WorldWideScience

Sample records for adaptor proteins networks

  1. The fifth adaptor protein complex.

    Directory of Open Access Journals (Sweden)

    Jennifer Hirst

    2011-10-01

    Full Text Available Adaptor protein (AP complexes sort cargo into vesicles for transport from one membrane compartment of the cell to another. Four distinct AP complexes have been identified, which are present in most eukaryotes. We report the existence of a fifth AP complex, AP-5. Tagged AP-5 localises to a late endosomal compartment in HeLa cells. AP-5 does not associate with clathrin and is insensitive to brefeldin A. Knocking down AP-5 subunits interferes with the trafficking of the cation-independent mannose 6-phosphate receptor and causes the cell to form swollen endosomal structures with emanating tubules. AP-5 subunits can be found in all five eukaryotic supergroups, but they have been co-ordinately lost in many organisms. Concatenated phylogenetic analysis provides robust resolution, for the first time, into the evolutionary order of emergence of the adaptor subunit families, showing AP-3 as the basal complex, followed by AP-5, AP-4, and AP-1 and AP-2. Thus, AP-5 is an evolutionarily ancient complex, which is involved in endosomal sorting, and which has links with hereditary spastic paraplegia.

  2. PAG - a multipurpose transmembrane adaptor protein

    Czech Academy of Sciences Publication Activity Database

    Hrdinka, Matouš; Hořejší, Václav

    2014-01-01

    Roč. 33, č. 41 (2014), s. 4881-4892 ISSN 0950-9232 R&D Projects: GA ČR(CZ) GBP302/12/G101 Institutional support: RVO:68378050 Keywords : PAG * adaptor protein * membrane raft Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 8.459, year: 2014

  3. Models of crk adaptor proteins in cancer.

    Science.gov (United States)

    Bell, Emily S; Park, Morag

    2012-05-01

    The Crk family of adaptor proteins (CrkI, CrkII, and CrkL), originally discovered as the oncogene fusion product, v-Crk, of the CT10 chicken retrovirus, lacks catalytic activity but engages with multiple signaling pathways through their SH2 and SH3 domains. Crk proteins link upstream tyrosine kinase and integrin-dependent signals to downstream effectors, acting as adaptors in diverse signaling pathways and cellular processes. Crk proteins are now recognized to play a role in the malignancy of many human cancers, stimulating renewed interest in their mechanism of action in cancer progression. The contribution of Crk signaling to malignancy has been predominantly studied in fibroblasts and in hematopoietic models and more recently in epithelial models. A mechanistic understanding of Crk proteins in cancer progression in vivo is still poorly understood in part due to the highly pleiotropic nature of Crk signaling. Recent advances in the structural organization of Crk domains, new roles in kinase regulation, and increased knowledge of the mechanisms and frequency of Crk overexpression in human cancers have provided an incentive for further study in in vivo models. An understanding of the mechanisms through which Crk proteins act as oncogenic drivers could have important implications in therapeutic targeting.

  4. Palmitoylated transmembrane adaptor proteins in leukocyte signaling

    Czech Academy of Sciences Publication Activity Database

    Štěpánek, Ondřej; Dráber, Peter; Hořejší, Václav

    2014-01-01

    Roč. 26, č. 5 (2014), s. 895-902 ISSN 0898-6568 R&D Projects: GA ČR(CZ) GBP302/12/G101 Institutional support: RVO:68378050 Keywords : Leukocyte * Adaptor * Palmitoylation Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.315, year: 2014

  5. Role of Crk Adaptor Proteins in Cellular Migration and Invasion in Human Breast Cancer

    National Research Council Canada - National Science Library

    Fathers, Kelly E

    2007-01-01

    The Crk adaptor proteins (CrkI, CrkII and CrkL) play an important role during cellular signalling by mediating the formation of protein-protein complexes and are involved in cellular migration, invasion, and adhesion...

  6. Role of Crk Adaptor Proteins in Cellular Migration and Invasion in Human Breast Cancer

    National Research Council Canada - National Science Library

    Fathers, Kelly E

    2008-01-01

    The Crk adaptor proteins (CrkI, CrkII and CrkL) play an important role during cellular signalling by mediating the formation of protein-protein complexes and are involved in cellular migration, invasion, and adhesion...

  7. The Emerging and Diverse Roles of Src-Like Adaptor Proteins in Health and Disease

    Directory of Open Access Journals (Sweden)

    Nikolett Marton

    2015-01-01

    Full Text Available Although Src-like adaptor proteins (SLAP-1 and SLAP-2 were mainly studied in lymphocytes, where they act as negative regulators and provide fine control of receptor signaling, recently, several other functions of these proteins were discovered. In addition to the well-characterized immunoregulatory functions, SLAP proteins appear to have an essential role in the pathogenesis of type I hypersensitivity, osteoporosis, and numerous malignant diseases. Both adaptor proteins are expressed in a wide variety of tissues, where they have mostly inhibitory effects on multiple intracellular signaling pathways. In this review, we summarize the diverse effects of SLAP proteins.

  8. Localization of the AP-3 adaptor complex defines a novel endosomal exit site for lysosomal membrane proteins

    NARCIS (Netherlands)

    Peden, A.A.; Oorschot, V.; Hesser, B.A.; Austin, C.D.; Scheller, R.H.; Klumperman, J.

    2004-01-01

    The adaptor protein (AP) 3 adaptor complex has been implicated in the transport of lysosomal membrane proteins, but its precise site of action has remained controversial. Here, we show by immuno-electron microscopy that AP-3 is associated with budding profiles evolving from a tubular endosomal

  9. Deletion of the LIME adaptor protein minimally affects T and B cell development and function

    Czech Academy of Sciences Publication Activity Database

    Grégoire, C.; Šímová, Šárka; Wang, Y.; Sansoni, A.; Richelme, S.; Schmidr-Giese, A.; Simeoni, L.; Angelisová, Pavla; Reinhold, D.; Burkhart, S.; Hořejší, Václav; Malissen, B.; Malissen, M.

    2007-01-01

    Roč. 37, č. 11 (2007), s. 3259-3269 ISSN 0014-2980 R&D Projects: GA MŠk 1M0506 Institutional research plan: CEZ:AV0Z50520514 Keywords : LIME * adaptor protein * receptor Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.662, year: 2007

  10. SCIMP, a transmembrane adaptor protein involved in major histocompatibility complex class II signaling

    Czech Academy of Sciences Publication Activity Database

    Dráber, Peter; Vonková, Ivana; Štěpánek, Ondřej; Hrdinka, Matouš; Kucová, Markéta; Skopcová, Tereza; Otáhal, Pavel; Angelisová, Pavla; Hořejší, Václav; Yeung, M.; Weiss, A.; Brdička, Tomáš

    2011-01-01

    Roč. 31, č. 22 (2011), s. 4550-4562 ISSN 0270-7306 R&D Projects: GA MŠk 1M0506; GA ČR GEMEM/09/E011 Institutional research plan: CEZ:AV0Z50520514 Keywords : SCIMP * transmembrane adaptor protein * MHC II Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 5.527, year: 2011

  11. Disabled is a putative adaptor protein that functions during signaling by the sevenless receptor tyrosine kinase.

    Science.gov (United States)

    Le, N; Simon, M A

    1998-08-01

    DRK, the Drosophila homolog of the SH2-SH3 domain adaptor protein Grb2, is required during signaling by the sevenless receptor tyrosine kinase (SEV). One role of DRK is to provide a link between activated SEV and the Ras1 activator SOS. We have investigated the possibility that DRK performs other functions by identifying additional DRK-binding proteins. We show that the phosphotyrosine-binding (PTB) domain-containing protein Disabled (DAB) binds to the DRK SH3 domains. DAB is expressed in the ommatidial clusters, and loss of DAB function disrupts ommatidial development. Moreover, reduction of DAB function attenuates signaling by a constitutively activated SEV. Our biochemical analysis suggests that DAB binds SEV directly via its PTB domain, becomes tyrosine phosphorylated upon SEV activation, and then serves as an adaptor protein for SH2 domain-containing proteins. Taken together, these results indicate that DAB is a novel component of the SEV signaling pathway.

