogenesis, to identify them using label-free quantitative proteomics, and to visualize their dynamics on giant unilamellar vesicles. PMID:24359957

  1. Gene organization, evolution and expression of the microtubule-associated protein ASAP (MAP9

    Directory of Open Access Journals (Sweden)

    Giorgi Dominique

    2008-09-01

    Full Text Available Abstract Background ASAP is a newly characterized microtubule-associated protein (MAP essential for proper cell-cycling. We have previously shown that expression deregulation of human ASAP results in profound defects in mitotic spindle formation and mitotic progression leading to aneuploidy, cytokinesis defects and/or cell death. In the present work we analyze the structure and evolution of the ASAP gene, as well as the domain composition of the encoded protein. Mouse and Xenopus cDNAs were cloned, the tissue expression characterized and the overexpression profile analyzed. Results Bona fide ASAP orthologs are found in vertebrates with more distantly related potential orthologs in invertebrates. This single-copy gene is conserved in mammals where it maps to syntenic chromosomal regions, but is also clearly identified in bird, fish and frog. The human gene is strongly expressed in brain and testis as a 2.6 Kb transcript encoding a ~110 KDa protein. The protein contains MAP, MIT-like and THY domains in the C-terminal part indicative of microtubule interaction, while the N-terminal part is more divergent. ASAP is composed of ~42% alpha helical structures, and two main coiled-coil regions have been identified. Different sequence features may suggest a role in DNA damage response. As with human ASAP, the mouse and Xenopus proteins localize to the microtubule network in interphase and to the mitotic spindle during mitosis. Overexpression of the mouse protein induces mitotic defects similar to those observed in human. In situ hybridization in testis localized ASAP to the germ cells, whereas in culture neurons ASAP localized to the cell body and growing neurites. Conclusion The conservation of ASAP indicated in our results reflects an essential function in vertebrates. We have cloned the ASAP orthologs in mouse and Xenopus, two valuable models to study the function of ASAP. Tissue expression of ASAP revealed a high expression in brain and testis, two

  2. Artificial neural networks trained to detect viral and phage structural proteins.

    Science.gov (United States)

    Seguritan, Victor; Alves, Nelson; Arnoult, Michael; Raymond, Amy; Lorimer, Don; Burgin, Alex B; Salamon, Peter; Segall, Anca M

    2012-01-01

    Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is

  3. Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins.

    Science.gov (United States)

    Kovács, István A; Szalay, Máté S; Csermely, Peter

    2005-04-25

    Water molecules and molecular chaperones efficiently help the protein folding process. Here we describe their action in the context of the energy and topological networks of proteins. In energy terms water and chaperones were suggested to decrease the activation energy between various local energy minima smoothing the energy landscape, rescuing misfolded proteins from conformational traps and stabilizing their native structure. In kinetic terms water and chaperones may make the punctuated equilibrium of conformational changes less punctuated and help protein relaxation. Finally, water and chaperones may help the convergence of multiple energy landscapes during protein-macromolecule interactions. We also discuss the possibility of the introduction of protein games to narrow the multitude of the energy landscapes when a protein binds to another macromolecule. Both water and chaperones provide a diffuse set of rapidly fluctuating weak links (low affinity and low probability interactions), which allow the generalization of all these statements to a multitude of networks. PMID:15848154

  4. Expression Profiling of Human Genetic and Protein Interaction Networks in Type 1 Diabetes

    DEFF Research Database (Denmark)

    Brunak, Søren; Bergholdt, R; Brorsson, C;

    2009-01-01

    : oxidative stress, regulation of transcription and apoptosis. To understand biological systems, integration of genetic and functional information is necessary, and the current study has used this approach to improve understanding of T1D and the underlying biological mechanisms.......Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have...... previously identified sets of genes, likely to represent distinct cellular pathways involved in T1D risk. Here we evaluate the candidate genes involved in these putative interaction networks not only at the single gene level, but also in the context of the networks of which they form an integral part. m...

  5. STITCH 2: an interaction network database for small molecules and proteins

    DEFF Research Database (Denmark)

    Kuhn, Michael; Szklarczyk, Damian; Franceschini, Andrea;

    2010-01-01

    Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug......-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other...... chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/....

  6. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yang; Bax, Ad, E-mail: bax@nih.gov [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

    2013-07-15

    A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, {>=}90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed ({phi}, {psi}) torsion angles of ca 12 Masculine-Ordinal-Indicator . TALOS-N also reports sidechain {chi}{sup 1} rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

  7. Mapping the Protein-Protein Interactome Networks Using Yeast Two-Hybrid Screens.

    Science.gov (United States)

    Rajagopala, Seesandra Venkatappa

    2015-01-01

    The yeast two-hybrid system (Y2H) is a powerful method to identify binary protein-protein interactions in vivo. Here we describe Y2H screening strategies that use defined libraries of open reading frames (ORFs) and cDNA libraries. The array-based Y2H system is well suited for interactome studies of small genomes with an existing ORFeome clones preferentially in a recombination based cloning system. For large genomes, pooled library screening followed by Y2H pairwise retests may be more efficient in terms of time and resources, but multiple sampling is necessary to ensure comprehensive screening. While the Y2H false positives can be efficiently reduced by using built-in controls, retesting, and evaluation of background activation; implementing the multiple variants of the Y2H vector systems is essential to reduce the false negatives and ensure comprehensive coverage of an interactome. PMID:26621469

  8. Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Baoman Wang

    2015-01-01

    Full Text Available Apoptosis is the process of programmed cell death (PCD that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

  9. Mapping the mutual information network of enzymatic families in the protein structure to unveil functional features.

    Science.gov (United States)

    Aguilar, Daniel; Oliva, Baldo; Marino Buslje, Cristina

    2012-01-01

    Amino acids committed to a particular function correlate tightly along evolution and tend to form clusters in the 3D structure of the protein. Consequently, a protein can be seen as a network of co-evolving clusters of residues. The goal of this work is two-fold: first, we have combined mutual information and structural data to describe the amino acid networks within a protein and their interactions. Second, we have investigated how this information can be used to improve methods of prediction of functional residues by reducing the search space. As a main result, we found that clusters of co-evolving residues related to the catalytic site of an enzyme have distinguishable topological properties in the network. We also observed that these clusters usually evolve independently, which could be related to a fail-safe mechanism. Finally, we discovered a significant enrichment of functional residues (e.g. metal binding, susceptibility to detrimental mutations) in the clusters, which could be the foundation of new prediction tools. PMID:22848494

  10. Toward structural dynamics: protein motions viewed by chemical shift modulations and direct detection of C'N multiple-quantum relaxation.

    Science.gov (United States)

    Mori, Mirko; Kateb, Fatiha; Bodenhausen, Geoffrey; Piccioli, Mario; Abergel, Daniel

    2010-03-17

    Multiple quantum relaxation in proteins reveals unexpected relationships between correlated or anti-correlated conformational backbone dynamics in alpha-helices or beta-sheets. The contributions of conformational exchange to the relaxation rates of C'N coherences (i.e., double- and zero-quantum coherences involving backbone carbonyl (13)C' and neighboring amide (15)N nuclei) depend on the kinetics of slow exchange processes, as well as on the populations of the conformations and chemical shift differences of (13)C' and (15)N nuclei. The relaxation rates of C'N coherences, which reflect concerted fluctuations due to slow chemical shift modulations (CSMs), were determined by direct (13)C detection in diamagnetic and paramagnetic proteins. In well-folded proteins such as lanthanide-substituted calbindin (CaLnCb), copper,zinc superoxide dismutase (Cu,Zn SOD), and matrix metalloproteinase (MMP12), slow conformational exchange occurs along the entire backbone. Our observations demonstrate that relaxation rates of C'N coherences arising from slow backbone dynamics have positive signs (characteristic of correlated fluctuations) in beta-sheets and negative signs (characteristic of anti-correlated fluctuations) in alpha-helices. This extends the prospects of structure-dynamics relationships to slow time scales that are relevant for protein function and enzymatic activity.

  11. A network model to correlate conformational change and the impedance spectrum of single proteins

    Energy Technology Data Exchange (ETDEWEB)

    Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via Arnesano, Lecce (Italy); Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia (CNISM) (Italy)

    2008-02-13

    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.

  12. Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

    Science.gov (United States)

    Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

    2015-01-01

    Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

  13. Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

    Directory of Open Access Journals (Sweden)

    Vassilis Stavrakas

    Full Text Available Modeling of signal transduction pathways is instrumental for understanding cells' function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells' biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways' logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein-protein interaction networks and to provide meaningful biological insights.

  14. Resemblance of actin-binding protein/actin gels to covalently crosslinked networks

    Science.gov (United States)

    Janmey, Paul A.; Hvidt, Søren; Lamb, Jennifer; Stossel, Thomas P.

    1990-05-01

    THE maintainance of the shape of cells is often due to their surface elasticity, which arises mainly from an actin-rich cytoplasmic cortex1,2. On locomotion, phagocytosis or fission, however, these cells become partially fluid-like. The finding of proteins that can bind to actin and control the assembly of, or crosslink, actin filaments, and of intracellular messages that regulate the activities of some of these actin-binding proteins, indicates that such 'gel sol' transformations result from the rearrangement of cortical actin-rich networks3. Alternatively, on the basis of a study of the mechanical properties of mixtures of actin filaments and an Acanthamoeba actin-binding protein, α-actinin, it has been proposed that these transformations can be accounted for by rapid exchange of crosslinks between actin filaments4: the cortical network would be solid when the deformation rate is greater than the rate of crosslink exchange, but would deform or 'creep' when deformation is slow enough to permit crosslinker molecules to rearrange. Here we report, however, that mixtures of actin filaments and actin-binding protein (ABP), an actin crosslinking protein of many higher eukaryotes, form gels Theologically equivalent to covalently crosslinked networks. These gels do not creep in response to applied stress on a time scale compatible with most cell-surface movements. These findings support a more complex and controlled mechanism underlying the dynamic mechanical properties of cortical cytoplasm, and can explain why cells do not collapse under the constant shear forces that often exist in tissues.

  15. Molecular Dynamics Studies on the Buffalo Prion Protein

    CERN Document Server

    Zhang, Jiapu

    2015-01-01

    It was reported that buffalo is a low susceptibility species resisting to TSEs (Transmissible Spongiform Encephalopathies) (same as rabbits, horses and dogs). TSEs, also called prion diseases, are invariably fatal and highly infectious neurodegenerative diseases that affect a wide variety of species (in humans prion diseases are (v)CJDs, GSS, FFI, and kulu etc). It was reported that buffalo is a low susceptibility species resisting to prion diseases (as rabbits, dogs, horses). In molecular structures, these neurodegenerative diseases are caused by the conversion from a soluble normal cellular prion protein, predominantly with alpha-helices, into insoluble abnormally folded infectious prions, rich in beta-sheets. This paper studies the molecular structure and structural dynamics of buffalo prion protein, in order to find out the reason why buffaloes are resistant to prion diseases. We first did molecular modeling a homology structure constructed by one mutation at residue 143 from the Nuclear Magnetic Resonanc...

  16. Multiple actin binding domains of Ena/VASP proteins determine actin network stiffening.

    Science.gov (United States)

    Gentry, Brian S; van der Meulen, Stef; Noguera, Philippe; Alonso-Latorre, Baldomero; Plastino, Julie; Koenderink, Gijsje H

    2012-11-01

    Vasodilator-stimulated phosphoprotein (Ena/VASP) is an actin binding protein, important for actin dynamics in motile cells and developing organisms. Though VASP's main activity is the promotion of barbed end growth, it has an F-actin binding site and can form tetramers, and so could additionally play a role in actin crosslinking and bundling in the cell. To test this activity, we performed rheology of reconstituted actin networks in the presence of wild-type VASP or mutants lacking the ability to tetramerize or to bind G-actin and/or F-actin. We show that increasing amounts of wild-type VASP increase network stiffness up to a certain point, beyond which stiffness actually decreases with increasing VASP concentration. The maximum stiffness is 10-fold higher than for pure actin networks. Confocal microscopy shows that VASP forms clustered actin filament bundles, explaining the reduction in network elasticity at high VASP concentration. Removal of the tetramerization site results in significantly reduced bundling and bundle clustering, indicating that VASP's flexible tetrameric structure causes clustering. Removing either the F-actin or the G-actin binding site diminishes VASP's effect on elasticity, but does not eliminate it. Mutating the F-actin and G-actin binding site together, or mutating the F-actin binding site and saturating the G-actin binding site with monomeric actin, eliminates VASP's ability to increase network stiffness. We propose that, in the cell, VASP crosslinking confers only moderate increases in linear network elasticity, and unlike other crosslinkers, VASP's network stiffening activity may be tuned by the local concentration of monomeric actin.

