WorldWideScience

Sample records for alpha-helical protein networks

  1. Alpha-helical protein networks are self-protective and flaw-tolerant.

    Science.gov (United States)

    Ackbarow, Theodor; Sen, Dipanjan; Thaulow, Christian; Buehler, Markus J

    2009-01-01

    Alpha-helix based protein networks as they appear in intermediate filaments in the cell's cytoskeleton and the nuclear membrane robustly withstand large deformation of up to several hundred percent strain, despite the presence of structural imperfections or flaws. This performance is not achieved by most synthetic materials, which typically fail at much smaller deformation and show a great sensitivity to the existence of structural flaws. Here we report a series of molecular dynamics simulations with a simple coarse-grained multi-scale model of alpha-helical protein domains, explaining the structural and mechanistic basis for this observed behavior. We find that the characteristic properties of alpha-helix based protein networks are due to the particular nanomechanical properties of their protein constituents, enabling the formation of large dissipative yield regions around structural flaws, effectively protecting the protein network against catastrophic failure. We show that the key for these self protecting properties is a geometric transformation of the crack shape that significantly reduces the stress concentration at corners. Specifically, our analysis demonstrates that the failure strain of alpha-helix based protein networks is insensitive to the presence of structural flaws in the protein network, only marginally affecting their overall strength. Our findings may help to explain the ability of cells to undergo large deformation without catastrophic failure while providing significant mechanical resistance. PMID:19547709

  2. 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.

  3. Dynamic allostery of protein alpha helical coiled-coils

    OpenAIRE

    Hawkins, R. J.; Mcleish, T.C.B.

    2005-01-01

    Alpha helical coiled-coils appear in many important allosteric proteins such as the dynein molecular motor and bacteria chemotaxis transmembrane receptors. As a mechanism for transmitting the information of ligand binding to a distant site across an allosteric protein, an alternative to conformational change in the mean static structure is an induced change in the pattern of the internal dynamics of the protein. We explore how ligand binding may change the intramolecular vibrational free ener...

  4. Flexibility of alpha-helices: results of a statistical analysis of database protein structures.

    Science.gov (United States)

    Emberly, Eldon G; Mukhopadhyay, Ranjan; Wingreen, Ned S; Tang, Chao

    2003-03-14

    Alpha-helices stand out as common and relatively invariant secondary structural elements of proteins. However, alpha-helices are not rigid bodies and their deformations can be significant in protein function (e.g. coiled coils). To quantify the flexibility of alpha-helices we have performed a structural principal-component analysis of helices of different lengths from a representative set of protein folds in the Protein Data Bank. We find three dominant modes of flexibility: two degenerate bend modes and one twist mode. The data are consistent with independent Gaussian distributions for each mode. The mode eigenvalues, which measure flexibility, follow simple scaling forms as a function of helix length. The dominant bend and twist modes and their harmonics are reproduced by a simple spring model, which incorporates hydrogen-bonding and excluded volume. As an application, we examine the amount of bend and twist in helices making up all coiled-coil proteins in SCOP. Incorporation of alpha-helix flexibility into structure refinement and design is discussed. PMID:12614621

  5. A Novel Method for Sampling Alpha-Helical Protein Backbones

    Science.gov (United States)

    Fain, Boris; Levitt, Michael

    2001-01-01

    We present a novel technique of sampling the configurations of helical proteins. Assuming knowledge of native secondary structure, we employ assembly rules gathered from a database of existing structures to enumerate the geometrically possible 3-D arrangements of the constituent helices. We produce a library of possible folds for 25 helical protein cores. In each case the method finds significant numbers of conformations close to the native structure. In addition we assign coordinates to all atoms for 4 of the 25 proteins. In the context of database driven exhaustive enumeration our method performs extremely well, yielding significant percentages of structures (0.02%--82%) within 6A of the native structure. The method's speed and efficiency make it a valuable contribution towards the goal of predicting protein structure.

  6. Markov analysis of alpha-helical, beta-sheet and random coil regions of proteins

    International Nuclear Information System (INIS)

    The rules up to now used to predict the spatial configuration of proteins from their primary structure are mostly based on the recurrence analysis of some doublets, triplets and so on of contiguous amino acids, but they do not take into account the correlation characteristics of the whole amino acid sequence. A statistical analysis of amino acid sequences for the alpha-helical, beta-sheet and random coil regions of about twenty proteins with known secondary structure by considering correlations effects has been carried out. The obtained results demonstrate that these sequences are at least a second-order Markov chain, i.e. they appear as if they were generated by a source that remembers at least the two aminoacids before the one being generated and that these two previous symbols influence the present choice

  7. Fast and forceful refolding of stretched alpha-helical solenoid proteins.

    Science.gov (United States)

    Kim, Minkyu; Abdi, Khadar; Lee, Gwangrog; Rabbi, Mahir; Lee, Whasil; Yang, Ming; Schofield, Christopher J; Bennett, Vann; Marszalek, Piotr E

    2010-06-16

    Anfinsen's thermodynamic hypothesis implies that proteins can encode for stretching through reversible loss of structure. However, large in vitro extensions of proteins that occur through a progressive unfolding of their domains typically dissipate a significant amount of energy, and therefore are not thermodynamically reversible. Some coiled-coil proteins have been found to stretch nearly reversibly, although their extension is typically limited to 2.5 times their folded length. Here, we report investigations on the mechanical properties of individual molecules of ankyrin-R, beta-catenin, and clathrin, which are representative examples of over 800 predicted human proteins composed of tightly packed alpha-helical repeats (termed ANK, ARM, or HEAT repeats, respectively) that form spiral-shaped protein domains. Using atomic force spectroscopy, we find that these polypeptides possess unprecedented stretch ratios on the order of 10-15, exceeding that of other proteins studied so far, and their extension and relaxation occurs with minimal energy dissipation. Their sequence-encoded elasticity is governed by stepwise unfolding of small repeats, which upon relaxation of the stretching force rapidly and forcefully refold, minimizing the hysteresis between the stretching and relaxing parts of the cycle. Thus, we identify a new class of proteins that behave as highly reversible nanosprings that have the potential to function as mechanosensors in cells and as building blocks in springy nanostructures. Our physical view of the protein component of cells as being comprised of predominantly inextensible structural elements under tension may need revision to incorporate springs. PMID:20550922

  8. An experimental approach to testing modular evolution: directed replacement of alpha-helices in a bacterial protein.

    OpenAIRE

    DuBose, R. F.; Hartl, D. L.

    1989-01-01

    We have used oligonucleotide site-directed mutagenesis to ask whether certain structural motifs in proteins are determined mainly by local interactions among amino acids. Multiple consecutive amino acids in three alpha-helices in the alkaline phosphatase (EC 3.1.3.1) of Escherichia coli have been replaced with helical sequences from four other sources. Altogether, 12 distinct helical replacements were created, 9 of which retain enzymatic activity. Most short stretches of helical sequence can ...

  9. 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

  10. 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

  11. Temperature-dependent structural changes in intrinsically disordered proteins: formation of alpha-helices or loss of polyproline II?

    DEFF Research Database (Denmark)

    Kjærgaard, Magnus; Nørholm, Ann-Beth; Hendus-Altenburger, Ruth; Pedersen, Stine Helene Falsig; Poulsen, Flemming M; Kragelund, Birthe B

    2010-01-01

    temperature, which most likely reflects formation of transient alpha-helices or loss of polyproline II (PPII) content. Using three IDPs, ACTR, NHE1, and Spd1, we show that the temperature-induced structural change is common among IDPs and is accompanied by a contraction of the conformational ensemble. This...

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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

    OpenAIRE

    Sandeep Chakraborty; Basuthkar J. Rao; 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 ...

  18. 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

  19. 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

    to a long alpha-helix. Tetranectin has been classified in a distinct group of the C-type lectin superfamily but has structural similarity to the proteins in the group of collectins. Tetranectin has three intramolecular disulfide bridges. Two of these are conserved in the C-type lectin superfamily......, whereas 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...

  20. 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.

  1. 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

  2. Bilinear forms and soliton solutions for a fourth-order variable-coefficient nonlinear Schrödinger equation in an inhomogeneous Heisenberg ferromagnetic spin chain or an alpha helical protein

    Science.gov (United States)

    Yang, Jin-Wei; Gao, Yi-Tian; Wang, Qi-Min; Su, Chuan-Qi; Feng, Yu-Jie; Yu, Xin

    2016-01-01

    In this paper, a fourth-order variable-coefficient nonlinear Schrödinger equation is studied, which might describe a one-dimensional continuum anisotropic Heisenberg ferromagnetic spin chain with the octuple-dipole interaction or an alpha helical protein with higher-order excitations and interactions under continuum approximation. With the aid of auxiliary function, we derive the bilinear forms and corresponding constraints on the variable coefficients. Via the symbolic computation, we obtain the Lax pair, infinitely many conservation laws, one-, two- and three-soliton solutions. We discuss the influence of the variable coefficients on the solitons. With different choices of the variable coefficients, we obtain the parabolic, cubic, and periodic solitons, respectively. We analyse the head-on and overtaking interactions between/among the two and three solitons. Interactions between a bound state and a single soliton are displayed with different choices of variable coefficients. We also derive the quasi-periodic formulae for the three cases of the bound states.

  3. 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

  4. 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

  5. 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...

  6. Thermodynamic characterization of an artificially designed amphiphilic alpha-helical peptide containing periodic prolines: observations of high thermal stability and cold denaturation.

    OpenAIRE

    Kitakuni, E.; Kuroda, Y; Oobatake, M.; Tanaka, T; Nakamura, H

    1994-01-01

    To investigate the structural stability of proteins, we analyzed the thermodynamics of an artificially designed 30-residue peptide. The designed peptide, NH2-EELLPLAEALAPLLEALLPLAEALAPLLKK-COOH (PERI COIL-1), with prolines at i + 7 positions, forms a pentameric alpha-helical structure in aqueous solution. The thermal denaturation curves of the CD at 222 nm (pH 7.5) show an unusual cold denaturation occurring well above 0 degrees C and no thermal denaturation is observable under 90 degrees C. ...

  7. 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...

  8. 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

  9. Evolutionary bidirectional expansion for the tracing of alpha helices in cryo-electron microscopy reconstructions.

    Science.gov (United States)

    Rusu, Mirabela; Wriggers, Willy

    2012-02-01

    Cryo-electron microscopy (cryo-EM) enables the imaging of macromolecular complexes in near-native environments at resolutions that often permit the visualization of secondary structure elements. For example, alpha helices frequently show consistent patterns in volumetric maps, exhibiting rod-like structures of high density. Here, we introduce VolTrac (Volume Tracer) - a novel technique for the annotation of alpha-helical density in cryo-EM data sets. VolTrac combines a genetic algorithm and a bidirectional expansion with a tabu search strategy to trace helical regions. Our method takes advantage of the stochastic search by using a genetic algorithm to identify optimal placements for a short cylindrical template, avoiding exploration of already characterized tabu regions. These placements are then utilized as starting positions for the adaptive bidirectional expansion that characterizes the curvature and length of the helical region. The method reliably predicted helices with seven or more residues in experimental and simulated maps at intermediate (4-10Å) resolution. The observed success rates, ranging from 70.6% to 100%, depended on the map resolution and validation parameters. For successful predictions, the helical axes were located within 2Å from known helical axes of atomic structures. PMID:22155667

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

    OpenAIRE

    Meier Iris; Stahlberg Eric A; Schraegle Shannon J; Rose Annkatrin

    2005-01-01

    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 previousl...

  11. 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

  12. 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

  13. 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...

  14. Chain length dependence of the helix orientation in Langmuir-Blodgett monolayers of alpha-helical diblock copolypeptides

    OpenAIRE

    Nguyen, Le-Thu T; Ardana, Aditya; Vorenkamp, Eltjo J.; ten Brinke, Gerrit; Schouten, Arend J.

    2010-01-01

    The effect of chain length on the helix orientation of alpha-helical diblock copolypeptides in Langmuir and Langmuir-Blodgett monolayers is reported for the first time. Amphiphilic diblock copolypeptides (PLGA-b-PMLGSLGs) of poly(alpha-L-glutamic acid) (PLGA) and poly(gamma-methyl-L-glutamate-ran-gamma-stearyl-L-glutamate) with 30 mol% of stearyl substituents (PMLGSLG) of various block lengths were studied. The tilt angle between the helices and the substrate-normal decreases upon increasing ...

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

    Science.gov (United States)

    Palidwor, Gareth A; Shcherbinin, Sergey; Huska, Matthew R; Rasko, Tamas; Stelzl, Ulrich; Arumughan, Anup; Foulle, Raphaele; Porras, Pablo; Sanchez-Pulido, Luis; Wanker, Erich E; Andrade-Navarro, Miguel A

    2009-03-01

    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 (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. PMID:19282972

  16. 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.

  17. Long range correlations and folding angle in polymers with applications to {\\alpha}-helical proteins

    CERN Document Server

    Krokhotin, Andrey; Niemi, Antti J

    2013-01-01

    The conformational complexity of linear polymers far exceeds that of point-like atoms and molecules. Polymers can bend, twist, even become knotted. Thus they may also display a much richer phase structure than point particles. But it is not very easy to characterize the phase of a polymer. Essentially, the only attribute is the radius of gyration. The way how it changes when the degree of polymerization becomes different, and how it evolves when the ambient temperature and solvent properties change, discloses the phase of the polymer. Moreover, in any finite length chain there are corrections to scaling, that complicate the detailed analysis of the phase structure. Here we introduce a quantity that we call the folding angle, a novel tool to identify and scrutinize the phases of polymers. We argue for a mean-field relationship between its values and those of the scaling exponent in the radius of gyration. But unlike in the case of the radius of gyration, the value of the folding angle can be evaluated from a s...

  18. Structural and functional analysis of the NF-kappa B p65 C terminus. An acidic and modular transactivation domain with the potential to adopt an alpha-helical conformation.

    Science.gov (United States)

    Schmitz, M L; dos Santos Silva, M A; Altmann, H; Czisch, M; Holak, T A; Baeuerle, P A

    1994-10-14

    The p65 subunit of the NF-kappa B transcription factor contains in its C-terminal 120 amino acids at least two transcription activation domains. One domain (TA1) is contained within only the 30 C-terminal amino acids. Structural studies employing CD and NMR spectroscopy revealed that the TA1 domain is unstructured. NMR analysis of a protein corresponding to the C-terminal 123 amino acids also showed a random coil conformation. However, a portion of TA1 was found to adopt an alpha-helical conformation in the presence of hydrophobic solvents. Transcriptional analysis of point mutants revealed the functional importance of two evolutionary conserved sequence repeats, which are located in the conditionally alpha-helical region of TA1. These repeats acted synergistically in transcription activation. The inhibitory effect of some mutants indicated secondary structure constraints on TA1 in intact cells. Inverting the sequence of two acidic activation domains significantly reduced their transactivating potential, suggesting that amino acid composition is not solely essential for activity; a defined primary structure is necessary as well. Acidic sequence motifs related in primary structure and squelching activity to those of TA1 are present in the activation domains of VP16, c-Rel, and several other transcription factors. We propose a model suggesting that primarily unstructured acidic activation domains can adopt a secondary structure upon contacting their target molecules by an "induced fit" mechanism. PMID:7929265

  19. 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 ...

  20. 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...

  1. 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...

  2. 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

  3. 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

  4. Charged single alpha-helices in proteomes revealed by a consensus prediction approach.

    Science.gov (United States)

    Gáspári, Zoltán; Süveges, Dániel; Perczel, András; Nyitray, László; Tóth, Gábor

    2012-04-01

    Charged single α-helices (CSAHs) constitute a recently recognized protein structural motif. Its presence and role is characterized in only a few proteins. To explore its general features, a comprehensive study is necessary. We have set up a consensus prediction method available as a web service (at http://csahserver.chem.elte.hu) and downloadable scripts capable of predicting CSAHs from protein sequences. Using our method, we have performed a comprehensive search on the UniProt database. We found that the motif is very rare but seems abundant in proteins involved in symbiosis and RNA binding/processing. Although there are related proteins with CSAH segments, the motif shows no deep conservation in protein families. We conclude that CSAH-containing proteins, although rare, are involved in many key biological processes. Their conservation pattern and prevalence in symbiosis-associated proteins suggest that they might be subjects of relatively rapid molecular evolution and thus can contribute to the emergence of novel functions. PMID:22310480

  5. 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...

  6. 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...

  7. 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

  8. 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.

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

    International Nuclear Information System (INIS)

    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

  10. 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...

  11. 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...

  12. 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...

  13. 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 ...

  14. 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.

  15. 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

  16. The role of the central L- or D-Pro residue on structure and mode of action of a cell-selective alpha-helical IsCT-derived antimicrobial peptide.