  12. DMPD: Structure, function and regulation of the Toll/IL-1 receptor adaptor proteins. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 17667936 Structure, function and regulation of the Toll/IL-1 receptor adaptor prote... (.svg) (.html) (.csml) Show Structure, function and regulation of the Toll/IL-1 receptor adaptor proteins. ...PubmedID 17667936 Title Structure, function and regulation of the Toll/IL-1 recep

  13. Insight into Phosphatidylinositol-Dependent Membrane Localization of the Innate Immune Adaptor Protein Toll/Interleukin 1 Receptor Domain-Containing Adaptor Protein

    Directory of Open Access Journals (Sweden)

    Mahesh Chandra Patra

    2018-01-01

    Full Text Available The toll/interleukin 1 receptor (TIR domain-containing adaptor protein (TIRAP plays an important role in the toll-like receptor (TLR 2, TLR4, TLR7, and TLR9 signaling pathways. TIRAP anchors to phosphatidylinositol (PI 4,5-bisphosphate (PIP2 on the plasma membrane and PI (3,4,5-trisphosphate (PIP3 on the endosomal membrane and assists in recruitment of the myeloid differentiation primary response 88 protein to activated TLRs. To date, the structure and mechanism of TIRAP’s membrane association are only partially understood. Here, we modeled an all-residue TIRAP dimer using homology modeling, threading, and protein–protein docking strategies. Molecular dynamics simulations revealed that PIP2 creates a stable microdomain in a dipalmitoylphosphatidylcholine bilayer, providing TIRAP with its physiologically relevant orientation. Computed binding free energy values suggest that the affinity of PI-binding domain (PBD for PIP2 is stronger than that of TIRAP as a whole for PIP2 and that the short PI-binding motif (PBM contributes to the affinity between PBD and PIP2. Four PIP2 molecules can be accommodated by distinct lysine-rich surfaces on the dimeric PBM. Along with the known PI-binding residues (K15, K16, K31, and K32, additional positively charged residues (K34, K35, and R36 showed strong affinity toward PIP2. Lysine-to-alanine mutations at the PI-binding residues abolished TIRAP’s affinity for PIP2; however, K34, K35, and R36 consistently interacted with PIP2 headgroups through hydrogen bond (H-bond and electrostatic interactions. TIRAP exhibited a PIP2-analogous intermolecular contact and binding affinity toward PIP3, aided by an H-bond network involving K34, K35, and R36. The present study extends our understanding of TIRAP’s membrane association, which could be helpful in designing peptide decoys to block TLR2-, TLR4-, TLR7-, and TLR9-mediated autoimmune diseases.

  14. New Insights to Clathrin and Adaptor Protein 2 for the Design and Development of Therapeutic Strategies

    Directory of Open Access Journals (Sweden)

    Ebbe Toftgaard Poulsen

    2015-12-01

    Full Text Available The Amyloid Precursor Protein (APP has been extensively studied for its role as the precursor of the β-amyloid protein (Aβ in Alzheimer’s disease (AD. However, our understanding of the normal function of APP is still patchy. Emerging evidence indicates that a dysfunction in APP trafficking and degradation can be responsible for neuronal deficits and progressive degeneration in humans. We recently reported that the Y682 mutation in the 682YENPTY687 domain of APP affects its binding to specific adaptor proteins and leads to its anomalous trafficking, to defects in the autophagy machinery and to neuronal degeneration. In order to identify adaptors that influence APP function, we performed pull-down experiments followed by quantitative mass spectrometry (MS on hippocampal tissue extracts of three month-old mice incubated with either the 682YENPTY687 peptide, its mutated form, 682GENPTY687 or its phosphorylated form, 682pYENPTY687. Our experiments resulted in the identification of two proteins involved in APP internalization and trafficking: Clathrin heavy chain (hc and its Adaptor Protein 2 (AP-2. Overall our results consolidate and refine the importance of Y682 in APP normal functions from an animal model of premature aging and dementia. Additionally, they open the perspective to consider Clathrin hc and AP-2 as potential targets for the design and development of new therapeutic strategies.

  15. Selective autophagy of non-ubiquitylated targets in plants: looking for cognate receptor/adaptor proteins

    Directory of Open Access Journals (Sweden)

    Vasko eVeljanovski

    2014-06-01

    Full Text Available Cellular homeostasis is essential for the physiology of eukaryotic cells. Eukaryotic cells, including plant cells, utilize two main pathways to adjust the level of cytoplasmic components, namely the proteasomal and the lysosomal/vacuolar pathways. Macroautophagy is a lysosomal/vacuolar pathway which, until recently, was thought to be non-specific and a bulk degradation process. However, selective autophagy which can be activated in the cell under various physiological conditions, involves the specific degradation of defined macromolecules or organelles by a conserved molecular mechanism. For this process to be efficient, the mechanisms underlying the recognition and selection of the cargo to be engulfed by the double-membrane autophagosome are critical, and not yet well understood. Ubiquitin (poly-ubiquitin conjugation to the target appears to be a conserved ligand mechanism in many types of selective autophagy, and defined receptors/adaptors recognizing and regulating the autophagosomal capture of the ubiquitylated target have been characterized. However, non-proteinaceous and non-ubiquitylated cargoes are also selectively degraded by this pathway. This ubiquitin-independent selective autophagic pathway also involves receptor and/or adaptor proteins linking the cargo to the autophagic machinery. Some of these receptor/adaptor proteins including accessory autophagy-related (Atg and non-Atg proteins have been described in yeast and animal cells but not yet in plants. In this review we discuss the ubiquitin-independent cargo selection mechanisms in selective autophagy degradation of organelles and macromolecules and speculate on potential plant receptor/adaptor proteins.

  16. Adaptor protein Lnk negatively regulates the mutant MPL, MPLW515L associated with myeloproliferative disorders

    OpenAIRE

    Gery, Sigal; Gueller, Saskia; Chumakova, Katya; Kawamata, Norihiko; Liu, Liqin; Koeffler, H. Phillip

    2007-01-01

    Recently, activating myeloproliferative leukemia virus oncogene (MPL) mutations, MPLW515L/K, were described in myeloproliferative disorder (MPD) patients. MPLW515L leads to activation of downstream signaling pathways and cytokine-independent proliferation in hematopoietic cells. The adaptor protein Lnk is a negative regulator of several cytokine receptors, including MPL. We show that overexpression of Lnk in Ba/F3-MPLW515L cells inhibits cytokine-independent growth, while suppression of Lnk i...

  17. SR proteins are NXF1 adaptors that link alternative RNA processing to mRNA export.

    Science.gov (United States)

    Müller-McNicoll, Michaela; Botti, Valentina; de Jesus Domingues, Antonio M; Brandl, Holger; Schwich, Oliver D; Steiner, Michaela C; Curk, Tomaz; Poser, Ina; Zarnack, Kathi; Neugebauer, Karla M

    2016-03-01

    Nuclear export factor 1 (NXF1) exports mRNA to the cytoplasm after recruitment to mRNA by specific adaptor proteins. How and why cells use numerous different export adaptors is poorly understood. Here we critically evaluate members of the SR protein family (SRSF1-7) for their potential to act as NXF1 adaptors that couple pre-mRNA processing to mRNA export. Consistent with this proposal, >1000 endogenous mRNAs required individual SR proteins for nuclear export in vivo. To address the mechanism, transcriptome-wide RNA-binding profiles of NXF1 and SRSF1-7 were determined in parallel by individual-nucleotide-resolution UV cross-linking and immunoprecipitation (iCLIP). Quantitative comparisons of RNA-binding sites showed that NXF1 and SR proteins bind mRNA targets at adjacent sites, indicative of cobinding. SRSF3 emerged as the most potent NXF1 adaptor, conferring sequence specificity to RNA binding by NXF1 in last exons. Interestingly, SRSF3 and SRSF7 were shown to bind different sites in last exons and regulate 3' untranslated region length in an opposing manner. Both SRSF3 and SRSF7 promoted NXF1 recruitment to mRNA. Thus, SRSF3 and SRSF7 couple alternative splicing and polyadenylation to NXF1-mediated mRNA export, thereby controlling the cytoplasmic abundance of transcripts with alternative 3' ends. © 2016 Müller-McNicoll et al.; Published by Cold Spring Harbor Laboratory Press.