  17. Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Inhae Kim

    2014-10-01

    Full Text Available The modular architecture of protein-protein interaction (PPI networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs are more likely to connect different biological modules than strong domain-domain interactions (DDIs. This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

  18. A novel member of the split betaalphabeta fold: Solution structure of the hypothetical protein YML108W from Saccharomyces cerevisiae.

    Science.gov (United States)

    Pineda-Lucena, Antonio; Liao, Jack C C; Cort, John R; Yee, Adelinda; Kennedy, Michael A; Edwards, Aled M; Arrowsmith, Cheryl H

    2003-05-01

    As part of the Northeast Structural Genomics Consortium pilot project focused on small eukaryotic proteins and protein domains, we have determined the NMR structure of the protein encoded by ORF YML108W from Saccharomyces cerevisiae. YML108W belongs to one of the numerous structural proteomics targets whose biological function is unknown. Moreover, this protein does not have sequence similarity to any other protein. The NMR structure of YML108W consists of a four-stranded beta-sheet with strand order 2143 and two alpha-helices, with an overall topology of betabetaalphabetabetaalpha. Strand beta1 runs parallel to beta4, and beta2:beta1 and beta4:beta3 pairs are arranged in an antiparallel fashion. Although this fold belongs to the split betaalphabeta family, it appears to be unique among this family; it is a novel arrangement of secondary structure, thereby expanding the universe of protein folds.

  19. Note: Network random walk model of two-state protein folding: Test of the theory

    Science.gov (United States)

    Berezhkovskii, Alexander M.; Murphy, Ronan D.; Buchete, Nicolae-Viorel

    2013-01-01

    We study two-state protein folding in the framework of a toy model of protein dynamics. This model has an important advantage: it allows for an analytical solution for the sum of folding and unfolding rate constants [A. M. Berezhkovskii, F. Tofoleanu, and N.-V. Buchete, J. Chem. Theory Comput. 7, 2370 (2011), 10.1021/ct200281d] and hence for the reactive flux at equilibrium. We use the model to test the Kramers-type formula for the reactive flux, which was derived assuming that the protein dynamics is described by a Markov random walk on a network of complex connectivity [A. Berezhkovskii, G. Hummer, and A. Szabo, J. Chem. Phys. 130, 205102 (2009), 10.1063/1.3139063]. It is shown that the Kramers-type formula leads to the same result for the reactive flux as the sum of the rate constants.

  20. Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Li

    2010-01-01

    Full Text Available Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways.

  1. Uncovering the protein lysine and arginine methylation network in Arabidopsis chloroplasts.

    Directory of Open Access Journals (Sweden)

    Claude Alban

    Full Text Available Post-translational modification of proteins by the addition of methyl groups to the side chains of Lys and Arg residues is proposed to play important roles in many cellular processes. In plants, identification of non-histone methylproteins at a cellular or subcellular scale is still missing. To gain insights into the extent of this modification in chloroplasts we used a bioinformatics approach to identify protein methyltransferases targeted to plastids and set up a workflow to specifically identify Lys and Arg methylated proteins from proteomic data used to produce the Arabidopsis chloroplast proteome. With this approach we could identify 31 high-confidence Lys and Arg methylation sites from 23 chloroplastic proteins, of which only two were previously known to be methylated. These methylproteins are split between the stroma, thylakoids and envelope sub-compartments. They belong to essential metabolic processes, including photosynthesis, and to the chloroplast biogenesis and maintenance machinery (translation, protein import, division. Also, the in silico identification of nine protein methyltransferases that are known or predicted to be targeted to plastids provided a foundation to build the enzymes/substrates relationships that govern methylation in chloroplasts. Thereby, using in vitro methylation assays with chloroplast stroma as a source of methyltransferases we confirmed the methylation sites of two targets, plastid ribosomal protein L11 and the β-subunit of ATP synthase. Furthermore, a biochemical screening of recombinant chloroplastic protein Lys methyltransferases allowed us to identify the enzymes involved in the modification of these substrates. The present study provides a useful resource to build the methyltransferases/methylproteins network and to elucidate the role of protein methylation in chloroplast biology.

  2. Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

    Science.gov (United States)

    Correia, Catarina; Oliveira, Guiomar; Vicente, Astrid M.

    2014-01-01

    Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical “noise” that warrant further analysis for causal variants. PMID:25409314

  3. Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity.

    Science.gov (United States)

    Papaleo, Elena

    2015-01-01

    In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

  4. Mining quasi-bicliques from HIV-1-human protein interaction network: a multiobjective biclustering approach.

    Science.gov (United States)

    Maulik, Ujjwal; Mukhopadhyay, Anirban; Bhattacharyya, Malay; Kaderali, Lars; Brors, Benedikt; Bandyopadhyay, Sanghamitra; Eils, Roland

    2013-01-01

    In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs. PMID:23929866

  5. Mining Quasi-Bicliques from HIV-1--Human Protein Interaction Network: A Multiobjective Biclustering Approach.

    Science.gov (United States)

    Maulik, Ujjwal; Mukhopadhyay, Anirban; Bhattacharyya, Malay; Kaderali, Lars; Brors, Benedikt; Bandyopadhyay, Sanghamitra; Eils, Roland

    2012-11-28

    In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs. PMID:23209057

  6. Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum.

    Directory of Open Access Journals (Sweden)

    Oliver Ratmann

    2007-11-01

    Full Text Available Gene duplication with subsequent interaction divergence is one of the primary driving forces in the evolution of genetic systems. Yet little is known about the precise mechanisms and the role of duplication divergence in the evolution of protein networks from the prokaryote and eukaryote domains. We developed a novel, model-based approach for Bayesian inference on biological network data that centres on approximate Bayesian computation, or likelihood-free inference. Instead of computing the intractable likelihood of the protein network topology, our method summarizes key features of the network and, based on these, uses a MCMC algorithm to approximate the posterior distribution of the model parameters. This allowed us to reliably fit a flexible mixture model that captures hallmarks of evolution by gene duplication and subfunctionalization to protein interaction network data of Helicobacter pylori and Plasmodium falciparum. The 80% credible intervals for the duplication-divergence component are [0.64, 0.98] for H. pylori and [0.87, 0.99] for P. falciparum. The remaining parameter estimates are not inconsistent with sequence data. An extensive sensitivity analysis showed that incompleteness of PIN data does not largely affect the analysis of models of protein network evolution, and that the degree sequence alone barely captures the evolutionary footprints of protein networks relative to other statistics. Our likelihood-free inference approach enables a fully Bayesian analysis of a complex and highly stochastic system that is otherwise intractable at present. Modelling the evolutionary history of PIN data, it transpires that only the simultaneous analysis of several global aspects of protein networks enables credible and consistent inference to be made from available datasets. Our results indicate that gene duplication has played a larger part in the network evolution of the eukaryote than in the prokaryote, and suggests that single gene

  7. Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins

    OpenAIRE

    Kovacs, Istvan A.; Szalay, Mate S.; Csermely, Peter

    2004-01-01

    Water molecules and molecular chaperones efficiently help the protein folding process. Here we describe their action in the context of the energy and topological networks of proteins. In energy terms water and chaperones were suggested to decrease the activation energy between various local energy minima smoothing the energy landscape, rescuing misfolded proteins from conformational traps and stabilizing their native structure. In kinetic terms water and chaperones may make the punctuated equ...

  8. Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction

    Institute of Scientific and Technical Information of China (English)

    YANG Yang; LI Kai-yang

    2006-01-01

    The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication-the highest prediction rate 75.65%, the average prediction rate 65.04%.

  9. Obesity risk gene TMEM18 encodes a sequence-specific DNA-binding protein.

    Directory of Open Access Journals (Sweden)

    Jaana M Jurvansuu

    Full Text Available Transmembrane protein 18 (TMEM18 has previously been connected to cell migration and obesity. However, the molecular function of the protein has not yet been described. Here we show that TMEM18 localises to the nuclear membrane and binds to DNA in a sequence-specific manner. The protein binds DNA with its positively charged C-terminus that contains also a nuclear localisation signal. Increase in the amount of TMEM18 in cells suppresses expression from a reporter vector with the TMEM18 target sequence. TMEM18 is a small protein of 140 residues and is predicted to be mostly alpha-helical with three transmembrane parts. As a consequence the DNA binding by TMEM18 would bring the chromatin very near to nuclear membrane. We speculate that this closed perinuclear localisation of TMEM18-bound DNA might repress transcription from it.

  10. The compartmentalized bacteria of the planctomycetes-verrucomicrobia-chlamydiae superphylum have membrane coat-like proteins.

    Directory of Open Access Journals (Sweden)

    Rachel Santarella-Mellwig

    2010-01-01

    Full Text Available The development of the endomembrane system was a major step in eukaryotic evolution. Membrane coats, which exhibit a unique arrangement of beta-propeller and alpha-helical repeat domains, play key roles in shaping eukaryotic membranes. Such proteins are likely to have been present in the ancestral eukaryote but cannot be detected in prokaryotes using sequence-only searches. We have used a structure-based detection protocol to search all proteomes for proteins with this domain architecture. Apart from the eukaryotes, we identified this protein architecture only in the Planctomycetes-Verrucomicrobia-Chlamydiae (PVC bacterial superphylum, many members of which share a compartmentalized cell plan. We determined that one such protein is partly localized at the membranes of vesicles formed inside the cells in the planctomycete Gemmata obscuriglobus. Our results demonstrate similarities between bacterial and eukaryotic compartmentalization machinery, suggesting that the bacterial PVC superphylum contributed significantly to eukaryogenesis.

  11. The human fatty acid-binding protein family: Evolutionary divergences and functions

    Directory of Open Access Journals (Sweden)

    Smathers Rebecca L

    2011-03-01

    Full Text Available Abstract Fatty acid-binding proteins (FABPs are members of the intracellular lipid-binding protein (iLBP family and are involved in reversibly binding intracellular hydrophobic ligands and trafficking them throughout cellular compartments, including the peroxisomes, mitochondria, endoplasmic reticulum and nucleus. FABPs are small, structurally conserved cytosolic proteins consisting of a water-filled, interior-binding pocket surrounded by ten anti-parallel beta sheets, forming a beta barrel. At the superior surface, two alpha-helices cap the pocket and are thought to regulate binding. FABPs have broad specificity, including the ability to bind long-chain (C16-C20 fatty acids, eicosanoids, bile salts and peroxisome proliferators. FABPs demonstrate strong evolutionary conservation and are present in a spectrum of species including Drosophila melanogaster, Caenorhabditis elegans, mouse and human. The human genome consists of nine putatively functional protein-coding FABP genes. The most recently identified family member, FABP12, has been less studied.

  12. Analysis of the human E2 ubiquitin conjugating enzyme protein interaction network

    Science.gov (United States)

    Markson, Gabriel; Kiel, Christina; Hyde, Russell; Brown, Stephanie; Charalabous, Panagoula; Bremm, Anja; Semple, Jennifer; Woodsmith, Jonathan; Duley, Simon; Salehi-Ashtiani, Kourosh; Vidal, Marc; Komander, David; Serrano, Luis; Lehner, Paul; Sanderson, Christopher M.