    Science.gov (United States)

    Lim, Shin Saeng; Kim, Yangmee; Park, Yoonkyung; Kim, Jae Il; Park, Il-Seon; Hahm, Kyung-Soo; Shin, Song Yub

    2005-09-01

    IsCT-P (ILKKIWKPIKKLF-NH2) is a novel alpha-helical antimicrobial peptide with bacterial cell selectivity designed from a scorpion-derived peptide IsCT. To investigate the role of L- or D-Pro kink on the structure and the mode of action of a short alpha-helical antimicrobial peptide with bacterial cell selectivity, we synthesized IsCT-p, in which D-Pro is substituted for L-Pro8 of IsCT-P. CD spectra revealed that IsCT-P adopted a typical alpha-helical structure in various membrane-mimicking conditions, whereas IsCT-p showed a random structure. This result indicated that D-Pro in the central position of a short alpha-helical peptide provides more remarkable structural flexibility than L-Pro. Despite its higher antibacterial activity, IsCT-p was much less effective at inducing dye leakage in the negatively charged liposome mimicking bacterial membrane and induced no or little membrane potential depolarization of Staphylococcus aureus. Confocal laser scanning microscopy showed that IsCT-p penetrated the bacterial cell membrane and accumulated in the cytoplasm, whereas IsCT-P remained outside or on the cell membrane. These results suggested that the major target of IsCT-P and IsCT-p is the bacterial membranes and intracellular components, respectively. Collectively, our results demonstrated that the central D-Pro kink in alpha-helical antimicrobial peptides plays an important role in penetrating bacterial membrane as well as bacterial cell selectivity. PMID:16040002

  17. Systematic computational prediction of protein interaction networks

    International Nuclear Information System (INIS)

    Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use

  18. Systematic computational prediction of protein interaction networks.

    Science.gov (United States)

    Lees, J G; Heriche, J K; Morilla, I; Ranea, J A; Orengo, C A

    2011-06-01

    Determining the network of physical protein associations is an important first step in developing mechanistic evidence for elucidating biological pathways. Despite rapid advances in the field of high throughput experiments to determine protein interactions, the majority of associations remain unknown. Here we describe computational methods for significantly expanding protein association networks. We describe methods for integrating multiple independent sources of evidence to obtain higher quality predictions and we compare the major publicly available resources available for experimentalists to use. PMID:21572181

  19. 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.

  20. 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

  1. 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...

  2. X-ray diffraction analysis of scrapie prion: intermediate and folded structures in a peptide containing two putative alpha-helices.

    Science.gov (United States)

    Inouye, H; Kirschner, D A

    1997-05-01

    Small proteinaceous infectious particles called prions cause certain neurodegenerative diseases in human and animals. Limited proteolysis of infectious scrapie prions PrP(Sc) yields an N-truncated polypeptide termed PrP 27-30, which encompasses residues 90 to 231 of PrP(Sc) and which assembles into 100 to 200 A wide amyloid rods. It has been hypothesized that the infectious prion is converted from its non-infectious cellular form (PrP(C)) by means of an alpha-helical to beta-sheet conformational change. Secondary structure analysis, computer modeling, and structural biophysics methods support this hypothesis. Residues 90 to 145 of PrP, which contain two putative alpha-helical domains H1 and H2, may be of particular relevance to the disease pathogenesis, as C-terminal truncation at residue 145 was found in a patient with an inherited prion disease. Moreover, our recent X-ray diffraction analysis suggests that the peptide consisting of these residues (designated SHa 90-145) closely models the amyloidogenic beta-sheet core of PrP. In the current study, we have analyzed in detail the X-ray diffraction patterns of SHa 90-145. Two samples were examined: one that was dehydrated under ambient conditions whilst in an external magnetic field (to induce fibril orientation), and another that was sealed after partial drying. The dried, magnetically oriented sample showed a cross-beta diffraction pattern in which the fiber axis (rotation axis) was parallel to the H-bonding direction of the beta-sheets. The major wide-angle peaks indicate the presence of approximately 40 A wide beta-crystallites, which constitute the protofilament. Each crystallite is composed of several orthogonal unit cells, normal to the fiber (a-axis) direction, having lattice constants a = 9.69 A, b = 6.54 A, and c = 18.06 A. Electron density maps were calculated by iterative Fourier synthesis using beta-silk as an initial phase model. The distribution of density indicated that there were two types of beta

  3. 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...

  4. 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

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

    Science.gov (United States)

    Burkhard, P; Meier, M; Lustig, A

    2000-12-01

    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-helical and 100% dimeric tinder physiological buffer conditions. Interestingly, the peptide was shown to switch its oligomerization state from a dimer to a trimer upon increasing ionic strength. The correctness of the rational design principles used here is supported by details of the atomic structure of the peptide deduced from X-ray crystallography. The structure of the peptide shows that it is not a molten globule but assumes a unique, native-like conformation. This de novo peptide thus represents an attractive model system for the design of a molecular recognition system. PMID:11206050

  6. 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, is...

  7. 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

  8. 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

  9. 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...

  10. Network analysis of human protein location

    OpenAIRE

    Ranganathan Shoba; Kumar Gaurav

    2010-01-01

    Abstract Background Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary f...

  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. 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...

  13. 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 ...

  14. Structural flexibility of the G alpha s alpha-helical domain in the beta2-adrenoceptor Gs complex

    DEFF Research Database (Denmark)

    Westfield, Gerwin H; Rasmussen, Søren Gøgsig Faarup; Su, Min; Dutta, Somnath; DeVree, Brian T; Chung, Ka Young; Calinski, Diane; Velez-Ruiz, Gisselle; Oleskie, Austin N; Pardon, Els; Chae, Pil Seok; Liu, Tong; Li, Sheng; Woods, Virgil L; Steyaert, Jan; Kobilka, Brian K; Sunahara, Roger K; Skiniotis, Georgios

    2011-01-01

    The active-state complex between an agonist-bound receptor and a guanine nucleotide-free G protein represents the fundamental signaling assembly for the majority of hormone and neurotransmitter signaling. We applied single-particle electron microscopy (EM) analysis to examine the architecture of ...

  15. 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

  16. 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.

  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. How curved membranes recruit amphipathic helices and protein anchoring motifs

    DEFF Research Database (Denmark)

    Hatzakis, Nikos; Bhatia, Vikram Kjøller; Larsen, Jannik;

    2009-01-01

    : membrane-anchored proteins. The fact that unrelated structural motifs such as alpha-helices and alkyl chains sense MC led us to propose that MC sensing is a generic property of curved membranes rather than a property of the anchoring molecules. We therefore anticipate that MC will promote the...... redistribution of proteins that are anchored in membranes through other types of hydrophobic moieties....

  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. Network-Based Protein Biomarker Discovery Platforms.

    Science.gov (United States)

    Kim, Minhyung; Hwang, Daehee

    2016-03-01

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

  4. 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

  5. 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...

  6. 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

  7. 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

  8. 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...

  9. 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.

  10. 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...

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

    Directory of Open Access Journals (Sweden)

    Tan Kai

    2010-10-01

    Full Text Available 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-protein interaction network. The algorithm uses a novel graph theoretic measure, parametric local modularity, to identify highly connected sub-networks as candidate protein complexes. Using gold standard sets of protein complexes and protein function and localization annotations, we show our algorithm achieved an overall improvement over previous algorithms in terms of precision, recall, and biological relevance of the predicted complexes. We applied our algorithm to predict and characterize a set of 138 novel protein complexes in S. cerevisiae. Conclusions miPALM is a novel algorithm for detecting protein complexes from large protein-protein interaction networks with improved accuracy than previous methods. The software is implemented in Matlab and is freely available at http://www.medicine.uiowa.edu/Labs/tan/software.html.

  12. 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

  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. 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.

  15. 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...

  16. 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

  17. 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...

  18. 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.

  19. 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...

  20. 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.

  1. 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.

  2. 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

  3. Topological aspects of the human protein interaction network

    OpenAIRE

    Wiebringhaus, Thomas

    2008-01-01

    It is currently widely accepted that the understanding of complex cell functions depends on an integrated network theoretical approach and not on an isolated view of the different molecular agents. Aim of this thesis was the examination of topological properties that mirror known biological aspects by depicting the human protein network with methods from graph- and network theory. The presented network is a partial human interactome of 9222 proteins and 36324 interactions, consisting of singl...

  4. 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.

  5. 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.

  6. 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

  7. From Biogenesis to Overexpression of Membrane Proteins in Escherichia coli

    OpenAIRE

    Wagner, Samuel

    2008-01-01

    In both pro- and eukaryotes 20-30% of all genes encode alpha-helical transmembrane domain proteins, which act in various and often essential capacities. Notably, membrane proteins play key roles in disease and they constitute more than half of all known drug targets. The natural abundance of membrane proteins is in general too low to conveniently isolate sufficient material for functional and structural studies. Therefore, most membrane proteins have to be obtained through overexpression. Esc...

  8. Construction of Ontology Augmented Networks for Protein Complex Prediction

    OpenAIRE

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Jian WANG

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology a...

  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. TRAJELIX: a computational tool for the geometric characterization of protein helices during molecular dynamics simulations.

    Science.gov (United States)

    Mezei, Mihaly; Filizola, Marta

    2006-02-01

    We have developed a computer program with the necessary mathematical formalism for the geometric characterization of distorted conformations of alpha-helices proteins, such as those that can potentially be sampled during typical molecular dynamics simulations. This formalism has been incorporated into TRAJELIX, a new module within the SIMULAID framework (http://inka.mssm.edu/~mezei/simulaid/) that is capable of monitoring distortions of alpha-helices in terms of their displacement, global and local tilting, rotation around their axes, compression/extension, winding/unwinding, and bending. Accurate evaluation of these global and local structural properties of the helix can help study possible intramolecular and intermolecular changes in the helix packing of alpha-helical membrane proteins, as shown here in an application to the interacting helical domains of rhodopsin dimers. Quantification of the dynamic structural behavior of alpha-helical membrane proteins is critical for our understanding of signal transduction, and may enable structure-based design of more specific and efficient drugs. PMID:16783601

  12. 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 designe...... is better than most secondary structure prediction methods based on single sequences even though this model contains much fewer parameters...

  13. 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

  14. Convolutional LSTM Networks for Subcellular Localization of Proteins

    OpenAIRE

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

    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) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (...

  15. 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...

  16. 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...

  17. 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 ...

  18. 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...

  19. 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...

  20. Dissipative electro-elastic network model of protein electrostatics

    International Nuclear Information System (INIS)

    We propose a dissipative electro-elastic network model 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 protein 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. (paper)

  1. Identifying protein complexes in protein-protein interaction networks by using clique seeds and graph entropy.

    Science.gov (United States)

    Chen, Bolin; Shi, Jinhong; Zhang, Shenggui; Wu, Fang-Xiang

    2013-01-01

    The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one. PMID:23112006

  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. Multi-level integrative analysis of Protein Protein Interaction networks: connecting completeness, depth and robustness.

    Science.gov (United States)

    Blayney, Jaine K; Zheng, Huiru; Wang, Haiying; Azuaje, Francisco

    2010-01-01

    A fully extended Protein Protein Interaction (PPI) network can consist of upwards of several thousand nodes and edges. To simplify analysis, smaller child samples are often used in substitution of the global network. In this study, the impact of different levels of sampling was evaluated on six PPI networks. Results from the case studies suggest that restricting analysis to the first network level, using metrics such as degree and BC, could lead to misrepresentative results, omitting potentially significant nodes. Fault-tolerance analysis also indicates that key nodes within the second network level, and above, contribute to the stability of the global network. PMID:20693609

  4. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization

    OpenAIRE

    Hui LI; Liu, Chunmei

    2014-01-01

    3DProIN is a computational tool to visualize protein–protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The interne...

  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. 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

  8. Evolution of a protein domain interaction network

    International Nuclear Information System (INIS)

    In this paper, we attempt to understand complex network evolution from the underlying evolutionary relationship between biological organisms. Firstly, we construct a Pfam domain interaction network for each of the 470 completely sequenced organisms, and therefore each organism is correlated with a specific Pfam domain interaction network; secondly, we infer the evolutionary relationship of these organisms with the nearest neighbour joining method; thirdly, we use the evolutionary relationship between organisms constructed in the second step as the evolutionary course of the Pfam domain interaction network constructed in the first step. This analysis of the evolutionary course shows: (i) there is a conserved sub-network structure in network evolution; in this sub-network, nodes with lower degree prefer to maintain their connectivity invariant, and hubs tend to maintain their role as a hub is attached preferentially to new added nodes; (ii) few nodes are conserved as hubs; most of the other nodes are conserved as one with very low degree; (iii) in the course of network evolution, new nodes are added to the network either individually in most cases or as clusters with relative high clustering coefficients in a very few cases. (general)

  9. 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

  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. 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

  12. PhIN: A Protein Pharmacology Interaction Network Database

    OpenAIRE

    Wang, Z.; Li, J.; Dang, R; Liang, L; J. Lin

    2015-01-01

    Network pharmacology is a new and hot concept in drug discovery for its ability to investigate the complexity of polypharmacology, and becomes more and more important in drug development. Here we report a protein pharmacology interaction network database (PhIN), aiming to assist multitarget drug discovery by providing comprehensive and flexible network pharmacology analysis. Overall, PhIN contains 1,126,060 target–target interaction pairs in terms of shared compounds and 3,428,020 pairs in te...

  13. 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...

  14. 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...

  15. 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...

  16. 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.

  17. 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

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

    Science.gov (United States)

    Chen, Chen; Shen, Hong; Zhang, Li-Guo; Liu, Jian; Cao, Xiao-Ge; Yao, An-Liang; Kang, Shao-San; Gao, Wei-Xing; Han, Hui; Cao, Feng-Hong; Li, Zhi-Guo

    2016-06-01

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

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. Protein diffusion in photopolymerized poly(ethylene glycol) hydrogel networks

    Energy Technology Data Exchange (ETDEWEB)

    Engberg, Kristin; Frank, Curtis W, E-mail: curt.frank@stanford.edu [Department of Chemical Engineering, Stanford University, 381 North-South Mall, Stauffer III, Stanford, CA 94305 (United States)

    2011-10-15

    In this study, protein diffusion through swollen hydrogel networks prepared from end-linked poly(ethylene glycol)-diacrylate (PEG-DA) was investigated. Hydrogels were prepared via photopolymerization from PEG-DA macromonomer solutions of two molecular weights, 4600 Da and 8000 Da, with three initial solid contents: 20, 33 and 50 wt/wt% PEG. Diffusion coefficients for myoglobin traveling across the hydrogel membrane were determined for all PEG network compositions. The diffusion coefficient depended on PEG molecular weight and initial solid content, with the slowest diffusion occurring through lower molecular weight, high-solid-content networks (D{sub gel} = 0.16 {+-} 0.02 x 10{sup -8} cm{sup 2} s{sup -1}) and the fastest diffusion occurring through higher molecular weight, low-solid-content networks (D{sub gel} = 11.05 {+-} 0.43 x 10{sup -8} cm{sup 2} s{sup -1}). Myoglobin diffusion coefficients increased linearly with the increase of water content within the hydrogels. The permeability of three larger model proteins (horseradish peroxidase, bovine serum albumin and immunoglobulin G) through PEG(8000) hydrogel membranes was also examined, with the observation that globular molecules as large as 10.7 nm in hydrodynamic diameter can diffuse through the PEG network. Protein diffusion coefficients within the PEG hydrogels ranged from one to two orders of magnitude lower than the diffusion coefficients in free water. Network defects were determined to be a significant contributing factor to the observed protein diffusion.

  4. Clustering proteins into families using artificial neural networks.

    Science.gov (United States)

    Ferrán, E A; Ferrara, P

    1992-02-01

    An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori indication of the number or composition of the expected families, the network self-organized the activation of its neurons into topologically ordered maps in which almost all the proteins (96.7%) were correctly clustered into the corresponding families. In a second computational experiment, a similar network was trained with one family of the previous learning set (76 cytochrome c sequences). The new neural map clustered these proteins into 25 different neurons (five in the first experiment), wherein phylogenetically related sequences were positioned close to each other. This result shows that the network can adapt the clustering resolution to the complexity of the learning set, a useful feature when working with an unknown number of clusters. Although the learning stage is time consuming, once the topological map is obtained, the classification of new proteins is very fast. Altogether, our results suggest that this novel approach may be a useful tool to organize the search for homologies in large macromolecular databases. PMID:1314686

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

    Science.gov (United States)

    Poirot, Olivier; Timsit, Youri

    2016-01-01

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

  6. An alpha-helical extension of the ELMO1 pleckstrin homology domain mediates direct interaction to DOCK180 and is critical in Rac signaling.

    Science.gov (United States)

    Komander, David; Patel, Manishha; Laurin, Mélanie; Fradet, Nadine; Pelletier, Ariane; Barford, David; Côté, Jean-François

    2008-11-01

    The mammalian DOCK180 protein belongs to an evolutionarily conserved protein family, which together with ELMO proteins, is essential for activation of Rac GTPase-dependent biological processes. Here, we have analyzed the DOCK180-ELMO1 interaction, and map direct interaction interfaces to the N-terminal 200 amino acids of DOCK180, and to the C-terminal 200 amino acids of ELMO1, comprising the ELMO1 PH domain. Structural and biochemical analysis of this PH domain reveals that it is incapable of phospholipid binding, but instead structurally resembles FERM domains. Moreover, the structure revealed an N-terminal amphiphatic alpha-helix, and point mutants of invariant hydrophobic residues in this helix disrupt ELMO1-DOCK180 complex formation. A secondary interaction between ELMO1 and DOCK180 is conferred by the DOCK180 SH3 domain and proline-rich motifs at the ELMO1 C-terminus. Mutation of both DOCK180-interaction sites on ELMO1 is required to disrupt the DOCK180-ELMO1 complex. Significantly, although this does not affect DOCK180 GEF activity toward Rac in vivo, Rac signaling is impaired, implying additional roles for ELMO in mediating intracellular Rac signaling. PMID:18768751

  7. 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.

  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. 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.

  10. 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.

  11. Topology analysis and visualization of Potyvirus protein-protein interaction network

    OpenAIRE

    Bosque, Gabriel; Folch-Fortuny, Abel; Picó, Jesús; Ferrer, Alberto; Santiago, F Elena

    2014-01-01

    Background One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new...

  12. 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...

  13. 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%).