  18. Identification of actin binding protein, ABP-280, as a binding partner of human Lnk adaptor protein.

    Science.gov (United States)

    He, X; Li, Y; Schembri-King, J; Jakes, S; Hayashi, J

    2000-08-01

    Human Lnk (hLnk) is an adaptor protein with multiple functional domains that regulates T cell activation signaling. In order to identify cellular Lnk binding partners, a yeast two-hybrid screening of human spleen cDNA library was carried out using human hLnk as bait. A polypeptide sequence identical to the C-terminal segment of the actin binding protein (ABP-280) was identified as a hLnk binding protein. The expressed hLnk and the FLAG tagged C-terminal 673 amino acid residues of ABP-280 or the endogenous ABP-280 in COS-7 cells could be co-immunoprecipitated using antibodies either to hLnk, FLAG or ABP-280, respectively. Furthermore, immunofluorescence confocal microscope showed that hLnk and ABP-280 co-localized at the plasma membrane and at juxtanuclear region of COS-7 cells. In Jurkat cells, the endogenous hLnk also associates with the endogenous ABP-280 indicating that the association of these two proteins is physiological. The interacting domains of both proteins were mapped using yeast two-hybrid assays. Our results indicate that hLnk binds to the residues 2006-2454 (repeats 19-23C) of ABP-280. The domain in hLnk that associates with ABP-280 was mapped to an interdomain region of 56 amino acids between pleckstrin homology and Src homology 2 domains. These results suggest that hLnk may exert its regulatory role through its association with ABP-280.

  19. Interactions between the Hepatitis C Virus Nonstructural 2 Protein and Host Adaptor Proteins 1 and 4 Orchestrate Virus Release

    Directory of Open Access Journals (Sweden)

    Fei Xiao

    2018-03-01

    Full Text Available Hepatitis C virus (HCV spreads via secreted cell-free particles or direct cell-to-cell transmission. Yet, virus-host determinants governing differential intracellular trafficking of cell-free- and cell-to-cell-transmitted virus remain unknown. The host adaptor proteins (APs AP-1A, AP-1B, and AP-4 traffic in post-Golgi compartments, and the latter two are implicated in basolateral sorting. We reported that AP-1A mediates HCV trafficking during release, whereas the endocytic adaptor AP-2 mediates entry and assembly. We demonstrated that the host kinases AAK1 and GAK regulate HCV infection by controlling these clathrin-associated APs. Here, we sought to define the roles of AP-4, a clathrin-independent adaptor; AP-1A; and AP-1B in HCV infection. We screened for interactions between HCV proteins and the μ subunits of AP-1A, AP-1B, and AP-4 by mammalian cell-based protein fragment complementation assays. The nonstructural 2 (NS2 protein emerged as an interactor of these adaptors in this screening and by coimmunoprecipitations in HCV-infected cells. Two previously unrecognized dileucine-based motifs in the NS2 C terminus mediated AP binding and HCV release. Infectivity and coculture assays demonstrated that while all three adaptors mediate HCV release and cell-free spread, AP-1B and AP-4, but not AP-1A, mediate cell-to-cell spread. Live-cell imaging revealed HCV cotrafficking with AP-1A, AP-1B, and AP-4 and that AP-4 mediates HCV trafficking in a post-Golgi compartment. Lastly, HCV cell-to-cell spread was regulated by AAK1 and GAK and thus susceptible to treatment with AAK1 and GAK inhibitors. These data provide a mechanistic understanding of HCV trafficking in distinct release pathways and reveal a requirement for APs in cell-to-cell viral spread.

  20. Regulation and function of the CD3¿ DxxxLL motif: a binding site for adaptor protein-1 and adaptor protein-2 in vitro

    DEFF Research Database (Denmark)

    Dietrich, J; Kastrup, J; Nielsen, B L

    1997-01-01

    /CD3gamma chimeras; and in vitro by binding CD3gamma peptides to clathrin-coated vesicle adaptor proteins (APs). We find that the CD3gamma D127xxxLL131/132 sequence represents one united motif for binding of both AP-1 and AP-2, and that this motif functions as an active sorting motif in monomeric CD4...... and for AP binding in vitro. Furthermore, we provide evidence indicating that phosphorylation of CD3gamma S126 in the context of the complete TCR induces a conformational change that exposes the DxxxLL sequence for AP binding. Exposure of the DxxxLL motif causes an increase in the TCR internalization rate...

  1. Biochemical and genetic analysis of the Drk SH2/SH3 adaptor protein of Drosophila.

    OpenAIRE

    Raabe, T; Olivier, J P; Dickson, B J; Liu, X; Gish, G D; Pawson, T; Hafen, E

    1995-01-01

    The Drk SH3-SH2-SH3 adaptor protein has been genetically identified in a screen for rate-limiting components acting downstream of the Sevenless (Sev) receptor tyrosine kinase in the developing eye of Drosophila. It provides a link between the activated Sev receptor and Sos, a guanine nucleotide release factor that activates Ras1. We have used a combined biochemical and genetic approach to study the interactions between Sev, Drk and Sos. We show that Tyr2546 in the cytoplasmic tail of Sev is r...

  2. Transmembrane adaptor proteins in the high-affinity IgE receptor signaling

    Czech Academy of Sciences Publication Activity Database

    Dráber, Petr; Hálová, Ivana; Levi-Schaffer, F.; Dráberová, Lubica

    2012-01-01

    Roč. 2, 11.1. (2012), s. 95 ISSN 1664-3224 R&D Projects: GA MŠk 1M0506; GA ČR GA301/09/1826; GA ČR GAP302/10/1759; GA AV ČR KAN200520701 Grant - others:AV ČR(CZ) M200520901 Institutional research plan: CEZ:AV0Z50520514 Institutional support: RVO:68378050 Keywords : IgE receptor * LAT/LAT1 * LAX * NTAL/Lab/LAT2 * PAG/Cbp * mast cells * plasma membrane * transmembrane adaptor proteins Subject RIV: EB - Genetics ; Molecular Biology

  3. Nck adaptor proteins link Tks5 to invadopodia actin regulation and ECM degradation.

    Science.gov (United States)

    Stylli, Stanley S; Stacey, T T I; Verhagen, Anne M; Xu, San San; Pass, Ian; Courtneidge, Sara A; Lock, Peter

    2009-08-01

    Invadopodia are actin-based projections enriched with proteases, which invasive cancer cells use to degrade the extracellular matrix (ECM). The Phox homology (PX)-Src homology (SH)3 domain adaptor protein Tks5 (also known as SH3PXD2A) cooperates with Src tyrosine kinase to promote invadopodia formation but the underlying pathway is not clear. Here we show that Src phosphorylates Tks5 at Y557, inducing it to associate directly with the SH3-SH2 domain adaptor proteins Nck1 and Nck2 in invadopodia. Tks5 mutants unable to bind Nck show reduced matrix degradation-promoting activity and recruit actin to invadopodia inefficiently. Conversely, Src- and Tks5-driven matrix proteolysis and actin assembly in invadopodia are enhanced by Nck1 or Nck2 overexpression and inhibited by Nck1 depletion. We show that clustering at the plasma membrane of the Tks5 inter-SH3 region containing Y557 triggers phosphorylation at this site, facilitating Nck recruitment and F-actin assembly. These results identify a Src-Tks5-Nck pathway in ECM-degrading invadopodia that shows parallels with pathways linking several mammalian and pathogen-derived proteins to local actin regulation.

  4. Structural basis for the interaction of the adaptor protein grb14 with activated ras.

    Directory of Open Access Journals (Sweden)

    Rohini Qamra

    Full Text Available Grb14, a member of the Grb7-10-14 family of cytoplasmic adaptor proteins, is a tissue-specific negative regulator of insulin signaling. Grb7-10-14 contain several signaling modules, including a Ras-associating (RA domain, a pleckstrin-homology (PH domain, a family-specific BPS (between PH and SH2 region, and a C-terminal Src-homology-2 (SH2 domain. We showed previously that the RA and PH domains, along with the BPS region and SH2 domain, are necessary for downregulation of insulin signaling. Here, we report the crystal structure at 2.4-Å resolution of the Grb14 RA and PH domains in complex with GTP-loaded H-Ras (G12V. The structure reveals that the Grb14 RA and PH domains form an integrated structural unit capable of binding simultaneously to small GTPases and phosphoinositide lipids. The overall mode of binding of the Grb14 RA domain to activated H-Ras is similar to that of the RA domains of RalGDS and Raf1 but with important distinctions. The integrated RA-PH structural unit in Grb7-10-14 is also found in a second adaptor family that includes Rap1-interacting adaptor molecule (RIAM and lamellipodin, proteins involved in actin-cytoskeleton rearrangement. The structure of Grb14 RA-PH in complex with H-Ras represents the first detailed molecular characterization of tandem RA-PH domains bound to a small GTPase and provides insights into the molecular basis for specificity.