    2009-01-01

    In eukaryotic cells the stability and function of many proteins are regulated by the addition of ubiquitin or ubiquitin-like peptides. This process is dependent upon the sequential action of an E1-activating enzyme, an E2-conjugating enzyme, and an E3 ligase. Different combinations of these proteins confer substrate specificity and the form of protein modification. However, combinatorial preferences within ubiquitination networks remain unclear. In this study, yeast two-hybrid (Y2H) screens were combined with true homology modeling methods to generate a high-density map of human E2/E3-RING interactions. These data include 535 experimentally defined novel E2/E3-RING interactions and >1300 E2/E3-RING pairs with more favorable predicted free-energy values than the canonical UBE2L3–CBL complex. The significance of Y2H predictions was assessed by both mutagenesis and functional assays. Significantly, 74/80 (>92%) of Y2H predicted complexes were disrupted by point mutations that inhibit verified E2/E3-RING interactions, and a ∼93% correlation was observed between Y2H data and the functional activity of E2/E3-RING complexes in vitro. Analysis of the high-density human E2/E3-RING network reveals complex combinatorial interactions and a strong potential for functional redundancy, especially within E2 families that have undergone evolutionary expansion. Finally, a one-step extended human E2/E3-RING network, containing 2644 proteins and 5087 edges, was assembled to provide a resource for future functional investigations. PMID:19549727

  13. Comparison of two different stochastic models for extracting protein regulatory pathways using Bayesian networks.

    Science.gov (United States)

    Grzegorczyk, Marco

    2008-01-01

    Toxicoproteomics integrates traditional toxicology and systems biology and seeks to infer the architecture of biochemical pathways in biological systems that are affected by and respond to chemical and environmental exposures. Different reverse engineering methods for extracting biochemical regulatory networks from data have been proposed and it is important to understand their relative strengths and weaknesses. To shed some light onto this problem, Werhli et al. (2006) cross-compared three widely used methodologies, relevance networks, graphical Gaussian models, and Bayesian networks (BN), on real cytometric and synthetic expression data. This study continues with the evaluation and compares the learning performances of two different stochastic models (BGe and BDe) for BN. Cytometric protein expression data from the RAF-signaling pathway were used for the cross-method comparison. Understanding this pathway is an important task, as it is known that RAF is a critical signaling protein whose deregulation leads to carcinogenesis. When the more flexible BDe model is employed, a data discretization, which usually incurs an inevitable information loss, is needed. However, the results of the study reveal that the BDe model is preferable to the BGe model when a sufficiently large number of observations from the pathway are available. PMID:18569581

  14. Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

    Science.gov (United States)

    Rozenblatt-Rosen, Orit; Deo, Rahul C; Padi, Megha; Adelmant, Guillaume; Calderwood, Michael A; Rolland, Thomas; Grace, Miranda; Dricot, Amélie; Askenazi, Manor; Tavares, Maria; Pevzner, Samuel J; Abderazzaq, Fieda; Byrdsong, Danielle; Carvunis, Anne-Ruxandra; Chen, Alyce A; Cheng, Jingwei; Correll, Mick; Duarte, Melissa; Fan, Changyu; Feltkamp, Mariet C; Ficarro, Scott B; Franchi, Rachel; Garg, Brijesh K; Gulbahce, Natali; Hao, Tong; Holthaus, Amy M; James, Robert; Korkhin, Anna; Litovchick, Larisa; Mar, Jessica C; Pak, Theodore R; Rabello, Sabrina; Rubio, Renee; Shen, Yun; Singh, Saurav; Spangle, Jennifer M; Tasan, Murat; Wanamaker, Shelly; Webber, James T; Roecklein-Canfield, Jennifer; Johannsen, Eric; Barabási, Albert-László; Beroukhim, Rameen; Kieff, Elliott; Cusick, Michael E; Hill, David E; Münger, Karl; Marto, Jarrod A; Quackenbush, John; Roth, Frederick P; DeCaprio, James A; Vidal, Marc

    2012-07-26

    Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer. PMID:22810586

  15. A fast iterative-clique percolation method for identifying functional modules in protein interaction networks

    Institute of Scientific and Technical Information of China (English)

    Penggang SUN; Lin GAO

    2009-01-01

    Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules-groups of vertices within which connections are dense but between which they are sparse. Identifying these modules is likely through capturing the biologically mean-ingful interactions. In recent years, many algorithms have been developed for detecting such structures. These al-gorithms, however, are computationally demanding, which limits their applications. In this paper, we propose a fast iterative-clique percolation method (ICPM) for identifying overlapping functional modules in protein-protein interac-tion (PPI) networks. Our method is based on clique percola-tion method (CPM), and it not only considers the degree of nodes to minimize the search space (the vertices in k-cliques must have the degree of k - 1 at least), but also converts k-cliques to (k - 1)-cliques. It finds k-cliques by append-ing one node to (k - 1)-cliques. By testing our method on PPI networks, our analysis of the yeast PPI network suggeststhat most of these modules have well-supported biological significance.

  16. A quantitative approach to analyzing genome reductive evolution using protein-protein interaction networks: A case study ofMycobacterium leprae

    Directory of Open Access Journals (Sweden)

    Richard O Akinola

    2016-03-01

    Full Text Available The advance in high-throughput sequencing technologies has yielded complete genome sequences of several organisms, including complete bacterial genomes. The growing number of these available sequenced genomes has enabled analyses of their dynamics, as well as the molecular and evolutionary processes which these organisms are under. Comparative genomics of different bacterial genomes have highlighted their genome size and gene content in association with lifestyles and adaptation to various environments and have contributed to enhancing our understanding of the mechanisms of their evolution. Protein-protein functional interactions mediate many essential processes for maintaining the stability of the biological systems under changing environmental conditions. Thus, these interactions play crucial roles in the evolutionary processes of different organisms, especially for obligate intracellular bacteria, proven to generally have reduced genome sizes compared to their nearest free-living relatives. In this study, we used the approach based on the Renormalization Group (RG analysis technique and the Maximum-Excluded-Mass-Burning (MEMB model to investigate the evolutionary process of genome reduction in relation to the organization of functional networks of two organisms. Using a Mycobacterium leprae (MLP network in comparison with a Mycobacterium tuberculosis (MTB network as a case study, we show that reductive evolution in MLP was as a result of removal of important proteins from neighbours of corresponding orthologous MTB proteins. While each orthologous MTB protein had an increase in number of interacting partners in most instances, the corresponding MLP protein had lost some of them. This work provides a quantitative model for mapping reductive evolution and protein-protein functional interaction network organization in terms of roles played by different proteins in the network structure.

  17. Predicting protein structural classes based on complex networks and recurrence analysis.

    Science.gov (United States)

    Olyaee, Mohammad H; Yaghoubi, Ali; Yaghoobi, Mahdi

    2016-09-01

    Protein sequences are divided into four structural classes. The determination of class is a challenging and beneficial task in the bioinformatics field. Several methods have been proposed to this end, but most utilize too many features and produce unsuitable results. In the present, features are extracted based on the predicted secondary structures. At first, predicted secondary structure sequences are mapped into two time series by the chaos game representation. Then, a recurrence matrix is calculated from each of the time series. The recurrence matrix is identified with the adjacency matrix of a complex network and measures are applied for the characterization of complex networks to these recurrence matrixes. For a given protein sequence, a total of 24 characteristic features can be calculated and these are fed into Fisher's discriminated analysis algorithm for classification. To examine the proposed method, two widely used low similarity benchmark datasets design and test its performance. A comparison with the results of existing methods shows that the current study's approach provides a satisfactory performance for protein structural class prediction. PMID:27320678

  18. Protein distance constraints predicted by neural networks and probability density functions

    DEFF Research Database (Denmark)

    Lund, Ole; Frimand, Kenneth; Gorodkin, Jan;

    1997-01-01

    We predict interatomic C-α distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taking....... The predictions are based on a data set derived using a new threshold similarity. We show that distances in proteins are predicted more accurately by neural networks than by probability density functions. We show that the accuracy of the predictions can be further increased by using sequence profiles. A threading...... the context of the two residues into account. These two methods are used to predict whether distances in independent test sets were above or below given thresholds. We investigate which distance thresholds produce the most information-rich constraints and, in turn, the optimal performance of the two methods...

  19. Orchestrated content release from Drosophila glue-protein vesicles by a contractile actomyosin network.

    Science.gov (United States)

    Rousso, Tal; Schejter, Eyal D; Shilo, Ben-Zion

    2016-02-01

    Releasing content from large vesicles measuring several micrometres in diameter poses exceptional challenges to the secretory system. An actomyosin network commonly coats these vesicles, and is thought to provide the necessary force mediating efficient cargo release. Here we describe the spatial and temporal dynamics of the formation of this actomyosin coat around large vesicles and the resulting vesicle collapse, in live Drosophila melanogaster salivary glands. We identify the Formin family protein Diaphanous (Dia) as the main actin nucleator involved in generating this structure, and uncover Rho as an integrator of actin assembly and contractile machinery activation comprising this actomyosin network. High-resolution imaging reveals a unique cage-like organization of myosin II on the actin coat. This myosin arrangement requires branched-actin polymerization, and is critical for exerting a non-isotropic force, mediating efficient vesicle contraction.

  20. Network single-walled carbon nanotube biosensors for fast and highly sensitive detection of proteins

    Energy Technology Data Exchange (ETDEWEB)

    Hu Pingan; Zhang Jia; Wen Zhenzhong [Research Centre for Micro/Nanotechnology, Harbin Institute of Technology, No. 2 YiKuang Street, Harbin 150080 (China); Zhang Can, E-mail: hupa@hit.edu.cn [Centre for Advanced Photonics and Electronics, University of Cambridge, Cambridge CB3 0FA (United Kingdom)

    2011-08-19

    Detection of proteins is powerfully assayed in the diagnosis of diseases. A strategy for the development of an ultrahigh sensitivity biosensor based on a network single-walled carbon nanotube (SWNT) field-effect transistor (FET) has been demonstrated. Metallic SWNTs (m-SWNTs) in the network nanotube FET were selectively removed or cut via a carefully controlled procedure of electrical break-down (BD), and left non-conducting m-SWNTs which magnified the Schottky barrier (SB) area. This nanotube FET exhibited ultrahigh sensitivity and fast response to biomolecules. The lowest detection limit of 0.5 pM was achieved by exploiting streptavidin (SA) or a biotin/SA pair as the research model, and BD-treated nanotube biosensors had a 2 x 10{sup 4}-fold lower minimum detectable concentration than the device without BD treatment. The response time is in the range of 0.3-3 min.

  1. GBNV encoded movement protein (NSm) remodels ER network via C-terminal coiled coil domain

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Pratibha; Savithri, H.S., E-mail: bchss@biochem.iisc.ernet.in

    2015-08-15

    Plant viruses exploit the host machinery for targeting the viral genome–movement protein complex to plasmodesmata (PD). The mechanism by which the non-structural protein m (NSm) of Groundnut bud necrosis virus (GBNV) is targeted to PD was investigated using Agrobacterium mediated transient expression of NSm and its fusion proteins in Nicotiana benthamiana. GFP:NSm formed punctuate structures that colocalized with mCherry:plasmodesmata localized protein 1a (PDLP 1a) confirming that GBNV NSm localizes to PD. Unlike in other movement proteins, the C-terminal coiled coil domain of GBNV NSm was shown to be involved in the localization of NSm to PD, as deletion of this domain resulted in the cytoplasmic localization of NSm. Treatment with Brefeldin A demonstrated the role of ER in targeting GFP NSm to PD. Furthermore, mCherry:NSm co-localized with ER–GFP (endoplasmic reticulum targeting peptide (HDEL peptide fused with GFP). Co-expression of NSm with ER–GFP showed that the ER-network was transformed into vesicles indicating that NSm interacts with ER and remodels it. Mutations in the conserved hydrophobic region of NSm (residues 130–138) did not abolish the formation of vesicles. Additionally, the conserved prolines at positions 140 and 142 were found to be essential for targeting the vesicles to the cell membrane. Further, systematic deletion of amino acid residues from N- and C-terminus demonstrated that N-terminal 203 amino acids are dispensable for the vesicle formation. On the other hand, the C-terminal coiled coil domain when expressed alone could also form vesicles. These results suggest that GBNV NSm remodels the ER network by forming vesicles via its interaction through the C-terminal coiled coil domain. Interestingly, NSm interacts with NP in vitro and coexpression of these two proteins in planta resulted in the relocalization of NP to PD and this relocalization was abolished when the N-terminal unfolded region of NSm was deleted. Thus, the NSm

  2. Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis

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    McDermott Jason E

    2012-04-01

    Full Text Available Abstract Background High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV infection. Results We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture. Conclusions The results presented show that the network topology of protein co

  3. Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

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    Arthur Brady

    Full Text Available As increasing amounts of high-throughput data for the yeast interactome become available, more system-wide properties are uncovered. One interesting question concerns the fault tolerance of protein interaction networks: whether there exist alternative pathways that can perform some required function if a gene essential to the main mechanism is defective, absent or suppressed. A signature pattern for redundant pathways is the BPM (between-pathway model motif, introduced by Kelley and Ideker. Past methods proposed to search the yeast interactome for BPM motifs have had several important limitations. First, they have been driven heuristically by local greedy searches, which can lead to the inclusion of extra genes that may not belong in the motif; second, they have been validated solely by functional coherence of the putative pathways using GO enrichment, making it difficult to evaluate putative BPMs in the absence of already known biological annotation. We introduce stable bipartite subgraphs, and show they form a clean and efficient way of generating meaningful BPMs which naturally discard extra genes included by local greedy methods. We show by GO enrichment measures that our BPM set outperforms previous work, covering more known complexes and functional pathways. Perhaps most importantly, since our BPMs are initially generated by examining the genetic-interaction network only, the location of edges in the protein-protein physical interaction network can then be used to statistically validate each candidate BPM, even with sparse GO annotation (or none at all. We uncover some interesting biological examples of previously unknown putative redundant pathways in such areas as vesicle-mediated transport and DNA repair.