  14. Prediction of Protein Secondary Structures From Conformational Biases

    OpenAIRE

    Hoang, Trinh Xuan; Cieplak, Marek; Banavar, Jayanth R.; Maritan, Amos

    2002-01-01

    We use LINUS, a procedure developed by Srinivasan and Rose, to provide a physical interpretation of and to predict the secondary structures of proteins. The secondary structure type at a given site is identified by the largest conformational bias during short time simulations. We examine the rate of successful prediction as a function of temperature and the interaction window. At high temperatures, there is a large propensity for the establishment of $\\beta$-strands whereas $\\alpha$-helices a...

  15. 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.

  16. 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.

  17. 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.

  18. Graph theory and stability analysis of protein complex interaction networks.

    Science.gov (United States)

    Huang, Chien-Hung; Chen, Teng-Hung; Ng, Ka-Lok

    2016-04-01

    Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov-Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological-based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree-based, betweenness-based and brokering-coefficient-based perturbations have the largest effect on network stability. PMID:26997661

  19. Reconstruction and Application of Protein–Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Tong Hao

    2016-06-01

    Full Text Available The protein-protein interaction network (PIN is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

  20. 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

  1. 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

  2. Decoding protein networks during virus entry by quantitative proteomics.

    Science.gov (United States)

    Gerold, Gisa; Bruening, Janina; Pietschmann, Thomas

    2016-06-15

    Virus entry into host cells relies on interactions between viral and host structures including lipids, carbohydrates and proteins. Particularly, protein-protein interactions between viral surface proteins and host proteins as well as secondary host protein-protein interactions play a pivotal role in coordinating virus binding and uptake. These interactions are dynamic and frequently involve multiprotein complexes. In the past decade mass spectrometry based proteomics methods have reached sensitivities and high throughput compatibilities of genomics methods and now allow the reliable quantitation of proteins in complex samples from limited material. As proteomics provides essential information on the biologically active entity namely the protein, including its posttranslational modifications and its interactions with other proteins, it is an indispensable method in the virologist's toolbox. Here we review protein interactions during virus entry and compare classical biochemical methods to study entry with novel technically advanced quantitative proteomics techniques. We highlight the value of quantitative proteomics in mapping functional virus entry networks, discuss the benefits and limitations and illustrate how the methodology will help resolve unsettled questions in virus entry research in the future. PMID:26365680

  3. 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. Graphics processing unit-based alignment of protein interaction networks.

    Science.gov (United States)

    Xie, Jiang; Zhou, Zhonghua; Ma, Jin; Xiang, Chaojuan; Nie, Qing; Zhang, Wu

    2015-08-01

    Network alignment is an important bridge to understanding human protein-protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large-scale networks via sequential computing. In this study, the typical Hungarian-Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2-nearest neighbours (HGA-2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA-2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA-2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large-scale networks are considered. By using HGA-2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods. PMID:26243827

  6. Characterization of Protein Complexes and Protein Interaction Networks by Mass Spectrometry

    OpenAIRE

    Shevchenko, Anna

    2004-01-01

    The major goal of this study was to develop an experimental proteomics approach for deciphering protein complexes and protein interaction networks in the budding and fission yeasts. Key steps of the employed analytical routine, including the purification of complexes and mass spectrometric identification of their subunits, were investigated in detail. Archiving, storage and handling of polyacylamide gels, visualization of protein bands and their effect on the efficiency of in-gel digestion an...

  7. 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...

  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. 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 ...

  10. 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.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available 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 are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues, and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical 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 on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a KD of 22 microM and a VxxxRxYS motif 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.

  13. 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

  14. 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.

  15. 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.

  16. Protein thermal denaturation is modulated by central residues in the protein structure network.

    Science.gov (United States)

    Souza, Valquiria P; Ikegami, Cecília M; Arantes, Guilherme M; Marana, Sandro R

    2016-03-01

    Network structural analysis, known as residue interaction networks or graphs (RIN or RIG, respectively) or protein structural networks or graphs (PSN or PSG, respectively), comprises a useful tool for detecting important residues for protein function, stability, folding and allostery. In RIN, the tertiary structure is represented by a network in which residues (nodes) are connected by interactions (edges). Such structural networks have consistently presented a few central residues that are important for shortening the pathways linking any two residues in a protein structure. To experimentally demonstrate that central residues effectively participate in protein properties, mutations were directed to seven central residues of the β-glucosidase Sfβgly (β-d-glucoside glucohydrolase; EC 3.2.1.21). These mutations reduced the thermal stability of the enzyme, as evaluated by changes in transition temperature (Tm ) and the denaturation rate at 45 °C. Moreover, mutations directed to the vicinity of a central residue also caused significant decreases in the Tm of Sfβgly and clearly increased the unfolding rate constant at 45 °C. However, mutations at noncentral residues or at surrounding residues did not affect the thermal stability of Sfβgly. Therefore, the data reported in the present study suggest that the perturbation of the central residues reduced the stability of the native structure of Sfβgly. These results are in agreement with previous findings showing that networks are robust, whereas attacks on central nodes cause network failure. Finally, the present study demonstrates that central residues underlie the functional properties of proteins. PMID:26785700

  17. 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.

  18. 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

  19. Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell.

    Science.gov (United States)

    De Las Rivas, Javier; Fontanillo, Celia

    2012-11-01

    Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics. PMID:22908212

  20. 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...

  1. 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...

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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 are...... 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...... known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical...

  7. 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. PMID:26691180

  8. 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

  9. Validation of protein models by a neural network approach

    Directory of Open Access Journals (Sweden)

    Fantucci Piercarlo

    2008-01-01

    Full Text Available Abstract Background The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts.

  10. TPPII, MYBBP1A and CDK2 form a protein-protein interaction network.

    Science.gov (United States)

    Nahálková, Jarmila; Tomkinson, Birgitta

    2014-12-15

    Tripeptidyl-peptidase II (TPPII) is an aminopeptidase with suggested regulatory effects on cell cycle, apoptosis and senescence. A protein-protein interaction study revealed that TPPII physically interacts with the tumor suppressor MYBBP1A and the cell cycle regulator protein CDK2. Mutual protein-protein interaction was detected between MYBBP1A and CDK2 as well. In situ Proximity Ligation Assay (PLA) using HEK293 cells overexpressing TPPII forming highly enzymatically active oligomeric complexes showed that the cytoplasmic interaction frequency of TPPII with MYBBP1A increased with the protein expression of TPPII and using serum-free cell growth conditions. A specific reversible inhibitor of TPPII, butabindide, suppressed the cytoplasmic interactions of TPPII and MYBBP1A both in control HEK293 and the cells overexpressing murine TPPII. The interaction of MYBBP1A with CDK2 was confirmed by in situ PLA in two different mammalian cell lines. Functional link between TPPII and MYBBP1A has been verified by gene expression study during anoikis, where overexpression of TPP II decreased mRNA expression level of MYBBP1A at the cell detachment conditions. All three interacting proteins TPPII, MYBBP1A and CDK2 have been previously implicated in the research for development of tumor-suppressing agents. This is the first report presenting mutual protein-protein interaction network of these proteins. PMID:25303791

  11. Development and implementation of an algorithm for detection of protein complexes in large interaction networks

    Directory of Open Access Journals (Sweden)

    Kanaya Shigehiko

    2006-04-01

    Full Text Available Abstract Background After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI networks warrants development of efficient computational methods for extraction of significant complexes. Results This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. Conclusion The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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

    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...

  20. 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...

  1. 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.

  2. 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.

  3. 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

  4. Production and characterisation of recombinant forms of human pulmonary surfactant protein C (SP-C)

    DEFF Research Database (Denmark)

    Lukovic, Dunja; Plasencia, Inés; Taberner, Francisco J;

    2006-01-01

    recombinant SP-C variants so obtained retained more than 50% alpha-helical content and showed surface activity comparable to the native protein, as measured by surface spreading of lipid/protein suspensions and from compression pi-A isotherms of lipid/protein films. Compared to the protein purified from......Surfactant protein C (SP-C) is an essential component for the surface tension-lowering activity of the pulmonary surfactant system. It contains a valine-rich alpha helix that spans the lipid bilayer, and is one of the most hydrophobic proteins known so far. SP-C is also an essential component of...

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. Protein Interaction Network of Arabidopsis thaliana Female Gametophyte Development Identifies Novel Proteins and Relations

    OpenAIRE

    Hosseinpour, Batool; HajiHoseini, Vahid; Kashfi, Rafieh; Ebrahimie, Esmaeil; Hemmatzadeh, Farhid

    2012-01-01

    Although the female gametophyte in angiosperms consists of just seven cells, it has a complex biological network. In this study, female gametophyte microarray data from Arabidopsis thaliana were integrated into the Arabidopsis interactome database to generate a putative interaction map of the female gametophyte development including proteome map based on biological processes and molecular functions of proteins. Biological and functional groups as well as topological characteristics of the net...

  10. 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...

  11. 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.

  12. 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...

  13. 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...

  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 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.

  16. Discovering Protein-Protein Interactions within the Programmed Cell Death Network Using a Protein-Fragment Complementation Screen

    Directory of Open Access Journals (Sweden)

    Yuval Gilad

    2014-08-01

    Full Text Available Apoptosis and autophagy are distinct biological processes, each driven by a different set of protein-protein interactions, with significant crosstalk via direct interactions among apoptotic and autophagic proteins. To measure the global profile of these interactions, we adapted the Gaussia luciferase protein-fragment complementation assay (GLuc PCA, which monitors binding between proteins fused to complementary fragments of a luciferase reporter. A library encompassing 63 apoptotic and autophagic proteins was constructed for the analysis of ∼3,600 protein-pair combinations. This generated a detailed landscape of the apoptotic and autophagic modules and points of interface between them, identifying 46 previously unknown interactions. One of these interactions, between DAPK2, a Ser/Thr kinase that promotes autophagy, and 14-3-3τ, was further investigated. We mapped the region responsible for 14-3-3τ binding and proved that this interaction inhibits DAPK2 dimerization and activity. This proof of concept underscores the power of the GLuc PCA platform for the discovery of biochemical pathways within the cell death network.

  17. 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...

  18. 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...

  19. Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer

    OpenAIRE

    Engin, H Billur; G??ney, Emre, 1983-; Keskin, Ozlem; Oliva Miguel, Baldomero; Gursoy, Attila

    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...

  20. Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity

    Directory of Open Access Journals (Sweden)

    Ali Kazemi-Pour

    2015-01-01

    Full Text Available The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions.

  1. Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model.

    Science.gov (United States)

    Morilla, Ian; Lees, Jon G; Reid, Adam J; Orengo, Christine; Ranea, Juan A G

    2010-12-31

    In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Some proteins, involved in a common biological process and encoded by separate genes in one organism, can be found fused within a single protein chain in other organisms. By detecting these triplets, a functional relationship can be established between the unfused proteins. Here we use a domain fusion prediction method to predict these protein interactions for the human interactome. We observed that gene fusion events are more related to physical interaction between proteins than to other weaker functional relationships such as participation in a common biological pathway. These results suggest that domain fusion is an appropriate method for predicting protein complexes. The most reliable fused domain predictions were used to build protein-protein interaction (PPI) networks. These predicted PPI network models showed the same topological features as real biological networks and different features from random behaviour. We built the PPI domain fusion sub-network model of the human kinetochore and observed that the majority of the predicted interactions have not yet been experimentally characterised in the publicly available PPI repositories. The study of the human kinetochore domain fusion sub-network reveals undiscovered kinetochore proteins with presumably relevant functions, such as hubs with many connections in the kinetochore sub-network. These results suggest that experimentally hidden regions in the predicted PPI networks contain key functional elements, associated with important functional areas, still undiscovered in the human interactome. Until novel experiments shed light on these hidden regions; domain fusion predictions provide a valuable approach for exploring them. PMID:20851221

  2. 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; Jensen, Knud Jørgen

    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...... hydrophobic core. Assembly of protein models on a template has been suggested as a way to reduce the entropy of folding. We have previously developed the concept of carbohydrates as templates in the de novo design of protein models termed 'carboproteins'. Here we present the chemical synthesis of three 8.1 k...... melting points in chemical and thermal denaturation experiments....

  3. 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

  4. 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...

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

    DEFF Research Database (Denmark)

    Ehrlich, L.; Reczko, M.; Bohr, Henrik; Wade, R.C.

    1998-01-01

    separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are teined with protein crystal structures. The prediction of hydration is improved by adding information on secondary......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...... using the actual values. The inclusion of property information allows a smaller squence window to be used in the networks to predict hydration. It has a greater impact on the accuracy of hydration site prediction for backbone atoms than for sidechains and for non-polar than polar residues. The networks...

  6. 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

  7. 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)

  8. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

    Directory of Open Access Journals (Sweden)

    Oyang Yen-Jen

    2010-10-01

    Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

  9. 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

  10. 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...

  11. Illuminating Spatial and Temporal Organization of Protein Interaction Networks by Mass Spectrometry-Based Proteomics

    OpenAIRE

    Yang, Jiwen; Wagner, Sebastian A; Beli, Petra

    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 (MS)-based proteomics has delivered unprecedented insights into human protein interaction networks. Affinity purification-MS (AP-MS) has been extensively employed for focused and high-throughput studies of steady state protein–protein interactions. Future challenges remain in mapping transient protein interact...

  12. 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

  13. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  15. 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.

  16. 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.

  17. 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...

  18. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  19. 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.

  20. 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.

  1. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks.

    Science.gov (United States)

    White, Forest M; Wolf-Yadlin, Alejandro

    2016-06-12

    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. PMID:27049636

  2. 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

  3. 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

  4. 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/.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Calculated dynamical scattering in a fibrous protein structure

    International Nuclear Information System (INIS)

    The effects of dynamical electron scattering on the amplitudes and phases diffracted by the microcrystals of an alpha-helical coiled-coil protein have been investigated by computer simulation using a model structure derived from relatively low resolution data recorded at liquid nitrogen temperature. The projected potential of this model show only small departure from the weak-phase-object approximation predictions, with amplitudes increasing almost linearly with thickness and phases nearly constant up to thickness of 200 Angstroems. 4 refs., 3 figs

  7. A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein-Protein Interaction Networks: A Case Study of Mycobacterium leprae.

    Science.gov (United States)

    Akinola, Richard O; Mazandu, Gaston K; Mulder, Nicola J

    2016-01-01

    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 neighbors 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. PMID:27066064

  8. 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.

  9. Eyes underground: regression of visual protein networks in subterranean mammals.

    Science.gov (United States)

    Emerling, Christopher A; Springer, Mark S

    2014-09-01

    Regressive evolution involves the degeneration of formerly useful structures in a lineage over time, and may be accompanied by the molecular decay of phenotype-specific genes. The mammalian eye has repeatedly undergone degeneration in taxa that occupy dim-light environments including subterranean habitats. Here we assess whether a decrease in the amount of light that reaches the retina is associated with increased regression of retinal genes, whether the phototransduction and visual cycle pathways degrade in a predictable pattern, and if the timing of retinal gene loss is associated with the entrance of mammalian lineages into subterranean environments. Sequence data were obtained from the publically available genomes of the Cape golden mole (Chrysochloris asiatica), naked mole-rat (Heterocephalus glaber) and star-nosed mole (Condylura cristata) for 65 genes associated with phototransduction, the visual cycle, and other retinal functions. Gene sequences were inspected for inactivating mutations and, when present, pseudogene sequences were compared to sequences from subaerial outgroup species. To test whether retinal degeneration is correlated with historical entrances into subterranean environments, estimated dates of retinal gene inactivation were compared to the fossil record and phylogenetic inferences of ancestral fossoriality. Our results show that (1) lower levels of light available to the retina correspond with an increase in the number of retinal pseudogenes, (2) retinal protein networks generally degrade in a predictable manner, although the extensive loss of cone phototransduction genes in Heterocephalus raises further questions regarding SWS1-cone monochromacy versus functional rod monochromacy in this species, and (3) inactivation dates of retinal genes usually post-date inferred entrances into subterranean habitats. PMID:24859681

  10. Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat.

    Science.gov (United States)

    Babbitt, Patricia C; Bagos, Pantelis G; Bairoch, Amos; Bateman, Alex; Chatonnet, Arnaud; Chen, Mark Jinan; Craik, David J; Finn, Robert D; Gloriam, David; Haft, Daniel H; Henrissat, Bernard; Holliday, Gemma L; Isberg, Vignir; Kaas, Quentin; Landsman, David; Lenfant, Nicolas; Manning, Gerard; Nagano, Nozomi; Srinivasan, Narayanaswamy; O'Donovan, Claire; Pruitt, Kim D; Sowdhamini, Ramanathan; Rawlings, Neil D; Saier, Milton H; Sharman, Joanna L; Spedding, Michael; Tsirigos, Konstantinos D; Vastermark, Ake; Vriend, Gerrit

    2015-01-01

    During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from 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 and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue. PMID:26284514

  11. 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

  12. How does a simplified-sequence protein fold?

    Science.gov (United States)

    Guarnera, Enrico; Pellarin, Riccardo; Caflisch, Amedeo

    2009-09-16

    To investigate a putatively primordial protein we have simplified the sequence of a 56-residue alpha/beta fold (the immunoglobulin-binding domain of protein G) by replacing it with polyalanine, polythreonine, and diglycine segments at regions of the sequence that in the folded structure are alpha-helical, beta-strand, and turns, respectively. Remarkably, multiple folding and unfolding events are observed in a 15-micros molecular dynamics simulation at 330 K. The most stable state (populated at approximately 20%) of the simplified-sequence variant of protein G has the same alpha/beta topology as the wild-type but shows the characteristics of a molten globule, i.e., loose contacts among side chains and lack of a specific hydrophobic core. The unfolded state is heterogeneous and includes a variety of alpha/beta topologies but also fully alpha-helical and fully beta-sheet structures. Transitions within the denatured state are very fast, and the molten-globule state is reached in structure of the wild-type is more rigid than the molten-globule conformation of the simplified-sequence variant. The difference in structural stability and the very fast folding of the simplified protein suggest that evolution has enriched the primordial alphabet of amino acids mainly to optimize protein function by stabilization of a unique structure with specific tertiary interactions. PMID:19751679

  13. Survey: Enhancing protein complex prediction in PPI networks with GO similarity weighting.

    Science.gov (United States)

    Price, True; Peña, Francisco I; Cho, Young-Rae

    2013-09-01

    Predicting protein complexes from protein-protein interaction (PPI) networks has been the focus of many computational approaches over the last decade. These methods tend to vary in performance based on the structure of the network and the parameters provided to the algorithm. Here, we evaluate the merits of enhancing PPI networks with semantic similarity edge weights using Gene Ontology (GO) and its annotation data. We compare the cluster features and predictive efficacy of six well-known unweighted protein complex detection methods (Clique Percolation, MCODE, DPClus, IPCA, Graph Entropy, and CoAch) against updated weighted implementations. We conclude that incorporating semantic similarity edge weighting in PPI network analysis unequivocally increases the performance of these methods. PMID:24307411

  14. 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.