  5. Synthetic protein scaffolds based on peptide motifs and cognate adaptor domains for improving metabolic productivity

    Directory of Open Access Journals (Sweden)

    Anselm H.C. Horn

    2015-11-01

    Full Text Available The efficiency of many cellular processes relies on the defined interaction among different proteins within the same metabolic or signaling pathway. Consequently, a spatial colocalization of functionally interacting proteins has frequently emerged during evolution. This concept has been adapted within the synthetic biology community for the purpose of creating artificial scaffolds. A recent advancement of this concept is the use of peptide motifs and their cognate adaptor domains. SH2, SH3, GBD, and PDZ domains have been used most often in research studies to date. The approach has been successfully applied to the synthesis of a variety of target molecules including catechin, D-glucaric acid, H2, hydrochinone, resveratrol, butyrate, gamma-aminobutyric acid, and mevalonate. Increased production levels of up to 77-fold have been observed compared to non-scaffolded systems. A recent extension of this concept is the creation of a covalent linkage between peptide motifs and adaptor domains, which leads to a more stable association of the scaffolded systems and thus bears the potential to further enhance metabolic productivity.

  6. The adaptor protein CIKS/Act1 is essential for IL-25-mediated allergic airway inflammation.

    Science.gov (United States)

    Claudio, Estefania; Sønder, Søren Ulrik; Saret, Sun; Carvalho, Gabrielle; Ramalingam, Thirumalai R; Wynn, Thomas A; Chariot, Alain; Garcia-Perganeda, Antonio; Leonardi, Antonio; Paun, Andrea; Chen, Amy; Ren, Nina Y; Wang, Hongshan; Siebenlist, Ulrich

    2009-02-01

    IL-17 is the signature cytokine of recently discovered Th type 17 (Th17) cells, which are prominent in defense against extracellular bacteria and fungi as well as in autoimmune diseases, such as rheumatoid arthritis and experimental autoimmune encephalomyelitis in animal models. IL-25 is a member of the IL-17 family of cytokines, but has been associated with Th2 responses instead and may negatively cross-regulate Th17/IL-17 responses. IL-25 can initiate an allergic asthma-like inflammation in the airways, which includes recruitment of eosinophils, mucus hypersecretion, Th2 cytokine production, and airways hyperreactivity. We demonstrate that these effects of IL-25 are entirely dependent on the adaptor protein CIKS (also known as Act1). Surprisingly, this adaptor is necessary to transmit IL-17 signals as well, despite the very distinct biologic responses that these two cytokines elicit. We identify CD11c(+) macrophage-like lung cells as physiologic relevant targets of IL-25 in vivo.

  7. Role of the AP-5 adaptor protein complex in late endosome-to-Golgi retrieval.

    Directory of Open Access Journals (Sweden)

    Jennifer Hirst

    2018-01-01

    Full Text Available The AP-5 adaptor protein complex is presumed to function in membrane traffic, but so far nothing is known about its pathway or its cargo. We have used CRISPR-Cas9 to knock out the AP-5 ζ subunit gene, AP5Z1, in HeLa cells, and then analysed the phenotype by subcellular fractionation profiling and quantitative mass spectrometry. The retromer complex had an altered steady-state distribution in the knockout cells, and several Golgi proteins, including GOLIM4 and GOLM1, were depleted from vesicle-enriched fractions. Immunolocalisation showed that loss of AP-5 led to impaired retrieval of the cation-independent mannose 6-phosphate receptor (CIMPR, GOLIM4, and GOLM1 from endosomes back to the Golgi region. Knocking down the retromer complex exacerbated this phenotype. Both the CIMPR and sortilin interacted with the AP-5-associated protein SPG15 in pull-down assays, and we propose that sortilin may act as a link between Golgi proteins and the AP-5/SPG11/SPG15 complex. Together, our findings suggest that AP-5 functions in a novel sorting step out of late endosomes, acting as a backup pathway for retromer. This provides a mechanistic explanation for why mutations in AP-5/SPG11/SPG15 cause cells to accumulate aberrant endolysosomes, and highlights the role of endosome/lysosome dysfunction in the pathology of hereditary spastic paraplegia and other neurodegenerative disorders.

  8. PHF6 Degrees of Separation: The Multifaceted Roles of a Chromatin Adaptor Protein.

    Science.gov (United States)

    Todd, Matthew A M; Ivanochko, Danton; Picketts, David J

    2015-06-19

    The importance of chromatin regulation to human disease is highlighted by the growing number of mutations identified in genes encoding chromatin remodeling proteins. While such mutations were first identified in severe developmental disorders, or in specific cancers, several genes have been implicated in both, including the plant homeodomain finger protein 6 (PHF6) gene. Indeed, germline mutations in PHF6 are the cause of the Börjeson-Forssman-Lehmann X-linked intellectual disability syndrome (BFLS), while somatic PHF6 mutations have been identified in T-cell acute lymphoblastic leukemia (T-ALL) and acute myeloid leukemia (AML). Studies from different groups over the last few years have made a significant impact towards a functional understanding of PHF6 protein function. In this review, we summarize the current knowledge of PHF6 with particular emphasis on how it interfaces with a distinct set of interacting partners and its functional roles in the nucleoplasm and nucleolus. Overall, PHF6 is emerging as a key chromatin adaptor protein critical to the regulation of neurogenesis and hematopoiesis.

  9. Losses, Expansions, and Novel Subunit Discovery of Adaptor Protein Complexes in Haptophyte Algae.

    Science.gov (United States)

    Lee, Laura J Y; Klute, Mary J; Herman, Emily K; Read, Betsy; Dacks, Joel B

    2015-11-01

    The phylum Haptophyta (Diaphoratickes) contains marine algae that perform biomineralization, extruding large, distinctive calcium carbonate scales (coccoliths) that completely cover the cell. Coccolith production is an important part of global carbon cycling; however, the membrane trafficking pathway by which they are secreted has not yet been elucidated. In most eukaryotes, post-Golgi membrane trafficking involves five heterotetrameric adaptor protein (AP) complexes, which impart cargo selection specificity. To better understand coccolith secretion, we performed comparative genomic, phylogenetic, and transcriptomic analyses of the AP complexes in Emiliania huxleyi strains 92A, Van556, EH2, and CCMP1516, and related haptophytes Gephyrocapsa oceanica and Isochrysis galbana; the latter has lost the ability to biomineralize. We show that haptophytes have a modified membrane trafficking system (MTS), as we found both AP subunit losses and duplications. Additionally, we identified a single conserved subunit of the AP-related TSET complex, whose expression suggests a functional role in membrane trafficking. Finally, we detected novel alpha adaptin ear and gamma adaptin ear proteins, the first of their kind to be described outside of opisthokonts. These novel ear proteins and the sculpting of the MTS may support the capacity for biomineralization in haptophytes, enhancing their ability to perform this highly specialized form of secretion. Copyright © 2015 Elsevier GmbH. All rights reserved.

  10. Biochemical and genetic analysis of the Drk SH2/SH3 adaptor protein of Drosophila.

    Science.gov (United States)

    Raabe, T; Olivier, J P; Dickson, B; Liu, X; Gish, G D; Pawson, T; Hafen, E

    1995-06-01

    The Drk SH3-SH2-SH3 adaptor protein has been genetically identified in a screen for rate-limiting components acting downstream of the Sevenless (Sev) receptor tyrosine kinase in the developing eye of Drosophila. It provides a link between the activated Sev receptor and Sos, a guanine nucleotide release factor that activates Ras1. We have used a combined biochemical and genetic approach to study the interactions between Sev, Drk and Sos. We show that Tyr2546 in the cytoplasmic tail of Sev is required for Drk binding, probably because it provides a recognition site for the Drk SH2 domain. Interestingly, a mutation at this site does not completely block Sev function in vivo. This may suggest that Sev can signal in a Drk-independent, parallel pathway or that Drk can also bind to an intermediate docking protein. Analysis of the Drk-Sos interaction has identified a high affinity binding site for Drk SH3 domains in the Sos tail. We show that the N-terminal Drk SH3 domain is primarily responsible for binding to the tail of Sos in vitro, and for signalling to Ras in vivo.