  4. Phylogeny, Functional Annotation, and Protein Interaction Network Analyses of the Xenopus tropicalis Basic Helix-Loop-Helix Transcription Factors

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    Wuyi Liu

    2013-01-01

    Full Text Available The previous survey identified 70 basic helix-loop-helix (bHLH proteins, but it was proved to be incomplete, and the functional information and regulatory networks of frog bHLH transcription factors were not fully known. Therefore, we conducted an updated genome-wide survey in the Xenopus tropicalis genome project databases and identified 105 bHLH sequences. Among the retrieved 105 sequences, phylogenetic analyses revealed that 103 bHLH proteins belonged to 43 families or subfamilies with 46, 26, 11, 3, 15, and 4 members in the corresponding supergroups. Next, gene ontology (GO enrichment analyses showed 65 significant GO annotations of biological processes and molecular functions and KEGG pathways counted in frequency. To explore the functional pathways, regulatory gene networks, and/or related gene groups coding for Xenopus tropicalis bHLH proteins, the identified bHLH genes were put into the databases KOBAS and STRING to get the signaling information of pathways and protein interaction networks according to available public databases and known protein interactions. From the genome annotation and pathway analysis using KOBAS, we identified 16 pathways in the Xenopus tropicalis genome. From the STRING interaction analysis, 68 hub proteins were identified, and many hub proteins created a tight network or a functional module within the protein families.

  5. Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family

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    Dallakyan Sargis

    2008-08-01

    Full Text Available Abstract Background Gram-negative bacteria use periplasmic-binding proteins (bPBP to transport nutrients through the periplasm. Despite immense diversity within the recognized substrates, all members of the family share a common fold that includes two domains that are separated by a conserved hinge. The hinge allows the protein to cycle between open (apo and closed (ligated conformations. Conformational changes within the proteins depend on a complex interplay of mechanical and thermodynamic response, which is manifested as an increase in thermal stability and decrease of flexibility upon ligand binding. Results We use a distance constraint model (DCM to quantify the give and take between thermodynamic stability and mechanical flexibility across the bPBP family. Quantitative stability/flexibility relationships (QSFR are readily evaluated because the DCM links mechanical and thermodynamic properties. We have previously demonstrated that QSFR is moderately conserved across a mesophilic/thermophilic RNase H pair, whereas the observed variance indicated that different enthalpy-entropy mechanisms allow similar mechanical response at their respective melting temperatures. Our predictions of heat capacity and free energy show marked diversity across the bPBP family. While backbone flexibility metrics are mostly conserved, cooperativity correlation (long-range couplings also demonstrate considerable amount of variation. Upon ligand removal, heat capacity, melting point, and mechanical rigidity are, as expected, lowered. Nevertheless, significant differences are found in molecular cooperativity correlations that can be explained by the detailed nature of the hydrogen bond network. Conclusion Non-trivial mechanical and thermodynamic variation across the family is explained by differences within the underlying H-bond networks. The mechanism is simple; variation within the H-bond networks result in altered mechanical linkage properties that directly affect

  6. Identification of Top-ranked Proteins within a Directional Protein Interaction Network using the PageRank Algorithm: Applications in Humans and Plants.

    Science.gov (United States)

    Li, Xiu-Qing; Xing, Tim; Du, Donglei

    2016-01-01

    Somatic mutation of signal transduction genes or key nodes of the cellular protein network can cause severe diseases in humans but can sometimes genetically improve plants, likely because growth is determinate in animals but indeterminate in plants. This article reviews protein networks; human protein ranking; the mitogen-activated protein kinase (MAPK) and insulin (phospho- inositide 3kinase [PI3K]/phosphatase and tensin homolog [PTEN]/protein kinase B [AKT]) signaling pathways; human diseases caused by somatic mutations to the PI3K/PTEN/ AKT pathway; use of the MAPK pathway in plant molecular breeding; and protein domain evolution. Casitas B-lineage lymphoma (CBL), PTEN, MAPK1 and PIK3CA are among PIK3CA the top-ranked proteins in directional rankings. Eight proteins (ACVR1, CDC42, RAC1, RAF1, RHOA, TGFBR1, TRAF2, and TRAF6) are ranked in the top 50 key players in both signal emission and signal reception and in interaction with many other proteins. Top-ranked proteins likely have major impacts on the network function. Such proteins are targets for drug discovery, because their mutations are implicated in various cancers and overgrowth syndromes. Appropriately managing food intake may help reduce the growth of tumors or malformation of tissues. The role of the protein kinase C/ fatty acid synthase pathway in fat deposition in PTEN/PI3K patients should be investigated. Both the MAPK and insulin signaling pathways exist in plants, and MAPK pathway engineering can improve plant tolerance to biotic and abiotic stresses such as salinity. PMID:26637164

  7. omega-Helices in proteins.

    Science.gov (United States)

    Enkhbayar, Purevjav; Boldgiv, Bazartseren; Matsushima, Norio

    2010-05-01

    A modification of the alpha-helix, termed the omega-helix, has four residues in one turn of a helix. We searched the omega-helix in proteins by the HELFIT program which determines the helical parameters-pitch, residues per turn, radius, and handedness-and p = rmsd/(N - 1)(1/2) estimating helical regularity, where "rmsd" is the root mean square deviation from the best fit helix and "N" is helix length. A total of 1,496 regular alpha-helices 6-9 residues long with p < or = 0.10 A were identified from 866 protein chains. The statistical analysis provides a strong evidence that the frequency distribution of helices versus n indicates the bimodality of typical alpha-helix and omega-helix. Sixty-two right handed omega-helices identified (7.2% of proteins) show non-planarity of the peptide groups. There is amino acid preference of Asp and Cys. These observations and analyses insist that the omega-helices occur really in proteins.

  8. omega-Helices in proteins.

    Science.gov (United States)

    Enkhbayar, Purevjav; Boldgiv, Bazartseren; Matsushima, Norio

    2010-05-01

    A modification of the alpha-helix, termed the omega-helix, has four residues in one turn of a helix. We searched the omega-helix in proteins by the HELFIT program which determines the helical parameters-pitch, residues per turn, radius, and handedness-and p = rmsd/(N - 1)(1/2) estimating helical regularity, where "rmsd" is the root mean square deviation from the best fit helix and "N" is helix length. A total of 1,496 regular alpha-helices 6-9 residues long with p < or = 0.10 A were identified from 866 protein chains. The statistical analysis provides a strong evidence that the frequency distribution of helices versus n indicates the bimodality of typical alpha-helix and omega-helix. Sixty-two right handed omega-helices identified (7.2% of proteins) show non-planarity of the peptide groups. There is amino acid preference of Asp and Cys. These observations and analyses insist that the omega-helices occur really in proteins. PMID:20496104

  9. Water molecule network and active site flexibility of apo protein tyrosine phosphatase 1B

    DEFF Research Database (Denmark)

    Pedersen, A.K.; Peters, Günther H.J.; Møller, K.B.;

    2004-01-01

    Protein tyrosine phosphatase 1B (PTP1B) plays a key role as a negative regulator of insulin and leptin signalling and is therefore considered to be an important molecular target for the treatment of type 2 diabetes and obesity. Detailed structural information about the structure of PTP1B, including...... the conformation and flexibility of active-site residues as well as the water-molecule network, is a key issue in understanding ligand binding and enzyme kinetics and in structure-based drug design. A 1.95 Angstrom apo PTP1B structure has been obtained, showing four highly coordinated water molecules...

  10. Hybrids of the bHLH and bZIP protein motifs display different DNA-binding activities in vivo vs. in vitro.

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    Hiu-Kwan Chow

    Full Text Available Minimalist hybrids comprising the DNA-binding domain of bHLH/PAS (basic-helix-loop-helix/Per-Arnt-Sim protein Arnt fused to the leucine zipper (LZ dimerization domain from bZIP (basic region-leucine zipper protein C/EBP were designed to bind the E-box DNA site, CACGTG, targeted by bHLHZ (basic-helix-loop-helix-zipper proteins Myc and Max, as well as the Arnt homodimer. The bHLHZ-like structure of ArntbHLH-C/EBP comprises the Arnt bHLH domain fused to the C/EBP LZ: i.e. swap of the 330 aa PAS domain for the 29 aa LZ. In the yeast one-hybrid assay (Y1H, transcriptional activation from the E-box was strong by ArntbHLH-C/EBP, and undetectable for the truncated ArntbHLH (PAS removed, as detected via readout from the HIS3 and lacZ reporters. In contrast, fluorescence anisotropy titrations showed affinities for the E-box with ArntbHLH-C/EBP and ArntbHLH comparable to other transcription factors (K(d 148.9 nM and 40.2 nM, respectively, but only under select conditions that maintained folded protein. Although in vivo yeast results and in vitro spectroscopic studies for ArntbHLH-C/EBP targeting the E-box correlate well, the same does not hold for ArntbHLH. As circular dichroism confirms that ArntbHLH-C/EBP is a much more strongly alpha-helical structure than ArntbHLH, we conclude that the nonfunctional ArntbHLH in the Y1H must be due to misfolding, leading to the false negative that this protein is incapable of targeting the E-box. Many experiments, including protein design and selections from large libraries, depend on protein domains remaining well-behaved in the nonnative experimental environment, especially small motifs like the bHLH (60-70 aa. Interestingly, a short helical LZ can serve as a folding- and/or solubility-enhancing tag, an important device given the focus of current research on exploration of vast networks of biomolecular interactions.

  11. Structure of the hypothetical Mycoplasma protein, MPN555, suggestsa chaperone function

    Energy Technology Data Exchange (ETDEWEB)

    Schulze-Gahmen, Ursula; Aono, Shelly; Chen, Shengfeng; Yokota,Hisao; Kim, Rosalind; Kim, Sung-Hou

    2005-06-15

    The crystal structure of the hypothetical protein MPN555from Mycoplasma pneumoniae (gi pbar 1673958) has been determined to a resolution of 2.8 Angstrom using anomalous diffraction data at the Sepeak wavelength. Structure determination revealed a mostly alpha-helical protein with a three-lobed shape. The three lobes or fingers delineate a central binding groove and additional grooves between lobes 1 and 3, and between lobes 2 and 3. For one of the molecules in the asymmetric unit,the central binding pocket was filled with a peptide from the uncleaved N-terminal affinity tag. The MPN555 structure has structural homology to two bacterial chaperone proteins, SurA and trigger factor from Escherichia coli. The structural data and the homology to other chaperone for MPN555.

  12. Identification of efflux proteins using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

    Science.gov (United States)

    Ou, Yu-Yen; Chen, Shu-An; Chang, Yun-Min; Velmurugan, Devadasan; Fukui, Kazuhiko; Michael Gromiha, M

    2013-09-01

    Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.