  15. 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.

  16. 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

  17. 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) model...

  18. 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...

  19. 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.

  20. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  1. 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

  2. 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...

  3. 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

  4. 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.

  5. 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

  6. Finding the "dark matter" in human and yeast protein network prediction and modelling.

    Science.gov (United States)

    Ranea, Juan A G; Morilla, Ian; Lees, Jon G; Reid, Adam J; Yeats, Corin; Clegg, Andrew B; Sanchez-Jimenez, Francisca; Orengo, Christine

    2010-01-01

    Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case

  7. 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...

  8. 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.

  9. 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.

  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...

  11. 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...

  12. 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.

  13. How does the antagonism between capping and anti-capping proteins affect actin network dynamics?

    International Nuclear Information System (INIS)

    Actin-based cell motility is essential to many biological processes. We built a simplified, three-dimensional computational model and subsequently performed stochastic simulations to study the growth dynamics of lamellipodia-like branched networks. In this work, we shed light on the antagonism between capping and anti-capping proteins in regulating actin dynamics in the filamentous network. We discuss detailed mechanisms by which capping and anti-capping proteins affect the protrusion speed of the actin network and the rate of nucleation of filaments. We computed a phase diagram showing the regimes of motility enhancement and inhibition by these proteins. Our work shows that the effects of capping and anti-capping proteins are mainly transmitted by modulation of the filamentous network density and local availability of monomeric actin. We discovered that the combination of the capping/anti-capping regulatory network with nucleation-promoting proteins introduces robustness and redundancy in cell motility machinery, allowing the cell to easily achieve maximal protrusion speeds under a broader set of conditions. Finally, we discuss distributions of filament lengths under various conditions and speculate on their potential implication for the emergence of filopodia from the lamellipodial network.

  14. How does the antagonism between capping and anti-capping proteins affect actin network dynamics?

    Science.gov (United States)

    Hu, Longhua; Papoian, Garegin A.

    2011-09-01

    Actin-based cell motility is essential to many biological processes. We built a simplified, three-dimensional computational model and subsequently performed stochastic simulations to study the growth dynamics of lamellipodia-like branched networks. In this work, we shed light on the antagonism between capping and anti-capping proteins in regulating actin dynamics in the filamentous network. We discuss detailed mechanisms by which capping and anti-capping proteins affect the protrusion speed of the actin network and the rate of nucleation of filaments. We computed a phase diagram showing the regimes of motility enhancement and inhibition by these proteins. Our work shows that the effects of capping and anti-capping proteins are mainly transmitted by modulation of the filamentous network density and local availability of monomeric actin. We discovered that the combination of the capping/anti-capping regulatory network with nucleation-promoting proteins introduces robustness and redundancy in cell motility machinery, allowing the cell to easily achieve maximal protrusion speeds under a broader set of conditions. Finally, we discuss distributions of filament lengths under various conditions and speculate on their potential implication for the emergence of filopodia from the lamellipodial network.

  15. How does the antagonism between capping and anti-capping proteins affect actin network dynamics?

    Energy Technology Data Exchange (ETDEWEB)

    Hu Longhua; Papoian, Garegin A, E-mail: gpapoian@umd.edu [Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742 (United States)

    2011-09-21

    Actin-based cell motility is essential to many biological processes. We built a simplified, three-dimensional computational model and subsequently performed stochastic simulations to study the growth dynamics of lamellipodia-like branched networks. In this work, we shed light on the antagonism between capping and anti-capping proteins in regulating actin dynamics in the filamentous network. We discuss detailed mechanisms by which capping and anti-capping proteins affect the protrusion speed of the actin network and the rate of nucleation of filaments. We computed a phase diagram showing the regimes of motility enhancement and inhibition by these proteins. Our work shows that the effects of capping and anti-capping proteins are mainly transmitted by modulation of the filamentous network density and local availability of monomeric actin. We discovered that the combination of the capping/anti-capping regulatory network with nucleation-promoting proteins introduces robustness and redundancy in cell motility machinery, allowing the cell to easily achieve maximal protrusion speeds under a broader set of conditions. Finally, we discuss distributions of filament lengths under various conditions and speculate on their potential implication for the emergence of filopodia from the lamellipodial network.

  16. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics.

    Science.gov (United States)

    Yu, Haiyuan; Kim, Philip M; Sprecher, Emmett; Trifonov, Valery; Gerstein, Mark

    2007-04-20

    It has been a long-standing goal in systems biology to find relations between the topological properties and functional features of protein networks. However, most of the focus in network studies has been on highly connected proteins ("hubs"). As a complementary notion, it is possible to define bottlenecks as proteins with a high betweenness centrality (i.e., network nodes that have many "shortest paths" going through them, analogous to major bridges and tunnels on a highway map). Bottlenecks are, in fact, key connector proteins with surprising functional and dynamic properties. In particular, they are more likely to be essential proteins. In fact, in regulatory and other directed networks, betweenness (i.e., "bottleneck-ness") is a much more significant indicator of essentiality than degree (i.e., "hub-ness"). Furthermore, bottlenecks correspond to the dynamic components of the interaction network-they are significantly less well coexpressed with their neighbors than non-bottlenecks, implying that expression dynamics is wired into the network topology. PMID:17447836

  17. 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.

  18. 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.; Kofoed, C.B.; Blom, Nikolaj; Sicheritz-Pontén, Thomas; Larsen, M.R.; Brunak, Søren; Jensen, O.N.; Gammeltoft, S.

    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 in...

  19. 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.

  20. 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.

  1. Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

    CERN Document Server

    Abi-Haidar, Alaa; Maguitman, Ana G; Radivojac, Predrag; Retchsteiner, Andreas; Verspoor, Karin; Wang, Zhiping; Rocha, Luis M; 10.1186/gb-2008-9-s2-s11

    2008-01-01

    We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS), discovery of protein pairs (IPS) and text passages characterizing protein interaction (ISS) in full text documents. We approached the abstract classification task with a novel, lightweight linear model inspired by spam-detection techniques, as well as an uncertainty-based integration scheme. We also used a Support Vector Machine and the Singular Value Decomposition on the same features for comparison purposes. Our approach to the full text subtasks (protein pair and passage identification) includes a feature expansion method based on word-proximity networks. Our approach to the abstract classification task (IAS) was among the top submissions for this task in terms of the measures of performance used in the challenge evaluation (accuracy, F-score and AUC). We also report on a web-tool we produced using our approach: the Protein Int...

  2. Characterization of the KLP68D kinesin-like protein in Drosophila: possible roles in axonal transport

    OpenAIRE

    1994-01-01

    This paper describes the molecular and biochemical properties of KLP68D, a new kinesin-like motor protein in Drosophila melanogaster. Sequence analysis of a full-length cDNA encoding KLP68D demonstrates that this protein has a domain that shares significant sequence identity with the entire 340-amin acid kinesin heavy chain motor domain. Sequences extending beyond the motor domain predict a region of alpha-helical coiled-coil followed by a globular "tail" region; there is significant sequence...

  3. 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

  4. 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

  5. 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...

  6. 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...

  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. 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.

  10. 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

  11. 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.

  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

    International Nuclear Information System (INIS)

    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. 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...

  16. A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks.

    Science.gov (United States)

    Zhang, Shihua; Liu, Hong-Wei; Ning, Xue-Mei; Zhang, Xiang-Sun

    2009-01-01

    Modular architecture, which encompasses groups of genes/proteins involved in elementary biological functional units, is a basic form of the organisation of interacting proteins. Here, we propose a method that combines the Line Graph Transformation (LGT) and clique percolation-clustering algorithm to detect network modules, which may overlap each other in large sparse PPI networks. The resulting modules by the present method show a high coverage among yeast, fly, and worm PPI networks, respectively. Our analysis of the yeast PPI network suggests that most of these modules have well-biological significance in context of protein localisation, function annotation, and protein complexes. PMID:19432377

  17. 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

  18. 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.

  19. 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).

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

    Science.gov (United States)

    Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.

    2000-01-01

    A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (Like Sm) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative genomic two-hybrid screens, starting with the Lsm proteins as baits. Indeed, extensive interactions amongst eight Lsm proteins were found that suggest the existence of a Lsm complex or complexes. These Lsm interactions apparently involve the conserved Sm domain that also mediates interactions between the Sm proteins. The screens also reveal functionally significant interactions with splicing factors, in particular with Prp4 and Prp24, compatible with genetic studies and with the reported association of Lsm proteins with spliceosomal U6 and U4/U6 particles. In addition, interactions with proteins involved in mRNA turnover, such as Mrt1, Dcp1, Dcp2 and Xrn1, point to roles for Lsm complexes in distinct RNA metabolic processes, that are confirmed in independent functional studies. These results provide compelling evidence that two-hybrid screens yield functionally meaningful information about protein–protein interactions and can suggest functions for uncharacterized proteins, especially when they are performed on a genome-wide scale. PMID:10900456

  6. Integration and visualization of non-coding RNA and protein interaction networks

    OpenAIRE

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian; Pan, Xiaoyong; Santos Delgado, Alberto; Anthon, Christian; Alkan, Ferhat; von Mering, Christian; Workman, Christopher; Jensen, Lars Juhl; Gorodkin, Jan

    2015-01-01

    Non-coding RNAs (ncRNAs) fulfill a diverse set of biological functions relying on interactions with other molecular entities. The advent of new experimental and computational approaches makes it possible to study ncRNAs and their associations on an unprecedented scale. We present RAIN (RNA Association and Interaction Networks) - a database that combines ncRNA-ncRNA, ncRNA-mRNA and ncRNA-protein interactions with large-scale protein association networks available in the STRING database. By int...

  7. 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.

  8. 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

  9. Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

    OpenAIRE

    Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.; Legrain, Pierre

    2000-01-01

    A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (Like Sm) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative geno...

  10. Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

    OpenAIRE

    Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.; Legrain, Pierre

    2000-01-01

    A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (L¯ike Sm¯) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative ge...

  11. 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

    2016-01-01

    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. PMID:27198619

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. 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...

  17. 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

  18. 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...

  19. 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...

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

    Directory of Open Access Journals (Sweden)

    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

  1. 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 ...

  2. 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.

  3. 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.

  4. Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map

    OpenAIRE

    Park, D; Lee, S; Bolser, D.; Schroeder, M.; Lappe, M.; Oh, D.; Bhak, J.

    2005-01-01

    Motivation: Many genomes have been completely sequenced. However, detecting and analyzing their protein–protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein–protein interaction rules within and among complete genomes. Results: We confirmed that all the pred...

  5. 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...

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. 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...

  9. 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

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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

  13. 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...... region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...

  14. 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

  15. 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.

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

    Directory of Open Access Journals (Sweden)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  18. Transmembrane protein topology prediction using support vector machines

    Directory of Open Access Journals (Sweden)

    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.

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. Prediction of protein-protein interaction network using a multi-objective optimization approach.

    Science.gov (United States)

    Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

    2016-06-01

    Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score. PMID:26846814

  4. 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

  5. The network of P(II) signalling protein interactions in unicellular cyanobacteria.

    Science.gov (United States)

    Forchhammer, Karl

    2010-01-01

    P(II) signalling proteins constitute a large superfamily of signal perception and transduction proteins, which is represented in all domains of life and whose members play central roles in coordinating nitrogen assimilation. Generally, P(II) proteins act as sensors of the cellular adenylylate energy charge and 2-oxoglutarate level, and in response to these signals, they regulate central nitrogen assimilatory processes at various levels of control (from nutrient transport to gene expression) through protein-protein interactions with P(II) receptor proteins. An examination of the phylogeny of cyanobacteria reveals that specific functions of P(II) signalling evolved in this microbial lineage, which are not found in other prokaryotes. At least one of these functions, regulation of arginine biosynthesis by controlling the key enzyme N-acetyl-L: -glutamate kinase (NAGK), was transmitted by the ancestral cyanobacterium, which gave rise to chloroplasts, into the eukaryotic domain and was conserved during the evolution of planta. We have investigated in some detail the P(II) signalling protein, its signal perception and its interactions with receptors in the unicellular cyanobacteria Synechococcus elongatus PCC 7942 and Synechocystis PCC 6803 and have performed comparative analysis with Arabidopsis thaliana P(II)-NAGK interaction. This chapter will summarize these studies and will describe the emerging picture of a complex network of P(II) protein interactions in the unicellular cyanobacteria. PMID:20532736

  6. 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......, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the...... known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type...

  7. 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

  8. The cost and capacity of signaling in the Escherichia coli protein reaction network

    International Nuclear Information System (INIS)

    In systems biology new ways are required to analyze the large amount of existing data on regulation of cellular processes. Recent work can be roughly classified into either dynamical models of well-described subsystems, or coarse-grained descriptions of the topology of the molecular networks at the scale of the whole organism. In order to bridge these two disparate approaches one needs to develop simplified descriptions of dynamics and topological measures which address the propagation of signals in molecular networks. Transmission of a signal across a reaction node depends on the presence of other reactants. It will typically be more demanding to transmit a signal across a reaction node with more input links. Sending signals along a path with several subsequent reaction nodes also increases the constraints on the presence of other proteins in the overall network. Therefore counting in and out links along reactions of a potential pathway can give insight into the signaling properties of a particular molecular network. Here, we consider the directed network of protein regulation in E. coli, characterizing its modularity in terms of its potential to transmit signals. We demonstrate that the simplest measure based on identifying subnetworks of strong components, within which each node could send a signal to every other node, does indeed partition the network into functional modules. We suggest that the total number of reactants needed to send a signal between two nodes in the network can be considered as the cost associated with transmitting this signal. Similarly we define spread as the number of reaction products that could be influenced by transmission of a successful signal. Our considerations open for a new class of network measures that implicitly utilize the constrained repertoire of chemical modifications of any biological molecule. The counting of cost and spread connects the topology of networks to the specificity of signaling across the network. Thereby, we

  9. A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases.

    Science.gov (United States)

    Scott-Boyer, Marie Pier; Lacroix, Sébastien; Scotti, Marco; Morine, Melissa J; Kaput, Jim; Priami, Corrado

    2016-01-01

    The involvement of vitamins and other micronutrients in intermediary metabolism was elucidated in the mid 1900's at the level of individual biochemical reactions. Biochemical pathways remain the foundational knowledgebase for understanding how micronutrient adequacy modulates health in all life stages. Current daily recommended intakes were usually established on the basis of the association of a single nutrient to a single, most sensitive adverse effect and thus neglect interdependent and pleiotropic effects of micronutrients on biological systems. Hence, the understanding of the impact of overt or sub-clinical nutrient deficiencies on biological processes remains incomplete. Developing a more complete view of the role of micronutrients and their metabolic products in protein-mediated reactions is of importance. We thus integrated and represented cofactor-protein interaction data from multiple and diverse sources into a multi-layer network representation that links cofactors, cofactor-interacting proteins, biological processes, and diseases. Network representation of this information is a key feature of the present analysis and enables the integration of data from individual biochemical reactions and protein-protein interactions into a systems view, which may guide strategies for targeted nutritional interventions aimed at improving health and preventing diseases. PMID:26777674

  10. 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

  11. 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...

  12. 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...

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

    International Nuclear Information System (INIS)

    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 (φ, ψ) torsion angles of ca 12º. TALOS-N also reports sidechain χ1 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

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

    Directory of Open Access Journals (Sweden)

    Care Matthew A

    2006-09-01

    Full Text Available Abstract Background A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. Results Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. Conclusion The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

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

    Science.gov (United States)

    Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino

    2008-02-01

    Integrated nanodevices based on proteins or biomolecules are attracting increasing interest in today's research. In fact, it has been shown that proteins such as azurin and bacteriorhodopsin manifest some electrical properties that are promising for the development of active components of molecular electronic devices. Here we focus on two relevant kinds of protein: bovine rhodopsin, prototype of G-protein-coupled-receptor (GPCR) proteins, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer's disease. Both these proteins exert their function starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different impedance spectra associated with the different configurations. The distinct types of conformational change of rhodopsin and AChE agree with their dissimilar electrical responses. In particular, for rhodopsin the model predicts variations of the impedance spectra up to about 30%, while for AChE the same variations are limited to about 10%, which supports the existence of a dynamical equilibrium between its native and complexed states.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. Uncovering the protein lysine and arginine methylation network in Arabidopsis chloroplasts.