  11. Architecture and roles of periplasmic adaptor proteins in tripartite efflux assemblies.

    Directory of Open Access Journals (Sweden)

    Vassiliy N. Bavro

    2015-05-01

    Full Text Available Recent years have seen major advances in the structural understanding of the different components of tripartite efflux assemblies, which encompass the multidrug efflux (MDR pumps and type I secretion systems. The majority of these investigations have focused on the role played by the inner membrane transporters and the outer membrane factor (OMF, leaving the third component of the system – the Periplasmic Adaptor Proteins (PAPs - relatively understudied. Here we review the current state of knowledge of these versatile proteins which, far from being passive linkers between the OMF and the transporter, emerge as active architects of tripartite assemblies, and play diverse roles in the transport process. Recognition between the PAPs and OMFs is essential for pump assembly and function, and targeting this interaction may provide a novel avenue for combating multidrug resistance. With the recent advances elucidating the drug-efflux and energetics of the tripartite assemblies, the understanding of the interaction between the OMFs and PAPs is the last piece remaining in the complete structure of the tripartite pump assembly puzzle.

  12. The adaptor protein CrkII regulates IGF-1-induced biological behaviors of pancreatic ductal adenocarcinoma.

    Science.gov (United States)

    Liu, Rui; Wang, Qing; Xu, Guangying; Li, Kexin; Zhou, Lingli; Xu, Baofeng

    2016-01-01

    Recently, the adaptor protein CrkII has been proved to function in initiating signals for proliferation and invasion in some malignancies. However, the specific mechanisms underlying insulin-like growth factor 1 (IGF-1)-CrkII signaling-induced proliferation of pancreatic ductal adenocarcinoma (PDAC) were not unraveled. In this work, PDAC tissues and cell lines were subjected to in vitro and in vivo assays. Our findings showed that CrkII was abundantly expressed in PDAC tissues and closely correlated with tumor-node-metastasis (TNM) stage and invasion. When cells were subjected to si-CrkII, si-CrkII inhibited IGF-1-mediated PDAC cell growth. In vitro, we demonstrated the upregulation of CrkII, p-Erk1/2, and p-Akt occurring in IGF-1-treated PDAC cells. Conversely, si-CrkII affected upregulation of CrkII, p-Erk1/2, and p-Akt. In addition, cell cycle and in vivo assay identified that knockdown of CrkII inhibited the entry of G1 into S phase and the increase of PDAC tumor weight. In conclusion, CrkII mediates IGF-1 signaling and further balanced PDAC biological behaviors via Erk1/2 and Akt pathway, which indicates that CrkII gene and protein may act as an effective target for the treatment of PDAC.

  13. Adaptor protein complex 2-mediated endocytosis is crucial for male reproductive organ development in Arabidopsis.

    Science.gov (United States)

    Kim, Soo Youn; Xu, Zheng-Yi; Song, Kyungyoung; Kim, Dae Heon; Kang, Hyangju; Reichardt, Ilka; Sohn, Eun Ju; Friml, Jirí; Juergens, Gerd; Hwang, Inhwan

    2013-08-01

    Fertilization in flowering plants requires the temporal and spatial coordination of many developmental processes, including pollen production, anther dehiscence, ovule production, and pollen tube elongation. However, it remains elusive as to how this coordination occurs during reproduction. Here, we present evidence that endocytosis, involving heterotetrameric adaptor protein complex 2 (AP-2), plays a crucial role in fertilization. An Arabidopsis thaliana mutant ap2m displays multiple defects in pollen production and viability, as well as elongation of staminal filaments and pollen tubes, all of which are pivotal processes needed for fertilization. Of these abnormalities, the defects in elongation of staminal filaments and pollen tubes were partially rescued by exogenous auxin. Moreover, DR5rev:GFP (for green fluorescent protein) expression was greatly reduced in filaments and anthers in ap2m mutant plants. At the cellular level, ap2m mutants displayed defects in both endocytosis of N-(3-triethylammonium-propyl)-4-(4-diethylaminophenylhexatrienyl) pyridinium dibromide, a lypophilic dye used as an endocytosis marker, and polar localization of auxin-efflux carrier PIN FORMED2 (PIN2) in the stamen filaments. Moreover, these defects were phenocopied by treatment with Tyrphostin A23, an inhibitor of endocytosis. Based on these results, we propose that AP-2-dependent endocytosis plays a crucial role in coordinating the multiple developmental aspects of male reproductive organs by modulating cellular auxin level through the regulation of the amount and polarity of PINs.

  14. Stargazin regulates AMPA receptor trafficking through adaptor protein complexes during long-term depression

    Science.gov (United States)

    Matsuda, Shinji; Kakegawa, Wataru; Budisantoso, Timotheus; Nomura, Toshihiro; Kohda, Kazuhisa; Yuzaki, Michisuke

    2013-11-01

    Long-term depression (LTD) underlies learning and memory in various brain regions. Although postsynaptic AMPA receptor trafficking mediates LTD, its underlying molecular mechanisms remain largely unclear. Here we show that stargazin, a transmembrane AMPA receptor regulatory protein, forms a ternary complex with adaptor proteins AP-2 and AP-3A in hippocampal neurons, depending on its phosphorylation state. Inhibiting the stargazin-AP-2 interaction disrupts NMDA-induced AMPA receptor endocytosis, and inhibiting that of stargazin-AP-3A abrogates the late endosomal/lysosomal trafficking of AMPA receptors, thereby upregulating receptor recycling to the cell surface. Similarly, stargazin’s interaction with AP-2 or AP-3A is necessary for low-frequency stimulus-evoked LTD in CA1 hippocampal neurons. Thus, stargazin has a crucial role in NMDA-dependent LTD by regulating two trafficking pathways of AMPA receptors—transport from the cell surface to early endosomes and from early endosomes to late endosomes/lysosomes—through its sequential binding to AP-2 and AP-3A.

  15. Adaptor protein Lnk negatively regulates the mutant MPL, MPLW515L associated with myeloproliferative disorders.

    Science.gov (United States)

    Gery, Sigal; Gueller, Saskia; Chumakova, Katya; Kawamata, Norihiko; Liu, Liqin; Koeffler, H Phillip

    2007-11-01

    Recently, activating myeloproliferative leukemia virus oncogene (MPL) mutations, MPLW515L/K, were described in myeloproliferative disorder (MPD) patients. MPLW515L leads to activation of downstream signaling pathways and cytokine-independent proliferation in hematopoietic cells. The adaptor protein Lnk is a negative regulator of several cytokine receptors, including MPL. We show that overexpression of Lnk in Ba/F3-MPLW515L cells inhibits cytokine-independent growth, while suppression of Lnk in UT7-MPLW515L cells enhances proliferation. Lnk blocks the activation of Jak2, Stat3, Erk, and Akt in these cells. Furthermore, MPLW515L-expressing cells are more susceptible to Lnk inhibitory functions than their MPL wild-type (MPLWT)-expressing counterparts. Lnk associates with activated MPLWT and MPLW515L and colocalizes with the receptors at the plasma membrane. The SH2 domain of Lnk is essential for its binding and for its down-regulation of MPLWT and MPLW515L. Lnk itself is tyrosine-phosphorylated following thrombopoietin stimulation. Further elucidating the cellular pathways that attenuate MPLW515L will provide insight into the pathogenesis of MPD and could help develop specific therapeutic approaches.

  16. The p66(Shc adaptor protein controls oxidative stress response in early bovine embryos.

    Directory of Open Access Journals (Sweden)

    Dean H Betts

    Full Text Available The in vitro production of mammalian embryos suffers from high frequencies of developmental failure due to excessive levels of permanent embryo arrest and apoptosis caused by oxidative stress. The p66Shc stress adaptor protein controls oxidative stress response of somatic cells by regulating intracellular ROS levels through multiple pathways, including mitochondrial ROS generation and the repression of antioxidant gene expression. We have previously demonstrated a strong relationship with elevated p66Shc levels, reduced antioxidant levels and greater intracellular ROS generation with the high incidence of permanent cell cycle arrest of 2-4 cell embryos cultured under high oxygen tensions or after oxidant treatment. The main objective of this study was to establish a functional role for p66Shc in regulating the oxidative stress response during early embryo development. Using RNA interference in bovine zygotes we show that p66Shc knockdown embryos exhibited increased MnSOD levels, reduced intracellular ROS and DNA damage that resulted in a greater propensity for development to the blastocyst stage. P66Shc knockdown embryos were stress resistant exhibiting significantly reduced intracellular ROS levels, DNA damage, permanent 2-4 cell embryo arrest and diminished apoptosis frequencies after oxidant treatment. The results of this study demonstrate that p66Shc controls the oxidative stress response in early mammalian embryos. Small molecule inhibition of p66Shc may be a viable clinical therapy to increase the developmental potential of in vitro produced mammalian embryos.