  13. A Network of Multi-Tasking Proteins at the DNA Replication Fork Preserves Genome Stability.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available To elucidate the network that maintains high fidelity genome replication, we have introduced two conditional mutant alleles of DNA2, an essential DNA replication gene, into each of the approximately 4,700 viable yeast deletion mutants and determined the fitness of the double mutants. Fifty-six DNA2-interacting genes were identified. Clustering analysis of genomic synthetic lethality profiles of each of 43 of the DNA2-interacting genes defines a network (consisting of 322 genes and 876 interactions whose topology provides clues as to how replication proteins coordinate regulation and repair to protect genome integrity. The results also shed new light on the functions of the query gene DNA2, which, despite many years of study, remain controversial, especially its proposed role in Okazaki fragment processing and the nature of its in vivo substrates. Because of the multifunctional nature of virtually all proteins at the replication fork, the meaning of any single genetic interaction is inherently ambiguous. The multiplexing nature of the current studies, however, combined with follow-up supporting experiments, reveals most if not all of the unique pathways requiring Dna2p. These include not only Okazaki fragment processing and DNA repair but also chromatin dynamics.

  14. A conserved protein interaction network involving the yeast MAP kinases Fus3 and Kss1.

    Science.gov (United States)

    Kusari, Anasua B; Molina, Douglas M; Sabbagh, Walid; Lau, Chang S; Bardwell, Lee

    2004-01-19

    The Saccharomyces cerevisiae mitogen-activated protein kinases (MAPKs) Fus3 and Kss1 bind to multiple regulators and substrates. We show that mutations in a conserved docking site in these MAPKs (the CD/7m region) disrupt binding to an important subset of their binding partners, including the Ste7 MAPK kinase, the Ste5 adaptor/scaffold protein, and the Dig1 and Dig2 transcriptional repressors. Supporting the possibility that Ste5 and Ste7 bind to the same region of the MAPKs, they partially competed for Fus3 binding. In vivo, some of the MAPK mutants displayed reduced Ste7-dependent phosphorylation, and all of them exhibited multiple defects in mating and pheromone response. The Kss1 mutants were also defective in Kss1-imposed repression of Ste12. We conclude that MAPKs contain a structurally and functionally conserved docking site that mediates an overall positively acting network of interactions with cognate docking sites on their regulators and substrates. Key features of this interaction network appear to have been conserved from yeast to humans. PMID:14734536

  15. Protein pheromone expression levels predict and respond to the formation of social dominance networks.

    Science.gov (United States)

    Nelson, A C; Cunningham, C B; Ruff, J S; Potts, W K

    2015-06-01

    Communication signals are key regulators of social networks and are thought to be under selective pressure to honestly reflect social status, including dominance status. The odours of dominants and nondominants differentially influence behaviour, and identification of the specific pheromones associated with, and predictive of, dominance status is essential for understanding the mechanisms of network formation and maintenance. In mice, major urinary proteins (MUPs) are excreted in extraordinary large quantities and expression level has been hypothesized to provide an honest signal of dominance status. Here, we evaluate whether MUPs are associated with dominance in wild-derived mice by analysing expression levels before, during and after competition for reproductive resources over 3 days. During competition, dominant males have 24% greater urinary MUP expression than nondominants. The MUP darcin, a pheromone that stimulates female attraction, is predictive of dominance status: dominant males have higher darcin expression before competition. Dominants also have a higher ratio of darcin to other MUPs before and during competition. These differences appear transient, because there are no differences in MUPs or darcin after competition. We also find MUP expression is affected by sire dominance status: socially naive sons of dominant males have lower MUP expression, but this apparent repression is released during competition. A requisite condition for the evolution of communication signals is honesty, and we provide novel insight into pheromones and social networks by showing that MUP and darcin expression is a reliable signal of dominance status, a primary determinant of male fitness in many species.

  16. Uncovering the Molecular Mechanism of Actions between Pharmaceuticals and Proteins on the AD Network.

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    Shujuan Cao

    Full Text Available This study begins with constructing the mini metabolic networks (MMNs of beta amyloid (Aβ and acetylcholine (ACh which stimulate the Alzheimer's Disease (AD. Then we generate the AD network by incorporating MMNs of Aβ and ACh, and other MMNs of stimuli of AD. The panel of proteins contains 49 enzymes/receptors on the AD network which have the 3D-structure in PDB. The panel of drugs is formed by 5 AD drugs and 5 AD nutraceutical drugs, and 20 non-AD drugs. All of these complexes formed by these 30 drugs and 49 proteins are transformed into dyadic arrays. Utilizing the prior knowledge learned from the drug panel, we propose a statistical classification (dry-lab. According to the wet-lab for the complex of amiloride and insulin degrading enzyme, and the complex of amiloride and neutral endopeptidase, we are confident that this dry-lab is reliable. As the consequences of the dry-lab, we discover many interesting implications. Especially, we show that possible causes of Tacrine, donepezil, galantamine and huperzine A cannot improve the level of ACh which is against to their original design purpose but they still prevent AD to be worse as Aβ deposition appeared. On the other hand, we recommend Miglitol and Atenolol as the safe and potent drugs to improve the level of ACh before Aβ deposition appearing. Moreover, some nutrients such as NADH and Vitamin E should be controlled because they may harm health if being used in wrong way and wrong time. Anyway, the insights shown in this study are valuable to be developed further.

  17. A protein network-guided screen for cell cycle regulators in Drosophila

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    Kashat Maria A

    2011-05-01

    Full Text Available Abstract Background Large-scale RNAi-based screens are playing a critical role in defining sets of genes that regulate specific cellular processes. Numerous screens have been completed and in some cases more than one screen has examined the same cellular process, enabling a direct comparison of the genes identified in separate screens. Surprisingly, the overlap observed between the results of similar screens is low, suggesting that RNAi screens have relatively high levels of false positives, false negatives, or both. Results We re-examined genes that were identified in two previous RNAi-based cell cycle screens to identify potential false positives and false negatives. We were able to confirm many of the originally observed phenotypes and to reveal many likely false positives. To identify potential false negatives from the previous screens, we used protein interaction networks to select genes for re-screening. We demonstrate cell cycle phenotypes for a significant number of these genes and show that the protein interaction network is an efficient predictor of new cell cycle regulators. Combining our results with the results of the previous screens identified a group of validated, high-confidence cell cycle/cell survival regulators. Examination of the subset of genes from this group that regulate the G1/S cell cycle transition revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 (eIF3 complex, the COP9 signalosome, and the proteasome lid. Using a combinatorial RNAi approach, we show that while all three of these complexes are required for Cdk2/Cyclin E activity, the eIF3 complex is specifically required for some other step that limits the G1/S cell cycle transition. Conclusions Our results show that false positives and false negatives each play a significant role in the lack of overlap that is observed between similar large-scale RNAi-based screens. Our results

  18. Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

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    Chen Xin

    2012-10-01

    Full Text Available Abstract Background The identification of genes that predict in vitro cellular chemosensitivity of cancer cells is of great importance. Chemosensitivity related genes (CRGs have been widely utilized to guide clinical and cancer chemotherapy decisions. In addition, CRGs potentially share functional characteristics and network features in protein interaction networks (PPIN. Methods In this study, we proposed a method to identify CRGs based on Gene Ontology (GO and PPIN. Firstly, we documented 150 pairs of drug-CCRG (curated chemosensitivity related gene from 492 published papers. Secondly, we characterized CCRGs from the perspective of GO and PPIN. Thirdly, we prioritized CRGs based on CCRGs’ GO and network characteristics. Lastly, we evaluated the performance of the proposed method. Results We found that CCRG enriched GO terms were most often related to chemosensitivity and exhibited higher similarity scores compared to randomly selected genes. Moreover, CCRGs played key roles in maintaining the connectivity and controlling the information flow of PPINs. We then prioritized CRGs using CCRG enriched GO terms and CCRG network characteristics in order to obtain a database of predicted drug-CRGs that included 53 CRGs, 32 of which have been reported to affect susceptibility to drugs. Our proposed method identifies a greater number of drug-CCRGs, and drug-CCRGs are much more significantly enriched in predicted drug-CRGs, compared to a method based on the correlation of gene expression and drug activity. The mean area under ROC curve (AUC for our method is 65.2%, whereas that for the traditional method is 55.2%. Conclusions Our method not only identifies CRGs with expression patterns strongly correlated with drug activity, but also identifies CRGs in which expression is weakly correlated with drug activity. This study provides the framework for the identification of signatures that predict in vitro cellular chemosensitivity and offers a valuable

  19. Interactive protein network of FXIII-A1 in lipid rafts of activated and non-activated platelets.

    Science.gov (United States)

    Rabani, Vahideh; Montange, Damien; Davani, Siamak

    2016-09-01

    Lipid-rafts are defined as membrane microdomains enriched in cholesterol and glycosphingolipids within platelet plasma membrane. Lipid raft-mediated clot retraction requires factor XIII and other interacting proteins. The aim of this study was to investigate the proteins that interact with factor XIII in raft and non-raft domains of activated and non-activated platelet plasma membrane. By lipidomics analysis, we identified cholesterol- and sphingomyelin-enriched areas as lipid rafts. Platelets were activated by thrombin. Proteomics analysis provided an overview of the pathways in which proteins of rafts and non-rafts participated in the interaction network of FXIII-A1, a catalytic subunit of FXIII. "Platelet activation" was the principal pathway among KEGG pathways for proteins of rafts, both before and after activation. Network analysis showed four types of interactions (activation, binding, reaction, and catalysis) in raft and non-raft domains in interactive network of FXIII-A1. FXIII-A1 interactions with other proteins in raft domains and their role in homeostasis highlight the specialization of the raft domain in clot retraction via the Factor XIII protein network.

  20. Pseudo 5D HN(C)N Experiment to Facilitate the Assignment of Backbone Resonances in Proteins Exhibiting High Backbone Shift Degeneracy

    CERN Document Server

    Kumar, Dinesh; Shukla, Vaibhav Kumar; Pandey, Himanshu; Arora, Ashish; Guleria, Anupam

    2014-01-01

    Assignment of protein backbone resonances is most routinely carried out using triple resonance three dimensional NMR experiments involving amide 1H and 15N resonances. However for intrinsically unstructured proteins, alpha-helical proteins or proteins containing several disordered fragments, the assignment becomes problematic because of high degree of backbone shift degeneracy. In this backdrop, a novel reduced dimensionality (RD) experiment -(5,3)D-hNCO-CANH- is presented to facilitate (and/or to validate) the sequential backbone resonance assignment in such proteins. The proposed 3D NMR experiment makes use of the modulated amide 15N chemical shifts (resulting from the joint sampling along both its indirect dimensions) to resolve the ambiguity involved in connecting the neighboring amide resonances (i.e. HiNi and Hi-1Ni-1) for overlapping amide NH peaks. The experiment -encoding 5D spectral information- leads to a conventional 3D spectrum with significantly reduced spectral crowding and complexity. The impr...

  1. Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma

    Institute of Scientific and Technical Information of China (English)

    WANG Jia-xiang; ZHANG Bo; YU Jie-kai; LIU Jian; YANG Mei-qin; ZHENG Shu

    2005-01-01

    Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis.Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage.In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed.Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma.Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers.The samples were randomly assigned into a training group (n=70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n=36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls).An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested.The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection.The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group.Matrix transfer was performed after data were output.Then the data were input into the previously built artificial neural network model to get the prediction value.The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering.The first threshold for noise filtering was set at 5, and the second was set at 2.The 10% was the minimum

  2. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks.

    Directory of Open Access Journals (Sweden)

    Tao Huang

    Full Text Available The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein's metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein's metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1 integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2 using the mRMR (Maximum Relevance & Minimum Redundancy principle and the IFS (Incremental Feature Selection procedure to optimize the prediction engine, and (3 being able to identify proteins among the four types: "short", "medium", "long", and "extra-long" half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1 KEGG enrichment scores of the protein and its neighbors in network, (2 subcellular locations, (3 polarity, (4 amino acids composition, (5 hydrophobicity, (6 secondary structure propensity, and (7 the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.

  3. Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.