    Science.gov (United States)

    Alban, Claude; Tardif, Marianne; Mininno, Morgane; Brugière, Sabine; Gilgen, Annabelle; Ma, Sheng; Mazzoleni, Meryl; Gigarel, Océane; Martin-Laffon, Jacqueline; Ferro, Myriam; Ravanel, Stéphane

    2014-01-01

    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. PMID:24748391

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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...

  11. 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

  12. 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%.

  13. 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...

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. SynGAP regulates protein synthesis and homeostatic synaptic plasticity in developing cortical networks.

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Wang

    Full Text Available Disrupting the balance between excitatory and inhibitory neurotransmission in the developing brain has been causally linked with intellectual disability (ID and autism spectrum disorders (ASD. Excitatory synapse strength is regulated in the central nervous system by controlling the number of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs. De novo genetic mutations of the synaptic GTPase-activating protein (SynGAP are associated with ID and ASD. SynGAP is enriched at excitatory synapses and genetic suppression of SynGAP increases excitatory synaptic strength. However, exactly how SynGAP acts to maintain synaptic AMPAR content is unclear. We show here that SynGAP limits excitatory synaptic strength, in part, by suppressing protein synthesis in cortical neurons. The data presented here from in vitro, rat and mouse cortical networks, demonstrate that regulation of translation by SynGAP involves ERK, mTOR, and the small GTP-binding protein Rheb. Furthermore, these data show that GluN2B-containing NMDARs and the cognitive kinase CaMKII act upstream of SynGAP and that this signaling cascade is required for proper translation-dependent homeostatic synaptic plasticity of excitatory synapses in developing cortical networks.

  19. 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

  20. Bottom-up study of flaw tolerance properties of protein networks

    Science.gov (United States)

    Qin, Zhao; Buehler, Markus

    2012-02-01

    We study the material properties of an intermediate filament proten network by computational modeling using a bottom-up approach. We start with an atomic model of each filament's and obtain the mechanical behavior of them. We then use these parameters in setting up a mesoscale model of the network material at scales of micrometers. Using this multi-scale method, we report a detailed analysis of the associated deformation and failure mechanisms of this hierarchical material. Our modeling reveals that a structure transition that occurs at the proteins' secondary structure level is crucial for the networks' flaw tolerance property, which implies that the material retains its mechanical function despite the existence of large defects. We also examine the effect of crosslink strength on the failure properties. We discover that relatively weaker crosslinks lead to a more flaw tolerant network that is 23% stronger. This unexpected behavior is caused by that the crosslink strength functions as a switch to alter the failure mechanism. Weak crosslinks are able to efficiently diffuse the stress around the crack tip, making the crack more difficult to propagate. We compare our results to that of elastic and softening materials and find that the effect of crosslink strength is much smaller in those systems. These findings imply that the mechanical properties of both the filaments and interfaces among filaments are critical for bioinspired material designs, challenging the conventional paradigm in engineering design.

  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. GBNV encoded movement protein (NSm) remodels ER network via C-terminal coiled coil domain

    International Nuclear Information System (INIS)

    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

  3. 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.

  4. 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.

  5. 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.

  6. Phylogeny, Functional Annotation, and Protein Interaction Network Analyses of the Xenopus tropicalis Basic Helix-Loop-Helix Transcription Factors

    Directory of Open Access Journals (Sweden)

    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.

  7. In-silico designing of a potent analogue against HIV-1 Nef protein and protease by predicting its interaction network with host cell proteins

    Directory of Open Access Journals (Sweden)

    Shikha Pal

    2013-01-01

    Full Text Available Background: HIV-1 has numerous proteins encoded within its genome, which acquaints it with the required arsenal to establish a favorable host cell environment suitable for viral replication and pathogenesis. Among these proteins, one protein that is indispensable and ambiguous is the Nef protein. Aim: Interaction of Nef protein with different host-cell protein was predicted and subsequently the down regulation of cluster of differentiation 4 (CD4 was targeted through designing of inhibitors of Nef protein for either preventing or if not at least delaying pathogenesis. Materials and Methods: The interaction network of Nef protein with host-cell proteins were predicted by PIMRider. Analogue of Lopinavir were prepared and evaluated considering all factors affecting the drug stability and toxicity. Finally Docking simulation were performed using an Auto-Dock Tool 4.0. Results: In the interaction network of Nef protein with different host-cell proteins it was found out that 22 host cell proteins are involved in the interaction and execution of different types of functions in host cell but these functions are altered with the interaction with the Nef protein. After extensive and controlled in silico analysis it has been observed that the analogue LOPI1 binds to Nef protein (2NEF at CD4 interacting site residues giving minimum binding energy of −7.68 Kcal/mole, low Ki value of 2.34 μM, maximum number of hydrogen bonds (8, good absorption, distribution, metabolism and excretion properties, and less toxicity in comparison with the standard Lopinavir against HIV1 protease (1HPV. Conclusion: The newly designed analogue (LOPI1 is showing significant in silico interaction with Nef protein and protease and can be taken forward as a potent drug lead, which may finally emerge out to be even better than the standard Lopinavir.

  8. Fault tolerance in protein interaction networks: stable bipartite subgraphs and redundant pathways.

    Directory of Open Access Journals (Sweden)

    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.

  9. 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

  10. Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family

    Directory of Open Access Journals (Sweden)

    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

  11. 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...

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

    DEFF Research Database (Denmark)

    Hansen, Kasper Lage; Mølgård, Kjeld; Greenway, Steven; Wakimoto, Hiroko; Gorham, Joshua M.; Workman, Christopher; Bendsen, Eske; Hansen, Niclas Tue; Rigina, Olga; Roque, Francisco José Sousa Simões Almeida; Wiese, Cornelia; Christoffels, Vincent M.; Roberts, Amy E.; Smoot, Leslie B.; Pu, William T.; Donahoe, Patricia K.; Tommerup, Niels; Brunak, Søren; Seidman, Christine E.; Seidman, Jonathan G.; Larsen, Lars A.

    2010-01-01

    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...... blocks, and suggests how mutations in the same genes can lead to diverse phenotypes. We observe a striking temporal correlation between organ complexity and the number of discrete functional modules coordinating morphogenesis. Our analysis elucidates the organization and composition of spatio...

  13. Digital Operation of Microelectronic Circuits Analogous to Protein Hydrogen Bonding Networks

    Directory of Open Access Journals (Sweden)

    Elitsa Gieva

    2012-12-01

    Full Text Available Two hydrogen bonding networks with water molecules and branching residues extracted from β-lactamase protein are investigated and their proton transfer characteristics are studied by creating analogous electrical circuits consisting of block-elements. The block-elements and their proton transfer are described by polynomials that are coded in Matlab and in Verilog-A for use in the Spectre simulator of Cadence IC design system. DC and digital pulse analyses are performed to demonstrate that some circuit outputs behave as repeaters while other - behave as inverters. The results also showed that the HBN circuits might behave as a D-latch and a demultiplexer.

  14. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  15. 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.

  16. Hybrids of the bHLH and bZIP protein motifs display different DNA-binding activities in vivo vs. in vitro.

    Directory of Open Access Journals (Sweden)

    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.

  17. A protein network-guided screen for cell cycle regulators in Drosophila

    Directory of Open Access Journals (Sweden)

    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. IntNetDB v1.0: an integrated protein-protein interaction network database generated by a probabilistic model

    Directory of Open Access Journals (Sweden)

    Han Jing-Dong J

    2006-11-01

    Full Text Available Abstract Background Although protein-protein interaction (PPI networks have been explored by various experimental methods, the maps so built are still limited in coverage and accuracy. To further expand the PPI network and to extract more accurate information from existing maps, studies have been carried out to integrate various types of functional relationship data. A frequently updated database of computationally analyzed potential PPIs to provide biological researchers with rapid and easy access to analyze original data as a biological network is still lacking. Results By applying a probabilistic model, we integrated 27 heterogeneous genomic, proteomic and functional annotation datasets to predict PPI networks in human. In addition to previously studied data types, we show that phenotypic distances and genetic interactions can also be integrated to predict PPIs. We further built an easy-to-use, updatable integrated PPI database, the Integrated Network Database (IntNetDB online, to provide automatic prediction and visualization of PPI network among genes of interest. The networks can be visualized in SVG (Scalable Vector Graphics format for zooming in or out. IntNetDB also provides a tool to extract topologically highly connected network neighborhoods from a specific network for further exploration and research. Using the MCODE (Molecular Complex Detections algorithm, 190 such neighborhoods were detected among all the predicted interactions. The predicted PPIs can also be mapped to worm, fly and mouse interologs. Conclusion IntNetDB includes 180,010 predicted protein-protein interactions among 9,901 human proteins and represents a useful resource for the research community. Our study has increased prediction coverage by five-fold. IntNetDB also provides easy-to-use network visualization and analysis tools that allow biological researchers unfamiliar with computational biology to access and analyze data over the internet. The web interface of

  19. 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.

  20. Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network

    Directory of Open Access Journals (Sweden)

    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

  1. 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

  2. A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein–Protein Interaction Networks: A Case Study of Mycobacterium leprae

    Science.gov (United States)

    Akinola, Richard O.; Mazandu, Gaston K.; Mulder, Nicola J.

    2016-01-01

    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 neighbors 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. PMID:27066064

  3. 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

  4. 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.

  5. 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...

  6. UbiNet: an online resource for exploring the functional associations and regulatory networks of protein ubiquitylation.

    Science.gov (United States)

    Nguyen, Van-Nui; Huang, Kai-Yao; Weng, Julia Tzu-Ya; Lai, K Robert; Lee, Tzong-Yi

    2016-01-01

    Protein ubiquitylation catalyzed by E3 ubiquitin ligases are crucial in the regulation of many cellular processes. Owing to the high throughput of mass spectrometry-based proteomics, a number of methods have been developed for the experimental determination of ubiquitylation sites, leading to a large collection of ubiquitylation data. However, there exist no resources for the exploration of E3-ligase-associated regulatory networks of for ubiquitylated proteins in humans. Therefore, the UbiNet database was developed to provide a full investigation of protein ubiquitylation networks by incorporating experimentally verified E3 ligases, ubiquitylated substrates and protein-protein interactions (PPIs). To date, UbiNet has accumulated 43 948 experimentally verified ubiquitylation sites from 14 692 ubiquitylated proteins of humans. Additionally, we have manually curated 499 E3 ligases as well as two E1 activating and 46 E2 conjugating enzymes. To delineate the regulatory networks among E3 ligases and ubiquitylated proteins, a total of 430 530 PPIs were integrated into UbiNet for the exploration of ubiquitylation networks with an interactive network viewer. A case study demonstrated that UbiNet was able to decipher a scheme for the ubiquitylation of tumor proteins p63 and p73 that is consistent with their functions. Although the essential role of Mdm2 in p53 regulation is well studied, UbiNet revealed that Mdm2 and additional E3 ligases might be implicated in the regulation of other tumor proteins by protein ubiquitylation. Moreover, UbiNet could identify potential substrates for a specific E3 ligase based on PPIs and substrate motifs. With limited knowledge about the mechanisms through which ubiquitylated proteins are regulated by E3 ligases, UbiNet offers users an effective means for conducting preliminary analyses of protein ubiquitylation. The UbiNet database is now freely accessible via http://csb.cse.yzu.edu.tw/UbiNet/ The content is regularly updated with the

  7. Dynamic correlation networks in human peroxisome proliferator-activated receptor-γ nuclear receptor protein.

    Science.gov (United States)

    Fidelak, Jeremy; Ferrer, Silvia; Oberlin, Michael; Moras, Dino; Dejaegere, Annick; Stote, Roland H

    2010-10-01

    Peroxisome proliferator-activated receptor-γ nuclear receptor (PPAR-γ) belongs to the superfamily of nuclear receptor proteins that function as ligand-dependent transcription factors and plays a specific physiological role as a regulator of lipid metabolism. A number of experimental studies have suggested that allostery plays an important role in the functioning of PPAR-γ. Here we use normal-mode analysis of PPAR-γ to characterize a network of dynamically coupled amino acids that link physiologically relevant binding surfaces such as the ligand-dependent activation domain AF-2 with the ligand binding site and the heterodimer interface. Multiple calculations were done in both the presence and absence of the agonist rosiglitazone, and the differences in dynamics were characterized. The global dynamics of the ligand binding domain were affected by the ligand, and in particular, changes to the network of dynamically correlated amino acids were observed with only small changes in conformation. These results suggest that changes in dynamic couplings can be functionally significant with respect to the transmission of allosteric signals. PMID:20496064

  8. 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 ...

  9. 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...

  10. 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...

  11. 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.

  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. 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.

  14. Early kinetic intermediate in the folding of acyl-CoA binding protein detected by fluorescence labeling and ultrarapid mixing

    DEFF Research Database (Denmark)

    Teilum, Kaare; Maki, Kosuke; Kragelund, Birthe B;

    2002-01-01

    Early conformational events during folding of acyl-CoA binding protein (ACBP), an 86-residue alpha-helical protein, were explored by using a continuous-flow mixing apparatus with a dead time of 70 micros to measure changes in intrinsic tryptophan fluorescence and tryptophan-dansyl fluorescence...... energy transfer. Although the folding of ACBP was initially described as a concerted two-state process, the tryptophan fluorescence measurements revealed a previously unresolved phase with a time constant tau = 80 micros, indicating formation of an intermediate with only slightly enhanced fluorescence of...... partially collapsed states with approximately 1/3 of the solvent-accessible surface buried. The findings indicate that ultrafast mixing methods combined with sensitive conformational probes can reveal transient accumulation of intermediate states in proteins with apparent two-state folding mechanisms....

  15. 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

  16. Interaction networks in protein folding via atomic-resolution experiments and long-time-scale molecular dynamics simulations

    DEFF Research Database (Denmark)

    Sborgi, Lorenzo; Verma, Abhinav; Piana, Stefano;

    2015-01-01

    to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue...

  17. Effects of developmental exposure to a Commercial PBDE mixture (DE-71) on protein networks in the rat Cerebellum and Hippocampus

    Science.gov (United States)

    Title (20 words): Effects of developmental exposure to a Commercial PBDE mixture (DE-71) on protein networks in the rat Cerebellum and Hippocampus. Introduction (120 words): Polybrominated diphenyl ethers (PBDE5) possess neurotoxic effects similar to those of PCBs. The cellular a...

  18. Elucidating Common Structural Features of Human Pathogenic Variations Using Large-Scale Atomic-Resolution Protein Networks

    Science.gov (United States)

    Das, Jishnu; Lee, Hao Ran; Sagar, Adithya; Fragoza, Robert; Liang, Jin; Wei, Xiaomu; Wang, Xiujuan; Mort, Matthew; Stenson, Peter D.; Cooper, David N.; Yu, Haiyuan

    2016-01-01

    With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface “hot-spots,” but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface. PMID:24599843

  19. 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.

  20. 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.

  1. 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.

  2. The physics of protein-DNA interaction networks in the control of gene expression

    Science.gov (United States)

    Saiz, Leonor

    2012-05-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Homma, Taihei [Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Shimizu, Miho [Kuroda Chiromorphology Team, ERATO-SORST, JST, Komaba, Meguro-ku, Tokyo 153-8902 (Japan); Kuroda, Reiko, E-mail: ckuroda@mail.ecc.u-tokyo.ac.jp [Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Kuroda Chiromorphology Team, ERATO-SORST, JST, Komaba, Meguro-ku, Tokyo 153-8902 (Japan); Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro-ku, Tokyo 153-8902 (Japan)

    2011-01-07

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

  4. 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)

  5. 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...

  6. 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.

  7. 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.

  8. 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

    2016-01-01

    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. PMID:27161996

  9. Control of Synaptic Connectivity by a Network of Drosophila IgSF Cell Surface Proteins.

    Science.gov (United States)

    Carrillo, Robert A; Özkan, Engin; Menon, Kaushiki P; Nagarkar-Jaiswal, Sonal; Lee, Pei-Tseng; Jeon, Mili; Birnbaum, Michael E; Bellen, Hugo J; Garcia, K Christopher; Zinn, Kai

    2015-12-17

    We have defined a network of interacting Drosophila cell surface proteins in which a 21-member IgSF subfamily, the Dprs, binds to a nine-member subfamily, the DIPs. The structural basis of the Dpr-DIP interaction code appears to be dictated by shape complementarity within the Dpr-DIP binding interface. Each of the six dpr and DIP genes examined here is expressed by a unique subset of larval and pupal neurons. In the neuromuscular system, interactions between Dpr11 and DIP-γ affect presynaptic terminal development, trophic factor responses, and neurotransmission. In the visual system, dpr11 is selectively expressed by R7 photoreceptors that use Rh4 opsin (yR7s). Their primary synaptic targets, Dm8 amacrine neurons, express DIP-γ. In dpr11 or DIP-γ mutants, yR7 terminals extend beyond their normal termination zones in layer M6 of the medulla. DIP-γ is also required for Dm8 survival or differentiation. Our findings suggest that Dpr-DIP interactions are important determinants of synaptic connectivity. PMID:26687361

  10. Optimizations of force-field parameters for protein systems with the secondary-structure stability and instability

    CERN Document Server

    Sakae, Yoshitake

    2013-01-01

    We propose a novel method for refining force-field parameters of protein systems. In this method, the agreement of the secondary-structure stability and instability between the protein conformations obtained by experiments and those obtained by molecular dynamics simulations is used as a criterion for the optimization of force-field parameters. As an example of the applications of the present method, we refined the force-field parameter set of the AMBER ff99SB force field by searching the torsion-energy parameter spaces of $\\psi$ (N-C$^{\\alpha}$-C-N) and $\\zeta$ (C$^{\\beta}$-C$^{\\alpha}$-C-N) of the backbone dihedral angles. We then performed folding simulations of $\\alpha$-helical and $\\beta$-hairpin peptides, using the optimized force field. The results showed that the new force-field parameters gave structures more consistent with the experimental implications than the original AMBER ff99SB force field.