  17. Tetraspanins and Transmembrane Adaptor Proteins As Plasma Membrane Organizers-Mast Cell Case.

    Science.gov (United States)

    Halova, Ivana; Draber, Petr

    2016-01-01

    The plasma membrane contains diverse and specialized membrane domains, which include tetraspanin-enriched domains (TEMs) and transmembrane adaptor protein (TRAP)-enriched domains. Recent biophysical, microscopic, and functional studies indicated that TEMs and TRAP-enriched domains are involved in compartmentalization of physicochemical events of such important processes as immunoreceptor signal transduction and chemotaxis. Moreover, there is evidence of a cross-talk between TEMs and TRAP-enriched domains. In this review we discuss the presence and function of such domains and their crosstalk using mast cells as a model. The combined data based on analysis of selected mast cell-expressed tetraspanins [cluster of differentiation (CD)9, CD53, CD63, CD81, CD151)] or TRAPs [linker for activation of T cells (LAT), non-T cell activation linker (NTAL), and phosphoprotein associated with glycosphingolipid-enriched membrane microdomains (PAG)] using knockout mice or specific antibodies point to a diversity within these two families and bring evidence of the important roles of these molecules in signaling events. An example of this diversity is physical separation of two TRAPs, LAT and NTAL, which are in many aspects similar but show plasma membrane location in different microdomains in both non-activated and activated cells. Although our understanding of TEMs and TRAP-enriched domains is far from complete, pharmaceutical applications of the knowledge about these domains are under way.

  18. Tetraspanins and Transmembrane Adaptor Proteins as Plasma Membrane Organizers – Mast Cell Case

    Directory of Open Access Journals (Sweden)

    Ivana eHalova

    2016-05-01

    Full Text Available The plasma membrane contains diverse and specialized membrane domains, which include tetraspanin-enriched domains (TEMs and transmembrane adaptor protein (TRAP-enriched domains. Recent biophysical, microscopic and functional studies indicated that TEMs and TRAP-enriched domains are involved in compartmentalization of physicochemical events of such important processes as immunoreceptor signal transduction and chemotaxis. Moreover, there is evidence of a cross-talk between TEMs and TRAP-enriched domains. In this review we discuss the presence and function of such domains and their crosstalk using mast cells as a model. The combined data based on analysis of selected mast cell-expressed tetraspanins [cluster of differentiation (CD9, CD53, CD63, CD81, CD151] or TRAPs [linker for activation of T cells (LAT, non-T cell activation linker (NTAL, and phosphoprotein associated with glycosphingolipid-enriched membrane microdomains (PAG] using knockout mice or specific antibodies point to a diversity within these two families and bring evidence of the important roles of these molecules in signaling events. An example of this diversity is physical separation of two TRAPs, LAT and NTAL, which are in many aspects similar but show plasma membrane location in different microdomains in both non-activated and activated cells. Although our understanding of TEMs and TRAP-enriched domains is far from complete, pharmaceutical applications of the knowledge about these domains are under way.

  19. SH2/SH3 adaptor proteins can link tyrosine kinases to a Ste20-related protein kinase, HPK1.

    Science.gov (United States)

    Anafi, M; Kiefer, F; Gish, G D; Mbamalu, G; Iscove, N N; Pawson, T

    1997-10-31

    Ste20-related protein kinases have been implicated as regulating a range of cellular responses, including stress-activated protein kinase pathways and the control of cytoskeletal architecture. An important issue involves the identities of the upstream signals and regulators that might control the biological functions of mammalian Ste20-related protein kinases. HPK1 is a protein-serine/threonine kinase that possesses a Ste20-like kinase domain, and in transfected cells activates a protein kinase pathway leading to the stress-activated protein kinase SAPK/JNK. Here we have investigated candidate upstream regulators that might interact with HPK1. HPK1 possesses an N-terminal catalytic domain and an extended C-terminal tail with four proline-rich motifs. The SH3 domains of Grb2 bound in vitro to specific proline-rich motifs in the HPK1 tail and functioned synergistically to direct the stable binding of Grb2 to HPK1 in transfected Cos1 cells. Epidermal growth factor (EGF) stimulation did not affect the binding of Grb2 to HPK1 but induced recruitment of the Grb2.HPK1 complex to the autophosphorylated EGF receptor and to the Shc docking protein. Several activated receptor and cytoplasmic tyrosine kinases, including the EGF receptor, stimulated the tyrosine phosphorylation of the HPK1 serine/threonine kinase. These results suggest that HPK1, a mammalian Ste20-related protein-serine/threonine kinase, can potentially associate with protein-tyrosine kinases through interactions mediated by SH2/SH3 adaptors such as Grb2. Such interaction may provide a possible mechanism for cross-talk between distinct biochemical pathways following the activation of tyrosine kinases.

  20. NTAL (non-T cell activation linker):a transmembrane adaptor protein involved in immunoreceptor signaling

    Czech Academy of Sciences Publication Activity Database

    Brdička, Tomáš; Imrich, Martin; Angelisová, Pavla; Brdičková, Naděžda; Horváth, Ondřej; Špička, Jiří; Hilgert, Ivan; Lusková, Petra; Dráber, Petr; Novák, P.; Engels, N.; Wienands, J.; Simeoni, L.; Osterreicher, J.; Aguado, E.; Malissen, M.; Schraven, B.; Hořejší, Václav

    2002-01-01

    Roč. 196, č. 12 (2002), s. 16180-16185 ISSN 0022-1007 R&D Projects: GA MŠk LN00A026 Keywords : NTAL * transmembrane adaptor * immunoreceptor signaling Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 15.838, year: 2002

  1. The motogenic and mitogenic responses to HGF are amplified by the Shc adaptor protein

    DEFF Research Database (Denmark)

    Pelicci, G; Giordano, S; Zhen, Z

    1995-01-01

    The receptor of Hepatocyte Growth Factor-Scatter Factor (HGF) is a tyrosine kinase which regulates cell motility and growth. After ligand-induced tyrosine phosphorylation, the HGF receptor associates with the Shc adaptor, via the SH2 domain. Site-directed mutagenesis of the HGF receptor indicates...

  2. Several adaptor proteins promote intracellular localisation of the transporter MRP4/ABCC4 in platelets and haematopoietic cells.

    Science.gov (United States)

    Schaletzki, Yvonne; Kromrey, Marie-Luise; Bröderdorf, Susanne; Hammer, Elke; Grube, Markus; Hagen, Paul; Sucic, Sonja; Freissmuth, Michael; Völker, Uwe; Greinacher, Andreas; Rauch, Bernhard H; Kroemer, Heyo K; Jedlitschky, Gabriele

    2017-01-05

    The multidrug resistance protein 4 (MRP4/ABCC4) has been identified as an important transporter for signalling molecules including cyclic nucleotides and several lipid mediators in platelets and may thus represent a novel target to interfere with platelet function. Besides its localisation in the plasma membrane, MRP4 has been also detected in the membrane of dense granules in resting platelets. In polarised cells it is localised at the basolateral or apical plasma membrane. To date, the mechanism of MRP4 trafficking has not been elucidated; protein interactions may regulate both the localisation and function of this transporter. We approached this issue by searching for interacting proteins by in vitro binding assays, followed by immunoblotting and mass spectrometry, and by visualising their co-localisation in platelets and haematopoietic cells. We identified the PDZ domain containing scaffold proteins ezrin-binding protein 50 (EBP50/NHERF1), postsynaptic density protein 95 (PSD95), and sorting nexin 27 (SNX27), but also the adaptor protein complex 3 subunit β3A (AP3B1) and the heat shock protein HSP90 as putative interaction partners of MRP4. The knock-down of SNX27, PSD95, and AP3B1 by siRNA in megakaryoblastic leukaemia cells led to a redistribution of MRP4 from intracellular structures to the plasma membrane. Inhibition of HSP90 led to a diminished expression and retention of MRP4 in the endoplasmic reticulum. These results indicate that MRP4 localisation and function are regulated by multiple protein interactions. Changes in the adaptor proteins can hence lead to altered localisation and function of the transporter.