    Science.gov (United States)

    García-Alonso, Luz; Alonso, Roberto; Vidal, Enrique; Amadoz, Alicia; de María, Alejandro; Minguez, Pablo; Medina, Ignacio; Dopazo, Joaquín

    2012-11-01

    Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network. PMID:22844098

  4. De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity.

    Science.gov (United States)

    Boyken, Scott E; Chen, Zibo; Groves, Benjamin; Langan, Robert A; Oberdorfer, Gustav; Ford, Alex; Gilmore, Jason M; Xu, Chunfu; DiMaio, Frank; Pereira, Jose Henrique; Sankaran, Banumathi; Seelig, Georg; Zwart, Peter H; Baker, David

    2016-05-01

    In nature, structural specificity in DNA and proteins is encoded differently: In DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here, we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen-bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen-bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen-bond networks with atomic accuracy enables the programming of protein interaction specificity for a broad range of synthetic biology applications; more generally, our results demonstrate that, even with the tremendous diversity observed in nature, there are fundamentally new modes of interaction to be discovered in proteins. PMID:27151862

  5. Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts

    Directory of Open Access Journals (Sweden)

    Spackman K

    2005-04-01

    Full Text Available Abstract Background Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. Results Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall, with high efficiency in the use of seed pairs. Conclusion The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

  6. Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

    Science.gov (United States)

    Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

    2015-12-01

    Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine.

  7. Relating diseases by integrating gene associations and information flow through protein interaction network.

    Science.gov (United States)

    Hamaneh, Mehdi Bagheri; Yu, Yi-Kuo

    2014-01-01

    Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.

  8. Mapping of the RNA recognition site of Escherichia coli ribosomal protein S7.

    Science.gov (United States)

    Robert, F; Gagnon, M; Sans, D; Michnick, S; Brakier-Gingras, L

    2000-11-01

    Bacterial ribosomal protein S7 initiates the folding of the 3' major domain of 16S ribosomal RNA by binding to its lower half. The X-ray structure of protein S7 from thermophilic bacteria was recently solved and found to be a modular structure, consisting of an alpha-helical domain with a beta-ribbon extension. To gain further insights into its interaction with rRNA, we cloned the S7 gene from Escherichia coli K12 into a pET expression vector and introduced 4 deletions and 12 amino acid substitutions in the protein sequence. The binding of each mutant to the lower half of the 3' major domain of 16S rRNA was assessed by filtration on nitrocellulose membranes. Deletion of the N-terminal 17 residues or deletion of the B hairpins (residues 72-89) severely decreased S7 affinity for the rRNA. Truncation of the C-terminal portion (residues 138-178), which includes part of the terminal alpha-helix, significantly affected S7 binding, whereas a shorter truncation (residues 148-178) only marginally influenced its binding. Severe effects were also observed with several strategic point mutations located throughout the protein, including Q8A and F17G in the N-terminal region, and K35Q, G54S, K113Q, and M115G in loops connecting the alpha-helices. Our results are consistent with the occurrence of several sites of contact between S7 and the 16S rRNA, in line with its role in the folding of the 3' major domain.

  9. Detection of regulatory circuits by integrating the cellular networks of protein–protein interactions and transcription regulation

    OpenAIRE

    Yeger-Lotem, Esti; Margalit, Hanah

    2003-01-01

    The post-genomic era is marked by huge amounts of data generated by large-scale functional genomic and proteomic experiments. A major challenge is to integrate the various types of genome-scale information in order to reveal the intra- and inter- relationships between genes and proteins that constitute a living cell. Here we present a novel application of classical graph algorithms to integrate the cellular networks of protein–protein interactions and transcription regulation. We demonstrate ...

  10. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

    OpenAIRE

    Yanxiong Gan; Shichao Zheng; Jan P A Baak; Silei Zhao; Yongfeng Zheng; Nini Luo; Wan Liao; Chaomei Fu

    2015-01-01

    Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein–protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis bas...

  11. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

    OpenAIRE

    Nielsen Jens; Jouhten Paula; Nandy Subir K

    2010-01-01

    Abstract Background Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. Results Here we des...

  12. Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11

    Science.gov (United States)

    Liu, Tong; Wang, Yiheng; Eickholt, Jesse; Wang, Zheng

    2016-01-01

    Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolute residue-specific quality of an individual model. Here, we have developed four novel single-model methods (Wang_deep_1, Wang_deep_2, Wang_deep_3, and Wang_SVM) based on stacked denoising autoencoders (SdAs) and support vector machines (SVMs). We evaluated these four methods along with six other methods participating in CASP11 at the global and local levels using Pearson’s correlation coefficients and ROC analysis. As for residue-specific quality assessment, our four methods achieved better performance than most of the six other CASP11 methods in distinguishing the reliably modeled residues from the unreliable measured by ROC analysis; and our SdA-based method Wang_deep_1 has achieved the highest accuracy, 0.77, compared to SVM-based methods and our ensemble of an SVM and SdAs. However, we found that Wang_deep_2 and Wang_deep_3, both based on an ensemble of multiple SdAs and an SVM, performed slightly better than Wang_deep_1 in terms of ROC analysis, indicating that integrating an SVM with deep networks works well in terms of certain measurements. PMID:26763289

  13. Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11

    Science.gov (United States)

    Liu, Tong; Wang, Yiheng; Eickholt, Jesse; Wang, Zheng

    2016-01-01

    Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolute residue-specific quality of an individual model. Here, we have developed four novel single-model methods (Wang_deep_1, Wang_deep_2, Wang_deep_3, and Wang_SVM) based on stacked denoising autoencoders (SdAs) and support vector machines (SVMs). We evaluated these four methods along with six other methods participating in CASP11 at the global and local levels using Pearson’s correlation coefficients and ROC analysis. As for residue-specific quality assessment, our four methods achieved better performance than most of the six other CASP11 methods in distinguishing the reliably modeled residues from the unreliable measured by ROC analysis; and our SdA-based method Wang_deep_1 has achieved the highest accuracy, 0.77, compared to SVM-based methods and our ensemble of an SVM and SdAs. However, we found that Wang_deep_2 and Wang_deep_3, both based on an ensemble of multiple SdAs and an SVM, performed slightly better than Wang_deep_1 in terms of ROC analysis, indicating that integrating an SVM with deep networks works well in terms of certain measurements.

  14. CCM2-CCM3 interaction stabilizes their protein expression and permits endothelial network formation

    Energy Technology Data Exchange (ETDEWEB)

    Draheim, Kyle M.; Li, Xiaofeng; Zhang, Rong; Fisher, Oriana S.; Villari, Giulia; Boggon, Titus J.; Calderwood, David A. [Yale

    2015-04-21

    Mutations in the essential adaptor proteins CCM2 or CCM3 lead to cerebral cavernous malformations (CCM), vascular lesions that most frequently occur in the brain and are strongly associated with hemorrhagic stroke, seizures, and other neurological disorders. CCM2 binds CCM3, but the molecular basis of this interaction, and its functional significance, have not been elucidated. Here, we used x-ray crystallography and structure-guided mutagenesis to show that an α-helical LD-like motif within CCM2 binds the highly conserved “HP1” pocket of the CCM3 focal adhesion targeting (FAT) homology domain. By knocking down CCM2 or CCM3 and rescuing with binding-deficient mutants, we establish that CCM2–CCM3 interactions protect CCM2 and CCM3 proteins from proteasomal degradation and show that both CCM2 and CCM3 are required for normal endothelial cell network formation. However, CCM3 expression in the absence of CCM2 is sufficient to support normal cell growth, revealing complex-independent roles for CCM3.

  15. Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development

    DEFF Research Database (Denmark)

    Hansen, Kasper Lage; Greenway, Steven C.; Rosenfeld, Jill A.;

    2012-01-01

    in protein networks driving the development of specific anatomical structures (e.g., outflow tract, ventricular septum, and atrial septum) that are malformed by CHD. This integrative analysis of CHD risk factors and responses suggests a complex pattern of functional interactions between genomic variation...

  16. Proteometabolomic Study of Compatible Interaction in Tomato Fruit Challenged with Sclerotinia rolfsii Illustrates Novel Protein Network during Disease Progression.

    Science.gov (United States)

    Ghosh, Sudip; Narula, Kanika; Sinha, Arunima; Ghosh, Rajgourab; Jawa, Priyanka; Chakraborty, Niranjan; Chakraborty, Subhra

    2016-01-01

    Fruit is an assimilator of metabolites, nutrients, and signaling molecules, thus considered as potential target for pathogen attack. In response to patho-stress, such as fungal invasion, plants reorganize their proteome, and reconfigure their physiology in the infected organ. This remodeling is coordinated by a poorly understood signal transduction network, hormonal cascades, and metabolite reallocation. The aim of the study was to explore organ-based proteomic alterations in the susceptibility of heterotrophic fruit to necrotrophic fungal attack. We conducted time-series protein profiling of Sclerotinia rolfsii invaded tomato (Solanum lycopersicum) fruit. The differential display of proteome revealed 216 patho-stress responsive proteins (PSRPs) that change their abundance by more than 2.5-fold. Mass spectrometric analyses led to the identification of 56 PSRPs presumably involved in disease progression; regulating diverse functions viz. metabolism, signaling, redox homeostasis, transport, stress-response, protein folding, modification and degradation, development. Metabolome study indicated differential regulation of organic acid, amino acids, and carbohydrates paralleling with the proteomics analysis. Further, we interrogated the proteome data using network analysis that identified two significant functional protein hubs centered around malate dehydrogenase, T-complex protein 1 subunit gamma, and ATP synthase beta. This study reports, for the first-time, kinetically controlled patho-stress responsive protein network during post-harvest storage in a sink tissue, particularly fruit and constitute the basis toward understanding the onset and context of disease signaling and metabolic pathway alterations. The network representation may facilitate the prioritization of candidate proteins for quality improvement in storage organ. PMID:27507973

  17. Pathway Detection from Protein Interaction Networks and Gene Expression Data Using Color-Coding Methods and A* Search Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yu Yeh

    2012-01-01

    Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.

  18. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviours

    Directory of Open Access Journals (Sweden)

    Daria eMolodtsova

    2014-12-01

    Full Text Available It is increasingly apparent that genes and networks that influence complex behaviour are evolutionary conserved, which is paradoxical considering that behaviour is labile over evolutionary timescales. How does adaptive change in behaviour arise if behaviour is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behaviour, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behaviour of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behaviour can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network.

  19. A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network

    Directory of Open Access Journals (Sweden)

    De Graaf David

    2007-07-01

    Full Text Available Abstract Background The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. Results Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. Conclusion We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

  20. Predicting free energy contributions to the conformational stability of folded proteins from the residue sequence with radial basis function networks

    Energy Technology Data Exchange (ETDEWEB)

    Casadio, R.; Fariselli, P.; Vivarelli, F. [Univ. of Bologna (Italy); Compiani, M. [Univ. of Camerino (Italy)

    1995-12-31

    Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from crystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental {Delta}G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol{sup -1} rather independently of the residue sequence, with an average error per protein of about 9 kcal mol{sup -1}.

  1. Predicting free energy contributions to the conformational stability of folded proteins from the residue sequence with radial basis function networks.

    Science.gov (United States)

    Casadio, R; Compiani, M; Fariselli, P; Vivarelli, F

    1995-01-01

    Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from chrystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental delta G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol-1 rather independently of the residue sequence, with an average error per protein of about 9 kcal mol-1.

  2. α-Helical to β-Helical Conformation Change in the C-Terminal of the Mammalian Prion Protein

    Science.gov (United States)

    Singh, Jesse; Whitford, Paul; Hayre, Natha; Cox, Daniel; Onuchic, José.

    2011-03-01

    We employ all-atom structure-based models with mixed basis contact maps to explore whether there are any significant geometric or energetic constraints limiting conjectured conformational transitions between the alpha-helical (α H) and the left handed beta helical (LHBH) conformations for the C-terminal (residues 166-226) of the mammalian prion protein. The LHBH structure has been proposed to describe infectious oligomers and one class of in vitro grown fibrils, as well as possibly self- templating the conversion of normal cellular prion protein to the infectious form. Our results confirm that the kinetics of the conformation change are not strongely limited by large scale geometry modification and there exists an overall preference for the LHBH conformation.