  11. 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; Bateman, Alex; Chatonnet, Arnaud; Chen, Mark Jinan; Craik, David J.; Finn, Robert D.; Gloriam, David; Haft, Daniel H.; Henrissat, Bernard; Holliday, Gemma L.; Isberg, Vignir; Kaas, Quentin; Landsman, David; Lenfant, Nicolas; Manning, Gerard; Nagano, Nozomi; Srinivasan, Narayanaswamy; O’Donovan, Claire; Pruitt, Kim D.; Sowdhamini, Ramanathan; Rawlings, Neil D.; Saier, Milton H.; Sharman, Joanna L.; Spedding, Michael; Tsirigos, Konstantinos D.; Vastermark, Ake; Vriend, Gerrit

    2015-01-01

    During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from ...

  12. 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.

  13. A large iris-like expansion of a mechanosensitive channel protein induced by membrane tension

    Science.gov (United States)

    Betanzos, Monica; Chiang, Chien-Sung; Guy, H. Robert; Sukharev, Sergei

    2002-01-01

    MscL, a bacterial mechanosensitive channel of large conductance, is the first structurally characterized mechanosensor protein. Molecular models of its gating mechanisms are tested here. Disulfide crosslinking shows that M1 transmembrane alpha-helices in MscL of resting Escherichia coli are arranged similarly to those in the crystal structure of MscL from Mycobacterium tuberculosis. An expanded conformation was trapped in osmotically shocked cells by the specific bridging between Cys 20 and Cys 36 of adjacent M1 helices. These bridges stabilized the open channel. Disulfide bonds engineered between the M1 and M2 helices of adjacent subunits (Cys 32-Cys 81) do not prevent channel gating. These findings support gating models in which interactions between M1 and M2 of adjacent subunits remain unaltered while their tilts simultaneously increase. The MscL barrel, therefore, undergoes a large concerted iris-like expansion and flattening when perturbed by membrane tension.

  14. Efficacy of whey protein gel networks as potential viability-enhancing scaffolds for cell immobilization of Lactobacillus rhamnosus GG.

    Science.gov (United States)

    Doherty, S B; Gee, V L; Ross, R P; Stanton, C; Fitzgerald, G F; Brodkorb, A

    2010-03-01

    This study investigated cell immobilization of Lactobacillus rhamnosus GG in three separate protein products: native, denatured and hydrolysed whey protein isolate (WPI). Treatments were assessed for their ability to enhance probiotic survival during storage, heat stress and ex vivo gastric incubation. Spatial distribution of probiotic cells within immobilized treatments was evaluated by atomic force and confocal scanning laser microscopy, while cell viability was enumerated by plate count and flow cytometry (FACS). Microscopic analysis of denatured treatments revealed an oasis of immobilized cells, phase-separated from the surrounding protein matrix; an environmental characteristic analogous to hydrolysed networks. Cell immobilization in hydrolysed and denatured WPI enhanced survival by 6.1+/-0.1 and 5.8+/-0.1 log10 cycles, respectively, following 14 day storage at 37 degrees C and both treatments generated thermal protection at 57 degrees C (7.3+/-0.1 and 6.5+/-0.1 log(10) cfu/ml). Furthermore, denatured WPI enhanced probiotic protection (8.9+/-0.2 log(10) cfu/ml) following 3h gastric incubation at 37 degrees C. In conclusion, hydrolysed or denatured WPI were the most suitable matrices for cell immobilization, while native protein provided the weakest safeguard against thermal and acid stress, thus making it possible to envision whey protein gel networks as protective substrates for cell immobilization applications. PMID:20045713

  15. 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.

  16. 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

    During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from...... 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...

  17. 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

  18. 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...

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    mu-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 drug......-design. 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......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...

  20. 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.

  1. 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...

  2. An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data.

    Science.gov (United States)

    Li, Lishuang; Guo, Rui; Jiang, Zhenchao; Huang, Degen

    2015-07-15

    Protein-Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html. PMID:25864936

  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. 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...

  5. Estimating the Energetic State of Malignant Cells from RNA Transcription and Protein Interaction Network Data

    OpenAIRE

    Rietman, Edward A.; Sachs, Rainer; Hahnfeldt, Philip; Hlatky, Lynn

    2014-01-01

    Gene expression data, or transcription data, are surrogates for actual protein concentrations in the cells. In addition protein-protein interactions are static diagrams of all the protein-protein interactions in the cell. These interactions may consist of covalent bonding or maybe just secondary bonding such as hydrogen bonding. Given these two surrogate data types we show a technique to compute the Gibbs free energy of a cell. We apply this to yeast cell cycle and to cancer.

  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. Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations.

    Science.gov (United States)

    Sborgi, Lorenzo; Verma, Abhinav; Piana, Stefano; Lindorff-Larsen, Kresten; Cerminara, Michele; Santiveri, Clara M; Shaw, David E; de Alba, Eva; Muñoz, Victor

    2015-05-27

    The integration of atomic-resolution experimental and computational methods offers the potential for elucidating key aspects of protein folding that are not revealed by either approach alone. Here, we combine equilibrium NMR measurements of thermal unfolding and long molecular dynamics simulations to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue-residue couplings associated with cooperative folding emerges. Such thermodynamic residue-residue couplings appear to be linked to the order of mechanistically significant events that take place during the folding process. Our results on gpW indicate that the methods employed in this study are likely to prove broadly applicable to the fine analysis of folding mechanisms in fast folding proteins. PMID:25924808

  9. The highly abundant protein Ag-lbp55 from Ascaridia galli represents a novel type of lipid-binding proteins.

    Science.gov (United States)

    Jordanova, Rositsa; Radoslavov, Georgi; Fischer, Peter; Torda, Andrew; Lottspeich, Friedrich; Boteva, Raina; Walter, Rolf D; Bankov, Ilia; Liebau, Eva

    2005-12-16

    Lipid-binding proteins exhibit important functions in lipid transport, cellular signaling, gene transcription, and cytoprotection. Their functional analogues in nematodes are nematode polyprotein allergens/antigens and fatty acid and retinoid-binding proteins. This work describes a novel 55-kDa protein, Ag-lbp55, purified from the parasitic nematode Ascaridia galli. By direct N-terminal sequencing, a partial amino acid sequence was obtained that allowed the design of oligonucleotide primers to obtain the full-length cDNA sequence. Sequence analysis revealed the presence of an N-terminal signal peptide of 25 amino acid residues and a FAR domain at the C terminus. Data base searches showed almost no significant homologies to other described proteins. The secondary structure of Ag-lbp55 was predominantly alpha-helical (65%) as shown by CD spectroscopy. It was found to bind with high affinity fatty acids (caprylic, oleic, and palmitic acid) and their fluorescent analogue dansylaminoundecanic acid. Immunolocalization showed that Ag-lbp55 is a highly abundant protein, mainly distributed in the inner hypodermis and extracellularly in the pseudocoelomatic fluid. A similar staining pattern was observed in other pathogenic nematodes, indicating the existence of similar proteins in these species. PMID:16210327

  10. A P4-ATPase protein interaction network reveals a link between aminophospholipid transport and phosphoinositide metabolism

    NARCIS (Netherlands)

    Puts, C.F.; Lenoir, G.F.; Krijgsveld, J.; WIlliamson, P.L.; Holthuis, J.C.M.

    2009-01-01

    High-throughput analysis of protein-protein interactions can provide unprecedented insight into how cellular processes are integrated at the molecular level. Yet membrane proteins are often overlooked in these studies owing to their hydrophobic nature and low abundance. Here we used a proteomics-bas

  11. SHARPEN-Systematic Hierarchical Algorithms for Rotamers and Proteins on an Extended Network

    KAUST Repository

    Loksha, Ilya V.

    2009-04-30

    Algorithms for discrete optimization of proteins play a central role in recent advances in protein structure prediction and design. We wish to improve the resources available for computational biologists to rapidly prototype such algorithms and to easily scale these algorithms to many processors. To that end, we describe the implementation and use of two new open source resources, citing potential benefits over existing software. We discuss CHOMP, a new object-oriented library for macromolecular optimization, and SHARPEN, a framework for scaling CHOMP scripts to many computers. These tools allow users to develop new algorithms for a variety of applications including protein repacking, protein-protein docking, loop rebuilding, or homology model remediation. Particular care was taken to allow modular energy function design; protein conformations may currently be scored using either the OPLSaa molecular mechanical energy function or an all-atom semiempirical energy function employed by Rosetta. © 2009 Wiley Periodicals, Inc.

  12. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

    OpenAIRE

    Szklarczyk, D.; A. Franceschini; Kuhn, M; Simonovic, M.; Roth, A.; P. Minguez; Doerks, T.; Stark, M; Muller, J.; Bork, P.; Jensen, L. J.; Mering, C.V.

    2010-01-01

    An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein...

  13. A novel heat shock protein alpha 8 (Hspa8) molecular network mediating responses to stress- and ethanol-related behaviors.

    Science.gov (United States)

    Urquhart, Kyle R; Zhao, Yinghong; Baker, Jessica A; Lu, Ye; Yan, Lei; Cook, Melloni N; Jones, Byron C; Hamre, Kristin M; Lu, Lu

    2016-04-01

    Genetic differences mediate individual differences in susceptibility and responses to stress and ethanol, although, the specific molecular pathways that control these responses are not fully understood. Heat shock protein alpha 8 (Hspa8) is a molecular chaperone and member of the heat shock protein family that plays an integral role in the stress response and that has been implicated as an ethanol-responsive gene. Therefore, we assessed its role in mediating responses to stress and ethanol across varying genetic backgrounds. The hippocampus is an important mediator of these responses, and thus, was examined in the BXD family of mice in this study. We conducted bioinformatic analyses to dissect genetic factors modulating Hspa8 expression, identify downstream targets of Hspa8, and examined its role. Hspa8 is trans-regulated by a gene or genes on chromosome 14 and is part of a molecular network that regulates stress- and ethanol-related behaviors. To determine additional components of this network, we identified direct or indirect targets of Hspa8 and show that these genes, as predicted, participate in processes such as protein folding and organic substance metabolic processes. Two phenotypes that map to the Hspa8 locus are anxiety-related and numerous other anxiety- and/or ethanol-related behaviors significantly correlate with Hspa8 expression. To more directly assay this relationship, we examined differences in gene expression following exposure to stress or alcohol and showed treatment-related differential expression of Hspa8 and a subset of the members of its network. Our findings suggest that Hspa8 plays a vital role in genetic differences in responses to stress and ethanol and their interactions. PMID:26780340

  14. Prioritizing Disease Candidate Proteins in Cardiomyopathy-Specific Protein-Protein Interaction Networks Based on “Guilt by Association” Analysis

    OpenAIRE

    Li, Wan; Chen, Lina; He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia

    2013-01-01

    The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on “guilt by association” analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built...

  15. Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

    Directory of Open Access Journals (Sweden)

    Yanxiong Gan

    2015-11-01

    Full Text Available 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.

  16. A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize.

    Directory of Open Access Journals (Sweden)

    Matt eGeisler

    2015-06-01

    Full Text Available Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6,004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize.

  17. A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

    Science.gov (United States)

    Laomettachit, Teeraphan; Chen, Katherine C.; Baumann, William T.

    2016-01-01

    To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804

  18. A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Teeraphan Laomettachit

    Full Text Available To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

  19. A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.

    Science.gov (United States)

    Laomettachit, Teeraphan; Chen, Katherine C; Baumann, William T; Tyson, John J

    2016-01-01

    To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast. PMID:27187804

  20. Prediction of protein structural features by use of artificial neural networks

    DEFF Research Database (Denmark)

    Petersen, Bent

    like for example GenBank are increasing considerably in size and GenBank currently contains more than 132 million sequences. Similar the Protein Data Bank currently contains more than 71,000 experimentally determined structures of nucleic acids, proteins and nucleic acid/protein complexes. There is a...... huge over-representation of DNA sequences when comparing the amount of experimentally verified proteins with the amount of DNA sequences. The academic and industrial research community therefore has to rely on structure predictions instead of waiting for the time consuming experimentally determined...... structure data. This thesis describes the development of two new tools to study such genetic sequence data. NetSurfP was developed to predict the surface accessibility of amino acids in amino acid sequences. Knowledge of the degree of surface exposure of an amino acid is valuable and has been used to...

  1. The Peptide Network between Tetanus Toxin and Human Proteins Associated with Epilepsy.

    Science.gov (United States)

    Lucchese, Guglielmo; Spinosa, Jean Pierre; Kanduc, Darja

    2014-01-01

    Sequence matching analyses show that Clostridium tetani neurotoxin shares numerous pentapeptides (68, including multiple occurrences) with 42 human proteins that, when altered, have been associated with epilepsy. Such a peptide sharing is higher than expected, nonstochastic, and involves tetanus toxin-derived epitopes that have been validated as immunopositive in the human host. Of note, an unexpected high level of peptide matching is found in mitogen-activated protein kinase 10 (MK10), a protein selectively expressed in hippocampal areas. On the whole, the data indicate a potential for cross-reactivity between the neurotoxin and specific epilepsy-associated proteins and may help evaluate the potential risk for epilepsy following immune responses induced by tetanus infection. Moreover, this study may contribute to clarifying the etiopathogenesis of the different types of epilepsy. PMID:24982805

  2. The intermediate filament network protein, vimentin, is required for parvoviral infection

    Energy Technology Data Exchange (ETDEWEB)

    Fay, Nikta; Panté, Nelly, E-mail: pante@zoology.ubc.ca

    2013-09-15

    Intermediate filaments (IFs) have recently been shown to serve novel roles during infection by many viruses. Here we have begun to study the role of IFs during the early steps of infection by the parvovirus minute virus of mice (MVM). We found that during early infection with MVM, after endosomal escape, the vimentin IF network was considerably altered, yielding collapsed immunofluorescence staining near the nuclear periphery. Furthermore, we found that vimentin plays an important role in the life cycle of MVM. The number of cells, which successfully replicated MVM, was reduced in infected cells in which the vimentin network was genetically or pharmacologically modified; viral endocytosis, however, remained unaltered. Perinuclear accumulation of MVM-containing vesicles was reduced in cells lacking vimentin. Our data suggests that vimentin is required for the MVM life cycle, presenting possibly a dual role: (1) following MVM escape from endosomes and (2) during endosomal trafficking of MVM. - Highlights: • MVM infection changes the distribution of the vimentin network to perinuclear regions. • Disrupting the vimentin network with acrylamide decreases MVM replication. • MVM replication is significantly reduced in vimentin-null cells. • Distribution of MVM-containing vesicles is affected in MVM infected vimentin-null cells.

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

    OpenAIRE

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

    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...

  4. The Peptide Network between Tetanus Toxin and Human Proteins Associated with Epilepsy

    OpenAIRE

    Guglielmo Lucchese; Jean Pierre Spinosa; Darja Kanduc

    2014-01-01

    Sequence matching analyses show that Clostridium tetani neurotoxin shares numerous pentapeptides (68, including multiple occurrences) with 42 human proteins that, when altered, have been associated with epilepsy. Such a peptide sharing is higher than expected, nonstochastic, and involves tetanus toxin-derived epitopes that have been validated as immunopositive in the human host. Of note, an unexpected high level of peptide matching is found in mitogen-activated protein kinase 10 (MK10), a pro...

  5. Prediction of yeast protein–protein interaction network: insights from the Gene Ontology and annotations

    OpenAIRE

    Wu, Xiaomei; Zhu, Lei; Guo, Jie; Zhang, Da-Yong; Lin, Kui

    2006-01-01

    A map of protein–protein interactions provides valuable insight into the cellular function and machinery of a proteome. By measuring the similarity between two Gene Ontology (GO) terms with a relative specificity semantic relation, here, we proposed a new method of reconstructing a yeast protein–protein interaction map that is solely based on the GO annotations. The method was validated using high-quality interaction datasets for its effectiveness. Based on a Z-score analysis, a positive data...

  6. Pathosystems Biology: Computational Prediction and Analysis of Host-Pathogen Protein Interaction Networks

    OpenAIRE

    Dyer, Matthew David

    2008-01-01

    An important aspect of systems biology is the elucidation of the protein-protein interactions (PPIs) that control important biological processes within a cell and between organisms. In particular, at the cellular and molecular level, interactions between a pathogen and its host play a vital role in initiating infection and a successful pathogenesis. Despite recent successes in the advancement of the systems biology of model organisms to understand complex diseases, the analysis of infectious ...

  7. Novel interactions of CLN5 support molecular networking between Neuronal Ceroid Lipofuscinosis proteins

    Directory of Open Access Journals (Sweden)

    Jalanko Anu

    2009-11-01

    Full Text Available Abstract Background Neuronal ceroid lipofuscinoses (NCLs comprise at least eight genetically characterized neurodegenerative disorders of childhood. Despite of genetic heterogeneity, the high similarity of clinical symptoms and pathology of different NCL disorders suggest cooperation between different NCL proteins and common mechanisms of pathogenesis. Here, we have studied molecular interactions between NCL proteins, concentrating specifically on the interactions of CLN5, the protein underlying the Finnish variant late infantile form of NCL (vLINCLFin. Results We found that CLN5 interacts with several other NCL proteins namely, CLN1/PPT1, CLN2/TPP1, CLN3, CLN6 and CLN8. Furthermore, analysis of the intracellular targeting of CLN5 together with the interacting NCL proteins revealed that over-expression of PPT1 can facilitate the lysosomal transport of mutated CLN5FinMajor, normally residing in the ER and in the Golgi complex. The significance of the novel interaction between CLN5 and PPT1 was further supported by the finding that CLN5 was also able to bind the F1-ATPase, earlier shown to interact with PPT1. Conclusion We have described novel interactions between CLN5 and several NCL proteins, suggesting a modifying role for these proteins in the pathogenesis of individual NCL disorders. Among these novel interactions, binding of CLN5 to CLN1/PPT1 is suggested to be the most significant one, since over-expression of PPT1 was shown to influence on the intracellular trafficking of mutated CLN5, and they were shown to share a binding partner outside the NCL protein spectrum.