  3. CD6 and Linker of Activated T Cells are Potential Interaction Partners for T Cell-Specific Adaptor Protein.

    Science.gov (United States)

    Hem, C D; Ekornhol, M; Granum, S; Sundvold-Gjerstad, V; Spurkland, A

    2017-02-01

    The T cell-specific adaptor protein (TSAd) contains several protein interaction domains, and is merging as a modulator of T cell activation. Several interaction partners for the TSAd proline-rich region and phosphotyrosines have been identified, including the Src and Tec family kinases lymphocyte-specific protein tyrosine kinase and interleukin 2-inducible T cell kinase. Via its Src homology 2 (SH2) domain, TSAd may thus function as a link between these enzymes and other signalling molecules. However, few binding partners to the TSAd SH2 domain in T cells are hitherto known. Through the use of in silico ligand prediction, peptide spot arrays, pull-down and immunoprecipitation experiments, we here report novel interactions between the TSAd SH2 domain and CD6 phosphotyrosine (pTyr) 629 and linker of activated T cells (LAT) pTyr 171 , pTyr 191 and pTyr 226 . © 2016 The Foundation for the Scandinavian Journal of Immunology.

  4. Receptor tyrosine phosphatase R-PTP-alpha is tyrosine-phosphorylated and associated with the adaptor protein Grb2

    DEFF Research Database (Denmark)

    Su, J; Batzer, A; Sap, J

    1994-01-01

    Receptor tyrosine phosphatases (R-PTPases) have generated interest because of their suspected involvement in cellular signal transduction. The adaptor protein Grb2 has been implicated in coupling receptor tyrosine kinases to Ras. We report that a ubiquitous R-PTPase, R-PTP-alpha, is tyrosine......-phosphorylated and associated in vivo with the Grb2 protein. This association can be reproduced in stably and transiently transfected cells, as well as in vitro using recombinant Grb2 protein. Association requires the presence of an intact SH2 domain in Grb2, as well as tyrosine phosphorylation of R-PTP-alpha. This observation...... links a receptor tyrosine phosphatase with a key component of a central cellular signalling pathway and provides a basis for addressing R-PTP-alpha function....

  5. Probing the Energetics of Dynactin Filament Assembly and the Binding of Cargo Adaptor Proteins Using Molecular Dynamics Simulation and Electrostatics-Based Structural Modeling.

    Science.gov (United States)

    Zheng, Wenjun

    2017-01-10

    Dynactin, a large multiprotein complex, binds with the cytoplasmic dynein-1 motor and various adaptor proteins to allow recruitment and transportation of cellular cargoes toward the minus end of microtubules. The structure of the dynactin complex is built around an actin-like minifilament with a defined length, which has been visualized in a high-resolution structure of the dynactin filament determined by cryo-electron microscopy (cryo-EM). To understand the energetic basis of dynactin filament assembly, we used molecular dynamics simulation to probe the intersubunit interactions among the actin-like proteins, various capping proteins, and four extended regions of the dynactin shoulder. Our simulations revealed stronger intersubunit interactions at the barbed and pointed ends of the filament and involving the extended regions (compared with the interactions within the filament), which may energetically drive filament termination by the capping proteins and recruitment of the actin-like proteins by the extended regions, two key features of the dynactin filament assembly process. Next, we modeled the unknown binding configuration among dynactin, dynein tails, and a number of coiled-coil adaptor proteins (including several Bicaudal-D and related proteins and three HOOK proteins), and predicted a key set of charged residues involved in their electrostatic interactions. Our modeling is consistent with previous findings of conserved regions, functional sites, and disease mutations in the adaptor proteins and will provide a structural framework for future functional and mutational studies of these adaptor proteins. In sum, this study yielded rich structural and energetic information about dynactin and associated adaptor proteins that cannot be directly obtained from the cryo-EM structures with limited resolutions.

  6. The adaptor protein CIKS/Act1 is essential for IL-25-mediated allergic airway inflammation1

    Science.gov (United States)

    Claudio, Estefania; Sønder, Søren Ulrik; Saret, Sun; Carvalho, Gabrielle; Ramalingam, Thirumalai R; Wynn, Thomas A; Chariot, Alain; Garcia-Perganeda, Antonio; Leonardi, Antonio; Paun, Andrea; Chen, Amy; Ren, Nina Y.; Wang, Hongshan; Siebenlist, Ulrich

    2008-01-01

    IL-17 is the signature cytokine of recently discovered T helper type 17 (Th17) cells, which are prominent in defense against extracellular bacteria and fungi as well as in autoimmune diseases, such as rheumatoid arthritis and experimental autoimmune encephalomyelitis in animal models. IL-25 is a member of the IL-17 family of cytokines, but has been associated with Th2 responses instead and may negatively cross-regulate Th17/IL-17 responses. IL-25 can initiate an allergic asthma-like inflammation in the airways, which includes recruitment of eosinophils, mucus hypersecretion, Th2 cytokine production and airways hyperreactivity. We demonstrate that these effects of IL-25 are entirely dependent on the adaptor protein CIKS (a.k.a. Act1). Surprisingly, this adaptor is necessary to transmit IL-17 signals as well, despite the very distinct biologic responses these two cytokines elicit. We identify CD11c+ macrophage-like lung cells as physiologic relevant targets of IL-25 in vivo. PMID:19155511

  7. Brain-derived neurotrophic factor modulation of Kv1.3 channel is disregulated by adaptor proteins Grb10 and nShc

    Directory of Open Access Journals (Sweden)

    Marks David R

    2009-01-01

    Full Text Available Abstract Background Neurotrophins are important regulators of growth and regeneration, and acutely, they can modulate the activity of voltage-gated ion channels. Previously we have shown that acute brain-derived neurotrophic factor (BDNF activation of neurotrophin receptor tyrosine kinase B (TrkB suppresses the Shaker voltage-gated potassium channel (Kv1.3 via phosphorylation of multiple tyrosine residues in the N and C terminal aspects of the channel protein. It is not known how adaptor proteins, which lack catalytic activity, but interact with members of the neurotrophic signaling pathway, might scaffold with ion channels or modulate channel activity. Results We report the co-localization of two adaptor proteins, neuronal Src homology and collagen (nShc and growth factor receptor-binding protein 10 (Grb10, with Kv1.3 channel as demonstrated through immunocytochemical approaches in the olfactory bulb (OB neural lamina. To further explore the specificity and functional ramification of adaptor/channel co-localization, we performed immunoprecipitation and Western analysis of channel, kinase, and adaptor transfected human embryonic kidney 293 cells (HEK 293. nShc formed a direct protein-protein interaction with Kv1.3 that was independent of BDNF-induced phosphorylation of Kv1.3, whereas Grb10 did not complex with Kv1.3 in HEK 293 cells. Both adaptors, however, co-immunoprecipitated with Kv1.3 in native OB. Grb10 was interestingly able to decrease the total expression of Kv1.3, particularly at the membrane surface, and subsequently eliminated the BDNF-induced phosphorylation of Kv1.3. To examine the possibility that the Src homology 2 (SH2 domains of Grb10 were directly binding to basally phosphorylated tyrosines in Kv1.3, we utilized point mutations to substitute multiple tyrosine residues with phenylalanine. Removal of the tyrosines 111–113 and 449 prevented Grb10 from decreasing Kv1.3 expression. In the absence of either adaptor protein

  8. Brucella TIR-like protein TcpB/Btp1 specifically targets the host adaptor protein MAL/TIRAP to promote infection.

    Science.gov (United States)

    Li, Wenna; Ke, Yuehua; Wang, Yufei; Yang, Mingjuan; Gao, Junguang; Zhan, Shaoxia; Xinying, Du; Huang, Liuyu; Li, Wenfeng; Chen, Zeliang; Li, Juan

    2016-08-26

    Brucella spp. are known to avoid host immune recognition and weaken the immune response to infection. Brucella like accomplish this by employing two clever strategies, called the stealth strategy and hijacking strategy. The TIR domain-containing protein (TcpB/Btp1) of Brucella melitensis is thought to be involved in inhibiting host NF-κB activation by binding to adaptors downstream of Toll-like receptors. However, of the five TIR domain-containing adaptors conserved in mammals, whether MyD88 or MAL, even other three adaptors, are specifically targeted by TcpB has not been identified. Here, we confirmed the effect of TcpB on B.melitensis virulence in mice and found that TcpB selectively targets MAL. By using siRNA against MAL, we found that TcpB from B.melitensis is involved in intracellular survival and that MAL affects intracellular replication of B.melitensis. Our results confirm that TcpB specifically targets MAL/TIRAP to disrupt downstream signaling pathways and promote intra-host survival of Brucella spp. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. A novel method for evaluating microglial activation using ionized calcium-binding adaptor protein-1 staining : cell body to cell size ratio

    NARCIS (Netherlands)

    Hovens, Iris; Nyakas, Csaba; Schoemaker, Regina

    2014-01-01

    Aim: The aim was to validate a newly developed methodology of semi-automatic image analysis to analyze microglial morphology as marker for microglial activation in ionized calcium-binding adaptor protein-1 (IBA-1) stained brain sections. Methods: The novel method was compared to currently used

  10. Transmembrane adaptor protein TRIM regulates T cell receptor (TCR) expression and TCR-mediated signaling via an association with the TCR zeta chain

    Czech Academy of Sciences Publication Activity Database

    Kirchgesser, H.; Dietrich, J.; Scherer, J.; Isomaki, P.; Kořínek, Vladimír; Hilgert, Ivan; Bruyns, E.; Leo, A.; Cope, A. P.; Schraven, B.