  3. Activated PTHLH Coupling Feedback Phosphoinositide to G-Protein Receptor Signal-Induced Cell Adhesion Network in Human Hepatocellular Carcinoma by Systems-Theoretic Analysis

    Directory of Open Access Journals (Sweden)

    Lin Wang

    2012-01-01

    Full Text Available Studies were done on analysis of biological processes in the same high expression (fold change ≥2 activated PTHLH feedback-mediated cell adhesion gene ontology (GO network of human hepatocellular carcinoma (HCC compared with the corresponding low expression activated GO network of no-tumor hepatitis/cirrhotic tissues (HBV or HCV infection. Activated PTHLH feedback-mediated cell adhesion network consisted of anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolism, cell adhesion, cell differentiation, cell-cell signaling, G-protein-coupled receptor protein signaling pathway, intracellular transport, metabolism, phosphoinositide-mediated signaling, positive regulation of transcription, regulation of cyclin-dependent protein kinase activity, regulation of transcription, signal transduction, transcription, and transport in HCC. We proposed activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network. Our hypothesis was verified by the different activated PTHLH feedback-mediated cell adhesion GO network of HCC compared with the corresponding inhibited GO network of no-tumor hepatitis/cirrhotic tissues, or the same compared with the corresponding inhibited GO network of HCC. Activated PTHLH coupling feedback phosphoinositide to G-protein receptor signal-induced cell adhesion network included BUB1B, GNG10, PTHR2, GNAZ, RFC4, UBE2C, NRXN3, BAP1, PVRL2, TROAP, and VCAN in HCC from GEO dataset using gene regulatory network inference method and our programming.

  4. Comparative Analysis of SWIRM Domain-Containing Proteins in Plants

    Directory of Open Access Journals (Sweden)

    Yan Gao

    2012-01-01

    Full Text Available Chromatin-remodeling complexes affect gene expression by using the energy of ATP hydrolysis to locally disrupt or alter the association of histones with DNA. SWIRM (Swi3p, Rsc8p, and Moira domain is an alpha-helical domain of about 85 residues in chromosomal proteins. SWIRM domain-containing proteins make up large multisubunit complexes by interacting with other chromatin modification factors and may have an important function in plants. However, little is known about SWIRM domain-containing proteins in plants. In this study, 67 SWIRM domain-containing proteins from 6 plant species were identified and analyzed. Plant SWIRM domain proteins can be divided into three distinct types: Swi-type, LSD1-type, and Ada2-type. Generally, the SWIRM domain forms a helix-turn-helix motif commonly found in DNA-binding proteins. The genes encoding SWIRM domain proteins in Oryza sativa are widely expressed, especially in pistils. In addition, OsCHB701 and OsHDMA701 were downregulated by cold stress, whereas OsHDMA701 and OsHDMA702 were significantly induced by heat stress. These observations indicate that SWIRM domain proteins may play an essential role in plant development and plant responses to environmental stress.

  5. The physics of protein-DNA interaction networks in the control of gene expression

    International Nuclear Information System (INIS)

    Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA. (topical review)

  6. Computational Prediction of Protein–Protein Interaction Networks: Algo-rithms and Resources

    OpenAIRE

    Zahiri, Javad; Bozorgmehr, Joseph Hannon; Masoudi-Nejad, Ali

    2013-01-01

    Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computation...

  7. Comparative studies of mitochondrial proteomics reveal an intimate protein network of male sterility in wheat (Triticum aestivum L.)

    Science.gov (United States)

    Wang, Shuping; Zhang, Gaisheng; Zhang, Yingxin; Song, Qilu; Chen, Zheng; Wang, Junsheng; Guo, Jialin; Niu, Na; Wang, Junwei; Ma, Shoucai

    2015-01-01

    Plant male sterility has often been associated with mitochondrial dysfunction; however, the mechanism in wheat (Triticum aestivum L.) has not been elucidated. This study set out to probe the mechanism of physiological male sterility (PHYMS) induced by the chemical hybridizing agent (CHA)-SQ-1, and cytoplasmic male sterility (CMS) of wheat at the proteomic level. A total of 71 differentially expressed mitochondrial proteins were found to be involved in pollen abortion and further identified by MALDI-TOF/TOF MS (matrix-assisted laser desorption/ionization-time of fight/time of flight mass spectrometry). These proteins were implicated in different cellular responses and metabolic processes, with obvious functional tendencies toward the tricarboxylic acid cycle, the mitochondrial electron transport chain, protein synthesis and degradation, oxidation stress, the cell division cycle, and epigenetics. Interactions between identified proteins were demonstrated by bioinformatics analysis, enabling a more complete insight into biological pathways involved in anther abortion and pollen defects. Accordingly, a mitochondria-mediated male sterility protein network in wheat is proposed; this network was further confirmed by physiological data, RT-PCR (real-time PCR), and TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling) assay. The results provide intriguing insights into the metabolic pathway of anther abortion induced by CHA-SQ-1 and also give useful clues to identify the crucial proteins of PHYMS and CMS in wheat. PMID:26136264

  8. AtGRIP protein locates to the secretory vesicles of trans Golgi-network in Arabidopsis root cap cells

    Institute of Scientific and Technical Information of China (English)

    CHEN Ying; ZHANG Wei; ZHAO Lei; LI Yan

    2008-01-01

    GRIP domain proteins, locating to the trans-Golgi network, are thought to play an essential role in Golgi apparatus trafficking in yeast and animal cells. In the present study, AtGRIP cDNA was amplified by reverse transcriptase PCR from RNA isolated from Arabidopsis seedling. The GST fusion protein of AtGRIP was affinity-purified and its rabbit polyclonal antibody was obtained. Immuno-blotting with the purified anti-AtGRIP polyclonal antibody demonstrated that the molecular mass of AtGRIP protein is about 92 kD, and its expression is not tissue-specific in Arabidopsis. Immunoflourescent labeling and confocal microscopy revealed that the AtGRIP protein was co-localized with Golgi stacks in Arabidop-sis root cells. Immuno-gold labeling and electron microscopy observation showed that AtGRIP protein was mainly located to the membrane of the secretory vesicles of trans-Golgi network in Arabidopsis root cap cells. Taken together, these results indicate that the localization of GRIP domain proteins be-tween plants and animal cells are conserved. These results also suggest that the AtGRIP may be in-volved in regulating the formation or sorting of Golgi-associated vesicles in plant cells.

  9. Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

    Science.gov (United States)

    Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

    2016-05-01

    RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

  10. Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen

    Directory of Open Access Journals (Sweden)

    Canard Bruno

    2011-10-01

    Full Text Available Abstract Background The genus Flavivirus encompasses more than 50 distinct species of arthropod-borne viruses, including several major human pathogens, such as West Nile virus, yellow fever virus, Japanese encephalitis virus and the four serotypes of dengue viruses (DENV type 1-4. Each year, flaviviruses cause more than 100 million infections worldwide, some of which lead to life-threatening conditions such as encephalitis or haemorrhagic fever. Among the viral proteins, NS3 and NS5 proteins constitute the major enzymatic components of the viral replication complex and are essential to the flavivirus life cycle. Results We report here the results of a high-throughput yeast two-hybrid screen to identify the interactions between human host proteins and the flavivirus NS3 and NS5 proteins. Using our screen results and literature curation, we performed a global analysis of the NS3 and NS5 cellular targets based on functional annotation with the Gene Ontology features. We finally created the first flavivirus NS3 and NS5 proteins interaction network and analysed the topological features of this network. Our proteome mapping screen identified 108 human proteins interacting with NS3 or NS5 proteins or both. The global analysis of the cellular targets revealed the enrichment of host proteins involved in RNA binding, transcription regulation, vesicular transport or innate immune response regulation. Conclusions We proposed that the selective disruption of these newly identified host/virus interactions could represent a novel and attractive therapeutic strategy in treating flavivirus infections. Our virus-host interaction map provides a basis to unravel fundamental processes about flavivirus subversion of the host replication machinery and/or immune defence strategy.

  11. The RAF family:an expanding network of post—traslational controls and protein—protein interactions

    Institute of Scientific and Technical Information of China (English)

    YURYEVANTON; LAWRENCEPWENNOGLE

    1998-01-01

    Protein kinase RAF is strategically located in the "Ras-MAP-kinase signal transduction pathway",a principle system which transmits signals from growth factor receptors to the nucleus,resulting in cell proliferation.Growth factor responses are mediated in part by activation of Ras,which in turn activates RAF to phosphorylate MEK,its downstream substrate.MEK activates MAPkinase to in fluence nuclear events.it is clear.however,that a network of signals other than those carred by Ras plays a role in RAF regulation.These orthogonal influences are mediated bu:serine/threonine kinases,tyrosine kinases,and protein-protein interactions.As a further complication to the RAF network,three isoforms of RAF have been established which have divergent N-terminal regulatory domains,Whereas these divergent regulatory domains implicate isoform-specific functions,no clear evidence or hypothesis for distinct functions for individual isoforms has been presented.Recently,"isoform-specific protein interactions"have been identified among numerous proteins interacting with RAF,These studies may serve to delineate independent functions for RAF isoforms.

  12. A yeast-based genomic strategy highlights the cell protein networks altered by FTase inhibitor peptidomimetics

    Directory of Open Access Journals (Sweden)

    Porcu Giampiero

    2010-07-01

    Full Text Available Abstract Background Farnesyltransferase inhibitors (FTIs are anticancer agents developed to inhibit Ras oncoprotein activities. FTIs of different chemical structure act via a conserved mechanism in eukaryotic cells. They have low toxicity and are active on a wide range of tumors in cellular and animal models, independently of the Ras activation state. Their ultimate mechanism of action, however, remains undetermined. FTase has hundred of substrates in human cells, many of which play a pivotal role in either tumorigenesis or in pro-survival pathways. This lack of knowledge probably accounts for the failure of FTIs at clinical stage III for most of the malignancies treated, with the notable exception of haematological malignancies. Understanding which cellular pathways are the ultimate targets of FTIs in different tumor types and the basis of FTI resistance is required to improve the efficacy of FTIs in cancer treatment. Results Here we used a yeast-based cellular assay to define the transcriptional changes consequent to FTI peptidomimetic administration in conditions that do not substantially change Ras membrane/cytosol distribution. Yeast and cancer cell lines were used to validate the results of the network analysis. The transcriptome of yeast cells treated with FTase inhibitor I was compared with that of untreated cells and with an isogenic strain genetically inhibited for FTase activity (Δram1. Cells treated with GGTI-298 were analyzed in a parallel study to validate the specificity of the FTI response. Network analysis, based on gene ontology criteria, identified a cell cycle gene cluster up-regulated by FTI treatment that has the Aurora A kinase IPL1 and the checkpoint protein MAD2 as hubs. Moreover, TORC1-S6K-downstream effectors were found to be down-regulated in yeast and mammalian FTI-treated cells. Notably only FTIs, but not genetic inhibition of FTase, elicited up-regulation of ABC/transporters. Conclusions This work provides a view

  13. The structure of the archaebacterial ribosomal protein S7 and its possible interaction with 16S rRNA.

    Science.gov (United States)

    Hosaka, H; Yao, M; Kimura, M; Tanaka, I

    2001-11-01

    Ribosomal protein S7 is one of the ubiquitous components of the small subunit of the ribosome. It is a 16S rRNA-binding protein positioned close to the exit of the tRNA, and it plays a role in initiating assembly of the head of the 30S subunit. Previous structural analyses of eubacterial S7 have shown that it has a stable alpha-helix core and a flexible beta-arm. Unlike these eubacterial proteins, archaebacterial or eukaryotic S7 has an N-terminal extension of approximately 60 residues. The crystal structure of S7 from archaebacterium Pyrococcus horikoshii (PhoS7) has been determined at 2.1 A resolution. The final model of PhoS7 consists of six major alpha-helices, a short 3(10)-helix and two beta-stands. The major part (residues 18-45) of the N-terminal extension of PhoS7 reinforces the alpha-helical core by well-extended hydrophobic interactions, while the other part (residues 46-63) is not visible in the crystal and is possibly fixed only by interacting with 16S rRNA. These differences in the N-terminal extension as well as in the insertion (between alpha1 and alpha2) of the archaebacterial S7 structure from eubacterial S7 are such that they do not necessitate a major change in the structure of the currently available eubacterial 16S rRNA. Some of the inserted chains might pass through gaps formed by helices of the 16S rRNA.

  14. Amplification and detection of single-molecule conformational fluctuation through a protein interaction network with bimodal distributions.