  8. Uncovering the Protein Lysine and Arginine Methylation Network in Arabidopsis Chloroplasts

    OpenAIRE

    Alban, Claude; Tardif, Marianne; Mininno, Morgane; Brugiere, Sabine; Gilgen, Annabelle; Ma, Sheng; Mazzoleni, Meryl; Gigarel, Oceane; Martin-Laffon, Jacqueline; Ferro, Myriam

    2014-01-01

    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 id...

  9. PROTEIN QUALITY CONTROL IN BACTERIAL CELLS: INTEGRATED NETWORKS OF CHAPERONES AND ATP-DEPENDENT PROTEASES.

    Energy Technology Data Exchange (ETDEWEB)

    FLANAGAN,J.M.BEWLEY,M.C.

    2002-10-01

    It is generally accepted that the information necessary to specify the native, functional, three-dimensional structure of a protein is encoded entirely within its amino acid sequence; however, efficient reversible folding and unfolding is observed only with a subset of small single-domain proteins. Refolding experiments often lead to the formation of kinetically-trapped, misfolded species that aggregate, even in dilute solution. In the cellular environment, the barriers to efficient protein folding and maintenance of native structure are even larger due to the nature of this process. First, nascent polypeptides must fold in an extremely crowded environment where the concentration of macromolecules approaches 300-400 mg/mL and on average, each ribosome is within its own diameter of another ribosome (1-3). These conditions of severe molecular crowding, coupled with high concentrations of nascent polypeptide chains, favor nonspecific aggregation over productive folding (3). Second, folding of newly-translated polypeptides occurs in the context of their vehtorial synthesis process. Amino acids are added to a growing nascent chain at the rate of {approx}5 residues per set, which means that for a 300 residue protein its N-terminus will be exposed to the cytosol {approx}1 min before its C-terminus and be free to begin the folding process. However, because protein folding is highly cooperative, the nascent polypeptide cannot reach its native state until a complete folding domain (50-250 residues) has emerged from the ribosome. Thus, for a single-domain protein, the final steps in ffolding are only completed post-translationally since {approx}40 residues of a nascent chain are sequestered within the exit channel of the ribosome and are not available for folding (4). A direct consequence of this limitation in cellular folding is that during translation incomplete domains will exist in partially-folded states that tend to expose hydrophobic residues that are prone to

  10. PROTEIN QUALITY CONTROL IN BACTERIAL CELLS: INTEGRATED NETWORKS OF CHAPERONES AND ATP-DEPENDENT PROTEASES.

    Energy Technology Data Exchange (ETDEWEB)

    FLANAGAN,J.M.; BEWLEY,M.C.

    2001-12-03

    It is generally accepted that the information necessary to specify the native, functional, three-dimensional structure of a protein is encoded entirely within its amino acid sequence; however, efficient reversible folding and unfolding is observed only with a subset of small single-domain proteins. Refolding experiments often lead to the formation of kinetically-trapped, misfolded species that aggregate, even in dilute solution. In the cellular environment, the barriers to efficient protein folding and maintenance of native structure are even larger due to the nature of this process. First, nascent polypeptides must fold in an extremely crowded environment where the concentration of macromolecules approaches 300-400 mg/mL and on average, each ribosome is within its own diameter of another ribosome (1-3). These conditions of severe molecular crowding, coupled with high concentrations of nascent polypeptide chains, favor nonspecific aggregation over productive folding (3). Second, folding of newly-translated polypeptides occurs in the context of their vehtorial synthesis process. Amino acids are added to a growing nascent chain at the rate of -5 residues per set, which means that for a 300 residue protein its N-terminus will be exposed to the cytosol {approx}1 min before its C-terminus and be free to begin the folding process. However, because protein folding is highly cooperative, the nascent polypeptide cannot reach its native state until a complete folding domain (50-250 residues) has emerged from the ribosome. Thus, for a single-domain protein, the final steps in folding are only completed post-translationally since {approx}40 residues of a nascent chain are sequestered within the exit channel of the ribosome and are not available for folding (4). A direct consequence of this limitation in cellular folding is that during translation incomplete domains will exist in partially-folded states that tend to expose hydrophobic residues that are prone to aggregation and

  11. PROTEIN QUALITY CONTROL IN BACTERIAL CELLS: INTEGRATED NETWORKS OF CHAPERONES AND ATP-DEPENDENT PROTEASES

    International Nuclear Information System (INIS)

    It is generally accepted that the information necessary to specify the native, functional, three-dimensional structure of a protein is encoded entirely within its amino acid sequence; however, efficient reversible folding and unfolding is observed only with a subset of small single-domain proteins. Refolding experiments often lead to the formation of kinetically-trapped, misfolded species that aggregate, even in dilute solution. In the cellular environment, the barriers to efficient protein folding and maintenance of native structure are even larger due to the nature of this process. First, nascent polypeptides must fold in an extremely crowded environment where the concentration of macromolecules approaches 300-400 mg/mL and on average, each ribosome is within its own diameter of another ribosome (1-3). These conditions of severe molecular crowding, coupled with high concentrations of nascent polypeptide chains, favor nonspecific aggregation over productive folding (3). Second, folding of newly-translated polypeptides occurs in the context of their vehtorial synthesis process. Amino acids are added to a growing nascent chain at the rate of -5 residues per set, which means that for a 300 residue protein its N-terminus will be exposed to the cytosol(approx)1 min before its C-terminus and be free to begin the folding process. However, because protein folding is highly cooperative, the nascent polypeptide cannot reach its native state until a complete folding domain (50-250 residues) has emerged from the ribosome. Thus, for a single-domain protein, the final steps in folding are only completed post-translationally since(approx)40 residues of a nascent chain are sequestered within the exit channel of the ribosome and are not available for folding (4). A direct consequence of this limitation in cellular folding is that during translation incomplete domains will exist in partially-folded states that tend to expose hydrophobic residues that are prone to aggregation and

  12. Manipulating and Visualizing Proteins

    Energy Technology Data Exchange (ETDEWEB)

    Simon, Horst D.

    2003-12-05

    ProteinShop Gives Researchers a Hands-On Tool for Manipulating, Visualizing Protein Structures. The Human Genome Project and other biological research efforts are creating an avalanche of new data about the chemical makeup and genetic codes of living organisms. But in order to make sense of this raw data, researchers need software tools which let them explore and model data in a more intuitive fashion. With this in mind, researchers at Lawrence Berkeley National Laboratory and the University of California, Davis, have developed ProteinShop, a visualization and modeling program which allows researchers to manipulate protein structures with pinpoint control, guided in large part by their own biological and experimental instincts. Biologists have spent the last half century trying to unravel the ''protein folding problem,'' which refers to the way chains of amino acids physically fold themselves into three-dimensional proteins. This final shape, which resembles a crumpled ribbon or piece of origami, is what determines how the protein functions and translates genetic information. Understanding and modeling this geometrically complex formation is no easy matter. ProteinShop takes a given sequence of amino acids and uses visualization guides to help generate predictions about the secondary structures, identifying alpha helices and flat beta strands, and the coil regions that bind them. Once secondary structures are in place, researchers can twist and turn these pre-configurations until they come up with a number of possible tertiary structure conformations. In turn, these are fed into a computationally intensive optimization procedure that tries to find the final, three-dimensional protein structure. Most importantly, ProteinShop allows users to add human knowledge and intuition to the protein structure prediction process, thus bypassing bad configurations that would otherwise be fruitless for optimization. This saves compute cycles and accelerates

  13. Functional protein network activation mapping reveals new potential molecular drug targets for poor prognosis pediatric BCP-ALL.

    Directory of Open Access Journals (Sweden)

    Benedetta Accordi

    Full Text Available BACKGROUND: In spite of leukemia therapy improvements obtained over the last decades, therapy is not yet effective in all cases. Current approaches in Acute Lymphoblastic Leukemia (ALL research focus on identifying new molecular targets to improve outcome for patients with a dismal prognosis. In this light phosphoproteomics seems to hold great promise for the identification of proteins suitable for targeted therapy. METHODOLOGY/PRINCIPAL FINDINGS: We employed Reverse Phase Protein Microarrays to identify aberrantly activated proteins in 118 pediatric B-cell precursor (BCP-ALL patients. Signal transduction pathways were assayed for activation/expression status of 92 key signalling proteins. We observed an increased activation/expression of several pathways involved in cell proliferation in poor clinical prognosis patients. MLL-rearranged tumours revealed BCL-2 hyperphosphorylation through AMPK activation, which indicates that AMPK could provide a functional role in inhibiting apoptosis in MLL-rearranged patients, and could be considered as a new potential therapeutic target. Second, in patients with poor clinical response to prednisone we observed the up-modulation of LCK activity with respect to patients with good response. This tyrosine-kinase can be down-modulated with clinically used inhibitors, thus modulating LCK activity could be considered for further studies as a new additional therapy for prednisone-resistant patients. Further we also found an association between high levels of CYCLIN E and relapse incidence. Moreover, CYCLIN E is more expressed in early relapsed patients, who usually show an unfavourable prognosis. CONCLUSIONS/SIGNIFICANCE: We conclude that functional protein pathway activation mapping revealed specific deranged signalling networks in BCP-ALL that could be potentially modulated to produce a better clinical outcome for patients resistant to standard-of-care therapies.

  14. Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates.

    Directory of Open Access Journals (Sweden)

    Daniel Boloc

    Full Text Available Retinitis pigmentosa (RP is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA. The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.We have built an RP-specific network (RPGeNet by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.

  15. Protein distance constraints predicted by neural networks and probability density functions

    DEFF Research Database (Denmark)

    Lund, Ole; Frimand, Kenneth; Gorodkin, Jan; Bohr, Henrik; Bohr, Jakob; Hansen, Jan; Brunak, Søren

    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 taki...... profiles. A threading method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/...

  16. Community Structure Detection for Overlapping Modules through Mathematical Programming in Protein Interaction Networks

    OpenAIRE

    Bennett, L; Kittas, A.; Liu, S; Papageorgiou, L. G.; Tsoka, S.

    2014-01-01

    Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non lin...

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

    OpenAIRE

    Garbutt, Cassandra C.; Bangalore, Purushotham V.; Pegah eKannar; Shahid eMukhtar

    2014-01-01

    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 biolo...

  18. Getting to the edge: protein dynamical networks as a new frontier in plant–microbe interactions

    OpenAIRE

    Garbutt, Cassandra C.; Bangalore, Purushotham V.; Kannar, Pegah; Mukhtar, M. S.

    2014-01-01

    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 bi...

  19. Folding dynamics of a family of beta-sheet proteins

    Science.gov (United States)

    Rousseau, Denis

    2008-03-01

    Fatty acid binding proteins (FABP) consist of ten anti-parallel beta strands and two small alpha helices. The beta strands are arranged into two nearly orthogonal five-strand beta sheets that surround the interior cavity, which binds unsaturated long-chain fatty acids. In the brain isoform (BFABP), these are very important for the development of the central nervous system and neuron differentiation. Furthermore, BFABP is implicated in the pathogenesis of a variety of human diseases including cancer and neuronal degenerative disorders. In this work, site-directed spin labeling combined with EPR techniques have been used to study the folding mechanism of BFABP. In the first series of studies, we labeled the two Cys residues at position 5 and 80 in the wild type protein with an EPR spin marker; in addition, two singly labeled mutants at positions 5 and 80 in the C80A and C5A mutants, respectively, were also produced and used as controls. The changes in the distances between the two residues were examined by a pulsed EPR method, DEER (Double Electron Electron Resonance), as a function of guanidinium hydrochloride concentration. The results were compared with those from CW EPR, circular dichroism and fluorescence measurements, which provide the information regarding sidechain mobility, secondary structure and tertiary structure, respectively. The results will be discussed in the context of the folding mechanism of the family of fatty acid binding proteins.

  20. Ner protein of phage Mu: Assignments using {sup 13}C/{sup 15}N-labeled protein

    Energy Technology Data Exchange (ETDEWEB)

    Strzelecka, T.; Gronenborn, A.M.; Clore, G.M. [National Institutes of Health, Bethesda, MD (United States)

    1994-12-01

    The Ner protein is a small (74-amino acid) DNA-binding protein that regulates a switch between the lysogenic and lytic stages of phage Mu. It inhibits expression of the C repressor gene and down-regulates its own expression. Two-dimensional NMR experiments on uniformly {sup 15}N-labeled protein provided most of the backbone and some of the sidechain proton assignments. The secondary structure determination using two-dimensional NOESY experiments showed that Ner consists of five {alpha}-helices. However, because most of the sidechain protons could not be assigned, the full structure was not determined. Using uniformly {sup 13}C/{sup 15}N-labeled Ner and a set of three-dimensional experiments, we were able to assign all of the backbone and 98% of the sidechain protons. In particular, the CBCANH and CBCA(CO)NH experiments were used to sequentially assign the C{alpha} and C{beta} resonances; the HCCH-CTOCSY and HCCH-COSY were used to assign sidechain carbon and proton resonances.

  1. Determinants of rodent longevity in the chaperone-protein degradation network.

    Science.gov (United States)

    Rodriguez, Karl A; Valentine, Joseph M; Kramer, David A; Gelfond, Jonathan A; Kristan, Deborah M; Nevo, Eviatar; Buffenstein, Rochelle

    2016-05-01

    Proteostasis is an integral component of healthy aging, ensuring maintenance of protein structural and functional integrity with concomitant impact upon health span and longevity. In most metazoans, increasing age is accompanied by a decline in protein quality control resulting in the accrual of damaged, self-aggregating cytotoxic proteins. A notable exception to this trend is observed in the longest-lived rodent, the naked mole-rat (NMR, Heterocephalus glaber) which maintains proteostasis and proteasome-mediated degradation and autophagy during aging. We hypothesized that high levels of the proteolytic degradation may enable better maintenance of proteostasis during aging contributing to enhanced species maximum lifespan potential (MLSP). We test this by examining proteasome activity, proteasome-related HSPs, the heat-shock factor 1 (HSF1) transcription factor, and several markers of autophagy in the liver and quadriceps muscles of eight rodent species with divergent MLSP. All subterranean-dwelling species had higher levels of proteasome activity and autophagy, possibly linked to having to dig in soils rich in heavy metals and where underground atmospheres have reduced oxygen availability. Even after correcting for phylogenetic relatedness, a significant (p machinery and maintenance of protein quality play a pivotal role in species longevity among rodents. PMID:26894765

  2. An elastic-network based local molecular field analysis of zinc-finger proteins

    CERN Document Server

    Dixit, Purushottam D

    2011-01-01

    We study two designed and one natural zinc-finger peptide each with the Cys2His2 (CCHH) type of metal binding motif. In the approach we have developed, we describe the role of the protein and solvent outside the Zn(II)-CCHH metal-residue cluster by a molecular field represented by generalized harmonic restraints. The strength of the field is adjusted to reproduce the binding energy distribution of the metal with the cluster obtained in a reference all-atom simulation with empirical potentials. The quadratic field allows us to investigate analytically the protein restraints on the binding site in terms of its eigenmodes. Examining these eigenmodes suggests, consistent with experimental observations, the importance of the first histidine (H) in the CCHH cluster in metal binding. Further, the eigenvalues corresponding to these modes also indicate that the designed proteins form a tighter complex with the metal. We find that the bulk protein and solvent response tends to destabilize metal-binding, emphasizing tha...

  3. Comparison of Modules of Wild Type and Mutant Huntingtin and TP53 Protein Interaction Networks: Implications in Biological Processes and Functions

    Science.gov (United States)

    Basu, Mahashweta; Bhattacharyya, Nitai P.; Mohanty, Pradeep K.

    2013-01-01

    Disease-causing mutations usually change the interacting partners of mutant proteins. In this article, we propose that the biological consequences of mutation are directly related to the alteration of corresponding protein protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes Huntington's disease (HD) and mutations to TP53 which is associated with different cancers are studied as two example cases. We construct the PPIN of wild type and mutant proteins separately and identify the structural modules of each of the networks. The functional role of these modules are then assessed by Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find that a large number of significantly enriched () GO terms in mutant PPIN were absent in the wild type PPIN indicating the gain of BPs due to mutation. Similarly some of the GO terms enriched in wild type PPIN cease to exist in the modules of mutant PPIN, representing the loss. GO terms common in modules of mutant and wild type networks indicate both loss and gain of BPs. We further assign relevant biological function(s) to each module by classifying the enriched GO terms associated with it. It turns out that most of these biological functions in HTT networks are already known to be altered in HD and those of TP53 networks are altered in cancers. We argue that gain of BPs, and the corresponding biological functions, are due to new interacting partners acquired by mutant proteins. The methodology we adopt here could be applied to genetic diseases where mutations alter the ability of the protein to interact with other proteins. PMID:23741403

  4. Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

    DEFF Research Database (Denmark)

    Helles, Glennie; Fonseca, Rasmus

    2009-01-01

    residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary...... local context dependent dihedral angle propensities in coil-regions. This predicted distribution can potentially improve tertiary structure prediction methods that are based on sampling the backbone dihedral angles of individual amino acids. The predicted distribution may also help predict local...

  5. Heterotrimeric G proteins as emerging targets for network based therapy in cancer: End of a long futile campaign striking heads of a Hydra.