    Science.gov (United States)

    Wu, Zhanghan; Elgart, Vlad; Qian, Hong; Xing, Jianhua

    2009-09-10

    A protein undergoes conformational dynamics with multiple time scales, which results in fluctuating enzyme activities. Recent studies in single-molecule enzymology have observe this "age-old" dynamic disorder phenomenon directly. However, the single-molecule technique has its limitation. To be able to observe this molecular effect with real biochemical functions in situ, we propose to couple the fluctuations in enzymatic activity to noise propagations in small protein interaction networks such as a zeroth-order ultrasensitive phosphorylation-dephosphorylation cycle. We show that enzyme fluctuations can indeed be amplified by orders of magnitude into fluctuations in the level of substrate phosphorylation, a quantity of wide interest in cellular biology. Enzyme conformational fluctuations sufficiently slower than the catalytic reaction turnover rate result in a bimodal concentration distribution of the phosphorylated substrate. In return, this network-amplified single-enzyme fluctuation can be used as a novel biochemical "reporter" for measuring single-enzyme conformational fluctuation rates.

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

    Science.gov (United States)

    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

  16. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; de Fries, Louise Skovlund

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social...

  17. Azimuthally invariant Mueller-matrix mapping of biological tissue in differential diagnosis of mechanisms protein molecules networks an sotropy

    Science.gov (United States)

    Ushenko, V. A.; Gavrylyak, M. S.

    2013-09-01

    The optical model of polycrystalline networks of blood plasma proteins is suggested. The results of investigating the interrelation between the values of correlation (correlation area, asymmetry coefficient and autocorrelation function excess) and fractal (dispersion of logarithmic dependencies of power spectra) parameters are presented. They characterize the coordinate distributions of Mueller-matrixes elements of blood plasma smears and pathological state of the organism. The diagnostic criteria of breast cancer nascency are determined.

  18. Coarse-Grained Free Energy Functions for Studying Protein Conformational Changes: A Double-Well Network Model

    OpenAIRE

    Chu, Jhih-Wei; Voth, Gregory A.

    2007-01-01

    In this work, a double-well network model (DWNM) is presented for generating a coarse-grained free energy function that can be used to study the transition between reference conformational states of a protein molecule. Compared to earlier work that uses a single, multidimensional double-well potential to connect two conformational states, the DWNM uses a set of interconnected double-well potentials for this purpose. The DWNM free energy function has multiple intermediate states and saddle poi...

  19. A review on protein-protein interaction network of APE1/Ref-1 and its associated biological functions.

    Science.gov (United States)

    Thakur, S; Dhiman, M; Tell, G; Mantha, A K

    2015-04-01

    Apurinic/apyrimidinic endonuclease 1 (APE1) is a classic example of functionally variable protein. Besides its well-known role in (i) DNA repair of oxidative base damage, APE1 also plays a critical role in (ii) redox regulation of transcription factors controlling gene expression for cell survival pathways, for which it is also known as redox effector factor 1 (Ref-1), and recent evidences advocates for (iii) coordinated control of other non-canonical protein-protein interaction(s) responsible for significant biological functions in mammalian cells. The diverse functions of APE1 can be ascribed to its ability to interact with different protein partners, owing to the attainment of unfolded domains during evolution. Association of dysregulation of APE1 with various human pathologies, such as cancer, cardiovascular diseases and neurodegeneration, is attributable to its multifunctional nature, and this makes APE1 a potential therapeutic target. This review covers the important aspects of APE1 in terms of its significant protein-protein interaction(s), and this knowledge is required to understand the onset and development of human pathologies and to design or improve the strategies to target such interactions for treatment and management of various human diseases.

  20. Gliadin functionality in the gluten network: Role of omega-gliadin proteins

    Science.gov (United States)

    Gluten forming proteins found in the wheat endosperm consist of gliadin and glutenin proteins. These proteins are responsible for the viscoelastic properties found in wheat dough. Gliadins are further divided into a-/ß-, '- and '- fractions. These proteins are monomeric in structure whereas, gluteni...

  1. Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

    OpenAIRE

    Pufeng Du; Lusheng Wang

    2014-01-01

    One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as w...

  2. Cooperation of the ER-shaping proteins atlastin, lunapark, and reticulons to generate a tubular membrane network.

    Science.gov (United States)

    Wang, Songyu; Tukachinsky, Hanna; Romano, Fabian B; Rapoport, Tom A

    2016-01-01

    In higher eukaryotes, the endoplasmic reticulum (ER) contains a network of membrane tubules, which transitions into sheets during mitosis. Network formation involves curvature-stabilizing proteins, including the reticulons (Rtns), as well as the membrane-fusing GTPase atlastin (ATL) and the lunapark protein (Lnp). Here, we have analyzed how these proteins cooperate. ATL is needed to not only form, but also maintain, the ER network. Maintenance requires a balance between ATL and Rtn, as too little ATL activity or too high Rtn4a concentrations cause ER fragmentation. Lnp only affects the abundance of three-way junctions and tubules. We suggest a model in which ATL-mediated fusion counteracts the instability of free tubule ends. ATL tethers and fuses tubules stabilized by the Rtns, and transiently sits in newly formed three-way junctions. Lnp subsequently moves into the junctional sheets and forms oligomers. Lnp is inactivated by mitotic phosphorylation, which contributes to the tubule-to-sheet conversion of the ER. PMID:27619977

  3. The protein network surrounding the human telomere repeat binding factors TRF1, TRF2, and POT1.

    Directory of Open Access Journals (Sweden)

    Richard J Giannone

    Full Text Available Telomere integrity (including telomere length and capping is critical in overall genomic stability. Telomere repeat binding factors and their associated proteins play vital roles in telomere length regulation and end protection. In this study, we explore the protein network surrounding telomere repeat binding factors, TRF1, TRF2, and POT1 using dual-tag affinity purification in combination with multidimensional protein identification technology liquid chromatography--tandem mass spectrometry (MudPIT LC-MS/MS. After control subtraction and data filtering, we found that TRF2 and POT1 co-purified all six members of the telomere protein complex, while TRF1 identified five of six components at frequencies that lend evidence towards the currently accepted telomere architecture. Many of the known TRF1 or TRF2 interacting proteins were also identified. Moreover, putative associating partners identified for each of the three core components fell into functional categories such as DNA damage repair, ubiquitination, chromosome cohesion, chromatin modification/remodeling, DNA replication, cell cycle and transcription regulation, nucleotide metabolism, RNA processing, and nuclear transport. These putative protein-protein associations may participate in different biological processes at telomeres or, intriguingly, outside telomeres.

  4. The protein network surrounding the human telomere repeat binding factors TRF1, TRF2, and POT1

    Energy Technology Data Exchange (ETDEWEB)

    Giannone, Richard J [ORNL; McDonald, W Hayes [ORNL; Hurst, Gregory {Greg} B [ORNL; Shen, Rong-Fong [National Institute on Aging, National Institutes of Health; Wang, Yisong [ORNL; Liu, Yie [National Institute on Aging, Baltimore

    2010-01-01

    Telomere integrity (including telomere length and capping) is critical in overall genomic stability. Telomere repeat binding factors and their associated proteins play vital roles in telomere length regulation and end protection. In this study, we explore the protein network surrounding telomere repeat binding factors, TRF1, TRF2, and POT1 using dual-tag affinity purification in combination with multidimensional protein identification technology liquid chromatography - tandem mass spectrometry (MudPIT LC-MS/MS). After control subtraction and data filtering, we found that TRF2 and POT1 co-purified all six members of the telomere protein complex, while TRF1 identified five of six components at frequencies that lend evidence towards the currently accepted telomere architecture. Many of the known TRF1 or TRF2 interacting proteins were also identified. Moreover, putative associating partners identified for each of the three core components fell into functional categories such as DNA damage repair, ubiquitination, chromosome cohesion, chromatin modification/remodeling, DNA replication, cell cycle and transcription regulation, nucleotide metabolism, RNA processing, and nuclear transport. These putative protein-protein associations may participate in different biological processes at telomeres or, intriguingly, outside telomeres.

  5. Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network

    Directory of Open Access Journals (Sweden)

    Xiaodong Mao

    2014-06-01

    Full Text Available In this study, near-infrared reflectance spectroscopy and radial basis function (RBF neural network algorithm were used to measure the protein content of wheat owing to their nondestructiveness and quick speed as well as better performance compared to the traditional measuring method (semimicro-Kjeldahl in actual practice. To simplify the complex structure of the RBF network caused by the excessive wave points of samples obtained by near-infrared reflectance spectroscopy, we proposed the particle swarm optimization (PSO algorithm to optimize the cluster center in the hidden layers of the RBF neural network. In addition, a series of improvements for the PSO algorithm was also made to deal with its drawbacks in premature convergence and mechanical inertia weight setting. The experimental analysis demonstrated that the improved PSO algorithm greatly reduced the complexity of the network structure and improved the training speed of the RBF network. Meanwhile, the research result also proved the high performance of the model with its root-mean-square error of prediction (RMSEP and prediction correlation coefficient (R at 0.26576 and 0.975, respectively, thereby fulfilling the modern agricultural testing requirements featuring nondestructiveness, real-timing, and abundance in the number of samples.

  6. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  7. Networks of Polarized Actin Filaments in the Axon Initial Segment Provide a Mechanism for Sorting Axonal and Dendritic Proteins

    Directory of Open Access Journals (Sweden)

    Kaori Watanabe

    2012-12-01

    Full Text Available Trafficking of proteins specifically to the axonal or somatodendritic membrane allows neurons to establish and maintain polarized compartments with distinct morphology and function. Diverse evidence suggests that an actin-dependent vesicle filter within the axon initial segment (AIS plays a critical role in polarized trafficking; however, no distinctive actin-based structures capable of comprising such a filter have been found within the AIS. Here, using correlative light and scanning electron microscopy, we visualized networks of actin filaments several microns wide within the AIS of cortical neurons in culture. Individual filaments within these patches are predominantly oriented with their plus ends facing toward the cell body, consistent with models of filter selectivity. Vesicles carrying dendritic proteins are much more likely to stop in regions occupied by the actin patches than in other regions, indicating that the patches likely prevent movement of dendritic proteins to the axon and thereby act as a vesicle filter.

  8. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis.

    Science.gov (United States)

    Gan, Yanxiong; Zheng, Shichao; Baak, Jan P A; Zhao, Silei; Zheng, Yongfeng; Luo, Nini; Liao, Wan; Fu, Chaomei

    2015-11-01

    Curcumin, the medically active component from Curcuma longa (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein-protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. PMID:26713275

  9. Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat

    DEFF Research Database (Denmark)

    Babbitt, Patricia C.; Bagos, Pantelis G.; Bairoch, Amos;

    2015-01-01

    protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication...

  10. Aggregation and network formation in self-assembly of protein (H3.1) by a coarse-grained Monte Carlo simulation

    Science.gov (United States)

    Pandey, R. B.; Farmer, B. L.

    2014-11-01

    Multi-scale aggregation to network formation of interacting proteins (H3.1) are examined by a knowledge-based coarse-grained Monte Carlo simulation as a function of temperature and the number of protein chains, i.e., the concentration of the protein. Self-assembly of corresponding homo-polymers of constitutive residues (Cys, Thr, and Glu) with extreme residue-residue interactions, i.e., attractive (Cys-Cys), neutral (Thr-Thr), and repulsive (Glu-Glu), are also studied for comparison with the native protein. Visual inspections show contrast and similarity in morphological evolutions of protein assembly, aggregation of small aggregates to a ramified network from low to high temperature with the aggregation of a Cys-polymer, and an entangled network of Glu and Thr polymers. Variations in mobility profiles of residues with the concentration of the protein suggest that the segmental characteristic of proteins is altered considerably by the self-assembly from that in its isolated state. The global motion of proteins and Cys polymer chains is enhanced by their interacting network at the low temperature where isolated chains remain quasi-static. Transition from globular to random coil transition, evidenced by the sharp variation in the radius of gyration, of an isolated protein is smeared due to self-assembly of interacting networks of many proteins. Scaling of the structure factor S(q) with the wave vector q provides estimates of effective dimension D of the mass distribution at multiple length scales in self-assembly. Crossover from solid aggregates (D ˜ 3) at low temperature to a ramified fibrous network (D ˜ 2) at high temperature is observed for the protein H3.1 and Cys polymers in contrast to little changes in mass distribution (D ˜ 1.6) of fibrous Glu- and Thr-chain configurations.

  11. A P4-ATPase