    Science.gov (United States)

    Ghosh, Pradipta

    2015-07-01

    Most common diseases, e.g., cancer are driven by not one, but multiple cell surface receptors that trigger and sustain a pathologic signaling network. The largest fraction of therapeutic agents that target individual receptors/pathways eventually fail due to the emergence of compensatory mechanisms that reestablish the pathologic network. Recently, a rapidly emerging paradigm has revealed GIV/Girdin as a central platform for receptor cross-talk which integrates signals downstream of a myriad of cell surface receptors, and modulates several key pathways within downstream signaling network, all via non-canonical activation of trimeric G proteins. Unlike canonical signal transduction via G proteins, which is spatially and temporally restricted, the temporal and spatial features of non-canonical activation of G protein via GIV is unusually unrestricted. Consequently, the GIV●G protein interface serves as a central hub allowing for control over several pathways within the pathologic signaling network, all at once. The relevance of this new paradigm in cancer and other disease states and the pros and cons of targeting the GIV●G protein interface are discussed. PMID:26224586

  6. Structural alterations in rat liver proteins due to streptozotocin-induced diabetes and the recovery effect of selenium: Fourier transform infrared microspectroscopy and neural network study

    Science.gov (United States)

    Bozkurt, Ozlem; Haman Bayari, Sevgi; Severcan, Mete; Krafft, Christoph; Popp, Jürgen; Severcan, Feride

    2012-07-01

    The relation between protein structural alterations and tissue dysfunction is a major concern as protein fibrillation and/or aggregation due to structural alterations has been reported in many disease states. In the current study, Fourier transform infrared microspectroscopic imaging has been used to investigate diabetes-induced changes on protein secondary structure and macromolecular content in streptozotocin-induced diabetic rat liver. Protein secondary structural alterations were predicted using neural network approach utilizing the amide I region. Moreover, the role of selenium in the recovery of diabetes-induced alterations on macromolecular content and protein secondary structure was also studied. The results revealed that diabetes induced a decrease in lipid to protein and glycogen to protein ratios in diabetic livers. Significant alterations in protein secondary structure were observed with a decrease in α-helical and an increase in β-sheet content. Both doses of selenium restored diabetes-induced changes in lipid to protein and glycogen to protein ratios. However, low-dose selenium supplementation was not sufficient to recover the effects of diabetes on protein secondary structure, while a higher dose of selenium fully restored diabetes-induced alterations in protein structure.

  7. Molecular energy dissipation in nanoscale networks of dentin matrix protein 1 is strongly dependent on ion valence

    International Nuclear Information System (INIS)

    The fracture resistance of biomineralized tissues such as bone, dentin, and abalone is greatly enhanced through the nanoscale interactions of stiff inorganic mineral components with soft organic adhesive components. A proper understanding of the interactions that occur within the organic component, and between the organic and inorganic components, is therefore critical for a complete understanding of the mechanics of these tissues. In this paper, we use atomic force microscope (AFM) force spectroscopy and dynamic force spectroscopy to explore the effect of ionic interactions within a nanoscale system consisting of networks of dentin matrix protein 1 (DMP1) (a component of both bone and dentin organic matrix), a mica surface and an AFM tip. We find that DMP1 is capable of dissipating large amounts of energy through an ion-mediated mechanism, and that the effectiveness increases with increasing ion valence

  8. Identification of recognition sites for myc/max/mxd network proteins by a whole human chromosome 19 selection strategy.

    Science.gov (United States)

    Akopov, S B; Chernov, I P; Wahlström, T; Kostina, M B; Klein, G; Henriksson, M; Nikolaev, L G

    2008-11-01

    In this study, we have identified 20 human sequences containing Myc network binding sites in a library from the whole human chromosome 19. We demonstrated binding of the Max protein to these sequences both in vitro and in vivo. The majority of the identified sequences contained one or several CACGTG or CATGTG E-boxes. Several of these sites were located within introns or in their vicinity and the corresponding genes were found to be up- or down-regulated in differentiating HL-60 cells. Our data show the proof of principle for using this strategy in identification of Max target genes, and this method can also be applied for other transcription factors. PMID:19120031

  9. Isolation, characterization, and bioinformatic analysis of calmodulin-binding protein cmbB reveals a novel tandem IP22 repeat common to many Dictyostelium and Mimivirus proteins.

    Science.gov (United States)

    O'Day, Danton H; Suhre, Karsten; Myre, Michael A; Chatterjee-Chakraborty, Munmun; Chavez, Sara E

    2006-08-01

    A novel calmodulin-binding protein cmbB from Dictyostelium discoideum is encoded in a single gene. Northern analysis reveals two cmbB transcripts first detectable at 4 h during multicellular development. Western blotting detects an approximately 46.6 kDa protein. Sequence analysis and calmodulin-agarose binding studies identified a "classic" calcium-dependent calmodulin-binding domain (179IPKSLRSLFLGKGYNQPLEF198) but structural analyses suggest binding may not involve classic alpha-helical calmodulin-binding. The cmbB protein is comprised of tandem repeats of a newly identified IP22 motif ([I,L]Pxxhxxhxhxxxhxxxhxxxx; where h = any hydrophobic amino acid) that is highly conserved and a more precise representation of the FNIP repeat. At least eight Acanthamoeba polyphaga Mimivirus proteins and over 100 Dictyostelium proteins contain tandem arrays of the IP22 motif and its variants. cmbB also shares structural homology to YopM, from the plague bacterium Yersenia pestis. PMID:16777069

  10. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases

    DEFF Research Database (Denmark)

    Tan, Chris Soon Heng; Bodenmiller, Bernd; Pasculescu, Adrian;

    2009-01-01

    Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar...... experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over...... approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly...

  11. Did homeodomain proteins duplicate before the origin of angiosperms, fungi, and metazoa?

    Science.gov (United States)

    Bharathan, G; Janssen, B J; Kellogg, E A; Sinha, N

    1997-12-01

    Homeodomain proteins are transcription factors that play a critical role in early development in eukaryotes. These proteins previously have been classified into numerous subgroups whose phylogenetic relationships are unclear. Our phylogenetic analysis of representative eukaryotic sequences suggests that there are two major groups of homeodomain proteins, each containing sequences from angiosperms, metazoa, and fungi. This result, based on parsimony and neighbor-joining analyses of primary amino acid sequences, was supported by two additional features of the proteins. The two protein groups are distinguished by an insertion/deletion in the homeodomain, between helices I and II. In addition, an amphipathic alpha-helical secondary structure in the region N terminal of the homeodomain is shared by angiosperm and metazoan sequences in one group. These results support the hypothesis that there was at least one duplication of homeobox genes before the origin of angiosperms, fungi, and metazoa. This duplication, in turn, suggests that these proteins had diverse functions early in the evolution of eukaryotes. The shared secondary structure in angiosperm and metazoan sequences points to an ancient conserved functional domain. PMID:9391098

  12. Did homeodomain proteins duplicate before the origin of angiosperms, fungi, and metazoa?

    Science.gov (United States)

    Bharathan, Geeta; Janssen, Bart-Jan; Kellogg, Elizabeth A.; Sinha, Neelima

    1997-01-01

    Homeodomain proteins are transcription factors that play a critical role in early development in eukaryotes. These proteins previously have been classified into numerous subgroups whose phylogenetic relationships are unclear. Our phylogenetic analysis of representative eukaryotic sequences suggests that there are two major groups of homeodomain proteins, each containing sequences from angiosperms, metazoa, and fungi. This result, based on parsimony and neighbor-joining analyses of primary amino acid sequences, was supported by two additional features of the proteins. The two protein groups are distinguished by an insertion/deletion in the homeodomain, between helices I and II. In addition, an amphipathic alpha-helical secondary structure in the region N terminal of the homeodomain is shared by angiosperm and metazoan sequences in one group. These results support the hypothesis that there was at least one duplication of homeobox genes before the origin of angiosperms, fungi, and metazoa. This duplication, in turn, suggests that these proteins had diverse functions early in the evolution of eukaryotes. The shared secondary structure in angiosperm and metazoan sequences points to an ancient conserved functional domain. PMID:9391098

  13. Structure-function studies of the magnetite-biomineralizing magnetosome-associated protein MamC.

    Science.gov (United States)

    Nudelman, Hila; Valverde-Tercedor, Carmen; Kolusheva, Sofiya; Perez Gonzalez, Teresa; Widdrat, Marc; Grimberg, Noam; Levi, Hilla; Nelkenbaum, Or; Davidov, Geula; Faivre, Damien; Jimenez-Lopez, Concepcion; Zarivach, Raz

    2016-06-01

    Magnetotactic bacteria are Gram-negative bacteria that navigate along geomagnetic fields using the magnetosome, an organelle that consists of a membrane-enveloped magnetic nanoparticle. Magnetite formation and its properties are controlled by a specific set of proteins. MamC is a small magnetosome-membrane protein that is known to be active in iron biomineralization but its mechanism has yet to be clarified. Here, we studied the relationship between the MamC magnetite-interaction loop (MIL) structure and its magnetite interaction using an inert biomineralization protein-MamC chimera. Our determined structure shows an alpha-helical fold for MamC-MIL with highly charged surfaces. Additionally, the MamC-MIL induces the formation of larger magnetite crystals compared to protein-free and inert biomineralization protein control experiments. We suggest that the connection between the MamC-MIL structure and the protein's charged surfaces is crucial for magnetite binding and thus for the size control of the magnetite nanoparticles. PMID:26970040

  14. Unraveling the Protein Network of Tomato Fruit in Response to Necrotrophic Phytopathogenic Rhizopus nigricans

    OpenAIRE

    Pan, Xiaoqi; Zhu, Benzhong; Luo, Yunbo; Fu, Daqi

    2013-01-01

    Plants are endowed with a sophisticated defense mechanism that gives signals to plant cells about the immediate danger from surroundings and protects them from pathogen invasion. In the search for the particular proteins involved in fruit defense responses, we report here a comparative analysis of tomato fruit (Solanum lycopersicum cv. Ailsa Craig) infected by Rhizopus nigricans Ehrenb, which is a significant contributor to postharvest rot disease in fresh tomato fruits. In total, four hundre...

  15. A SCARECROW-RETINOBLASTOMA protein network controls protective quiescence in the Arabidopsis root stem cell organizer.

    OpenAIRE

    Alfredo Cruz-Ramírez; Sara Díaz-Triviño; Guy Wachsman; Yujuan Du; Mario Arteága-Vázquez; Hongtao Zhang; Rene Benjamins; Ikram Blilou; Neef, Anne B.; Vicki Chandler; Ben Scheres

    2013-01-01

    Author Summary In the plant Arabidposis thaliana, root meristems (in the growing tip of the root) contain slowly dividing cells that act as an organizing center for the root stem cells that surround them. This centre is called the quiescent centre (QC). In this study, we show that the slow rate of division in the QC is regulated by the interaction between two proteins: Retinoblastoma homolog (RBR) and SCARECROW (SCR), a transcription factor that controls stem cell maintenance. RBR and SCR reg...

  16. Structure, Function, Self-Assembly and Origin of Simple Membrane Proteins

    Science.gov (United States)

    Pohorille, Andrew

    2003-01-01

    Integral membrane proteins perform such essential cellular functions as transport of ions, nutrients and waste products across cell walls, transduction of environmental signals, regulation of cell fusion, recognition of other cells, energy capture and its conversion into high-energy compounds. In fact, 30-40% of genes in modem organisms codes for membrane proteins. Although contemporary membrane proteins or their functional assemblies can be quite complex, their transmembrane fragments are usually remarkably simple. The most common structural motif for these fragments is a bundle of alpha-helices, but occasionally it could be a beta-barrel. In a series of molecular dynamics computer simulations we investigated self-organizing properties of simple membrane proteins based on these structural motifs. Specifically, we studied folding and insertion into membranes of short, nonpolar or amphiphatic peptides. We also investigated glycophorin A, a peptide that forms sequence-specific dimers, and a transmembrane aggregate of four identical alpha-helices that forms an efficient and selective voltage-gated proton channel was investigated. Many peptides are attracted to water-membrane interfaces. Once at the interface, nonpolar peptides spontaneously fold to a-helices. Whenever the sequence permits, peptides that contain both polar and nonpolar amino also adopt helical structures, in which polar and nonpolar amino acid side chains are immersed in water and membrane, respectively. Specific identity of side chains is less important. Helical peptides at the interface could insert into the membrane and adopt a transmembrane conformation. However, insertion of a single helix is unfavorable because polar groups in the peptide become completely dehydrated upon insertion. The unfavorable free energy of insertion can be regained by spontaneous association of peptides in the membrane. The first step in this process is the formation of dimers, although the most common are aggregates of 4

  17. The Potato virus X TGBp3 protein associates with the ER network for virus cell-to-cell movement

    Science.gov (United States)

    Krishnamurthy, Konduru; Heppler, Marty; Mitra, Ruchira; Blancaflor, Elison; Payton, Mark; Nelson, Richard S.; Verchot-Lubicz, Jeanmarie

    2003-01-01

    Potato virus X (PVX) TGBp3 is required for virus cell-to-cell movement. Cell-to-cell movement of TGBp3 was studied using biolistic bombardment of plasmids expressing GFP:TGBp3. TGBp3 moves between cells in Nicotiana benthamiana, but requires TGBp1 to move in N. tabacum leaves. In tobacco leaves GFP:TGBp3 accumulated in a pattern resembling the endoplasmic reticulum (ER). To determine if the ER network is important for GFP:TGBp3 and for PVX cell-to-cell movement, a single mutation inhibiting membrane binding of TGBp3 was introduced into GFP:TGBp3 and into PVX. This mutation disrupted movement of GFP:TGBp3 and PVX. Brefeldin A, which disrupts the ER network, also inhibited GFP:TGBp3 movement in both Nicotiana species. Two deletion mutations, that do not affect membrane binding, hindered GFP:TGBp3 and PVX cell-to-cell movement. Plasmids expressing GFP:TGBp2 and GFP:TGBp3 were bombarded to several other PVX hosts and neither protein moved between adjacent cells. In most hosts, TGBp2 or TGBp3 cannot move cell-to-cell.

  18. Mapping the Hydrogen Bond Networks in the Catalytic Subunit of Protein Kinase A Using H/D Fractionation Factors.

    Science.gov (United States)

    Li, Geoffrey C; Srivastava, Atul K; Kim, Jonggul; Taylor, Susan S; Veglia, Gianluigi

    2015-07-01

    Protein kinase A is a prototypical phosphoryl transferase, sharing its catalytic core (PKA-C) with the entire kinase family. PKA-C substrate recognition, active site organization, and product release depend on the enzyme's conformational transitions from the open to the closed state, which regulate its allosteric cooperativity. Here, we used equilibrium nuclear magnetic resonance hydrogen/deuterium (H/D) fractionation factors (φ) to probe the changes in the strength of hydrogen bonds within the kinase upon binding the nucleotide and a pseudosubstrate peptide (PKI5-24). We found that the φ values decrease upon binding both ligands, suggesting that the overall hydrogen bond networks in both the small and large lobes of PKA-C become stronger. However, we observed several important exceptions, with residues displaying higher φ values upon ligand binding. Notably, the changes in φ values are not localized near the ligand binding pockets; rather, they are radiated throughout the entire enzyme. We conclude that, upon ligand and pseudosubstrate binding, the hydrogen bond networks undergo extensive reorganization, revealing that the open-to-closed transitions require global rearrangements of the internal forces that stabilize the enzyme's fold. PMID:26030372

  19. Putting the Pieces Together: High-performance LC-MS/MS Provides Network-, Pathway-, and Protein-level Perspectives in Populus*

    OpenAIRE

    Abraham, Paul; Giannone, Richard J.; Adams, Rachel M.; Kalluri, Udaya; Tuskan, Gerald A.; Hettich, Robert L.

    2012-01-01

    High-performance mass spectrometry (MS)-based proteomics enabled the construction of a detailed proteome atlas for Populus, a woody perennial plant model organism. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: (1) network-wide, (2) pathway-specific, and (3) protein-level viewpoints. Together, enhanced protein retrieval t...

  20. Comparison of molecular signatures in large scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate

    Directory of Open Access Journals (Sweden)

    Rajat Suvra Banik

    2016-04-01

    Full Text Available Background Cancer, the disease of intricateness, has remained beyond our complete perception so far. Network systems biology (termed NSB is one of the most recent approaches to understand the unsolved problems of cancer development. From this perspective, differential protein networks (PINs have been developed based on the expression and interaction data of brain, cervix, lung, ovary and prostate for normal and cancer conditions. Methods Differential expression database GeneHub-GEPIS and interaction database STRING were applied for primary data retrieval. Cytoscape platform and related plugins named network analyzer; MCODE and ModuLand were used for visualization of complex networks and subsequent analysis. Results Significant differences were observedamong different common network parameters between normal and cancer states. Moreover, molecular complex numbers and overlapping modularization found to be varying significantly between normal and cancerous tissues. The number of the ranked molecular complex and the nodes involved in the overlapping modules were meaningfully higher in cancer condition.We identified79 commonly up regulated and 6 down regulated proteins in all five tissues. Number of nodes, edges; multi edge node pair, and average number of neighbor are found with significant fluctuations in case of cervix and ovarian tissues.Cluster analysis showed that the association of Myc and Cdk4 proteins is very close with other proteins within the network.Cervix and ovarian tissue showed higher increment of the molecular complex number and overlapping module network during cancer in comparison to normal state. Conclusions The differential molecular signatures identified from the work can be studied further to understand the cancer signaling process, and potential therapeutic and detection approach. [Biomed Res Ther 2016; 3(4.000: 605-615