    2001-01-01

    Roč. 193, č. 11 (2001), s. 1269-1283 ISSN 0022-1007 R&D Projects: GA ČR GA204/99/0367 Institutional research plan: CEZ:AV0Z5052915 Keywords : receptor * adaptor protein * signaling Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 15.340, year: 2001

  11. A dimer of the Toll-like receptor 4 cytoplasmic domain provides a specific scaffold for the recruitment of signalling adaptor proteins.

    Directory of Open Access Journals (Sweden)

    Ricardo Núñez Miguel

    2007-08-01

    Full Text Available The Toll-like receptor 4 (TLR4 is a class I transmembrane receptor expressed on the surface of immune system cells. TLR4 is activated by exposure to lipopolysaccharides derived from the outer membrane of Gram negative bacteria and forms part of the innate immune response in mammals. Like other class 1 receptors, TLR4 is activated by ligand induced dimerization, and recent studies suggest that this causes concerted conformational changes in the receptor leading to self association of the cytoplasmic Toll/Interleukin 1 receptor (TIR signalling domain. This homodimerization event is proposed to provide a new scaffold that is able to bind downstream signalling adaptor proteins. TLR4 uses two different sets of adaptors; TRAM and TRIF, and Mal and MyD88. These adaptor pairs couple two distinct signalling pathways leading to the activation of interferon response factor 3 (IRF-3 and nuclear factor kappaB (NFkappaB respectively. In this paper we have generated a structural model of the TLR4 TIR dimer and used molecular docking to probe for potential sites of interaction between the receptor homodimer and the adaptor molecules. Remarkably, both the Mal and TRAM adaptors are strongly predicted to bind at two symmetry-related sites at the homodimer interface. This model of TLR4 activation is supported by extensive functional studies involving site directed mutagenesis, inhibition by cell permeable peptides and stable protein phosphorylation of receptor and adaptor TIR domains. Our results also suggest a molecular mechanism for two recent findings, the caspase 1 dependence of Mal signalling and the protective effects conferred by the Mal polymorphism Ser180Leu.

  12. Matrilin-2, an extracellular adaptor protein, is needed for the regeneration of muscle, nerve and other tissues

    Directory of Open Access Journals (Sweden)

    Éva Korpos

    2015-01-01

    Full Text Available The extracellular matrix (ECM performs essential functions in the differentiation, maintenance and remodeling of tissues during development and regeneration, and it undergoes dynamic changes during remodeling concomitant to alterations in the cell-ECM interactions. Here we discuss recent data addressing the critical role of the widely expressed ECM protein, matrilin-2 (Matn2 in the timely onset of differentiation and regeneration processes in myogenic, neural and other tissues and in tumorigenesis. As a multiadhesion adaptor protein, it interacts with other ECM proteins and integrins. Matn2 promotes neurite outgrowth, Schwann cell migration, neuromuscular junction formation, skeletal muscle and liver regeneration and skin wound healing. Matn2 deposition by myoblasts is crucial for the timely induction of the global switch toward terminal myogenic differentiation during muscle regeneration by affecting transforming growth factor beta/bone morphogenetic protein 7/Smad and other signal transduction pathways. Depending on the type of tissue and the pathomechanism, Matn2 can also promote or suppress tumor growth.

  13. Role of adaptor proteins and clathrin in the trafficking of human kidney anion exchanger 1 (kAE1) to the cell surface.

    Science.gov (United States)

    Junking, Mutita; Sawasdee, Nunghathai; Duangtum, Natapol; Cheunsuchon, Boonyarit; Limjindaporn, Thawornchai; Yenchitsomanus, Pa-thai

    2014-07-01

    Kidney anion exchanger 1 (kAE1) plays an important role in acid-base homeostasis by mediating chloride/bicarbornate (Cl-/HCO3-) exchange at the basolateral membrane of α-intercalated cells in the distal nephron. Impaired intracellular trafficking of kAE1 caused by mutations of SLC4A1 encoding kAE1 results in kidney disease - distal renal tubular acidosis (dRTA). However, it is not known how the intracellular sorting and trafficking of kAE1 from trans-Golgi network (TGN) to the basolateral membrane occurs. Here, we studied the role of basolateral-related sorting proteins, including the mu1 subunit of adaptor protein (AP) complexes, clathrin and protein kinase D, on kAE1 trafficking in polarized and non-polarized kidney cells. By using RNA interference, co-immunoprecipitation, yellow fluorescent protein-based protein fragment complementation assays and immunofluorescence staining, we demonstrated that AP-1 mu1A, AP-3 mu1, AP-4 mu1 and clathrin (but not AP-1 mu1B, PKD1 or PKD2) play crucial roles in intracellular sorting and trafficking of kAE1. We also demonstrated colocalization of kAE1 and basolateral-related sorting proteins in human kidney tissues by double immunofluorescence staining. These findings indicate that AP-1 mu1A, AP-3 mu1, AP-4 mu1 and clathrin are required for kAE1 sorting and trafficking from TGN to the basolateral membrane of acid-secreting α-intercalated cells. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Adaptor Protein Complex 2–Mediated Endocytosis Is Crucial for Male Reproductive Organ Development in Arabidopsis[W

    Science.gov (United States)

    Kim, Soo Youn; Xu, Zheng-Yi; Song, Kyungyoung; Kim, Dae Heon; Kang, Hyangju; Reichardt, Ilka; Sohn, Eun Ju; Friml, Jiří; Juergens, Gerd; Hwang, Inhwan

    2013-01-01

    Fertilization in flowering plants requires the temporal and spatial coordination of many developmental processes, including pollen production, anther dehiscence, ovule production, and pollen tube elongation. However, it remains elusive as to how this coordination occurs during reproduction. Here, we present evidence that endocytosis, involving heterotetrameric adaptor protein complex 2 (AP-2), plays a crucial role in fertilization. An Arabidopsis thaliana mutant ap2m displays multiple defects in pollen production and viability, as well as elongation of staminal filaments and pollen tubes, all of which are pivotal processes needed for fertilization. Of these abnormalities, the defects in elongation of staminal filaments and pollen tubes were partially rescued by exogenous auxin. Moreover, DR5rev:GFP (for green fluorescent protein) expression was greatly reduced in filaments and anthers in ap2m mutant plants. At the cellular level, ap2m mutants displayed defects in both endocytosis of N-(3-triethylammonium-propyl)-4-(4-diethylaminophenylhexatrienyl) pyridinium dibromide, a lypophilic dye used as an endocytosis marker, and polar localization of auxin-efflux carrier PIN FORMED2 (PIN2) in the stamen filaments. Moreover, these defects were phenocopied by treatment with Tyrphostin A23, an inhibitor of endocytosis. Based on these results, we propose that AP-2–dependent endocytosis plays a crucial role in coordinating the multiple developmental aspects of male reproductive organs by modulating cellular auxin level through the regulation of the amount and polarity of PINs. PMID:23975898

  15. Regulation of protease-activated receptor 1 signaling by the adaptor protein complex 2 and R4 subfamily of regulator of G protein signaling proteins.

    Science.gov (United States)

    Chen, Buxin; Siderovski, David P; Neubig, Richard R; Lawson, Mark A; Trejo, Joann

    2014-01-17

    The G protein-coupled protease-activated receptor 1 (PAR1) is irreversibly proteolytically activated by thrombin. Hence, the precise regulation of PAR1 signaling is important for proper cellular responses. In addition to desensitization, internalization and lysosomal sorting of activated PAR1 are critical for the termination of signaling. Unlike most G protein-coupled receptors, PAR1 internalization is mediated by the clathrin adaptor protein complex 2 (AP-2) and epsin-1, rather than β-arrestins. However, the function of AP-2 and epsin-1 in the regulation of PAR1 signaling is not known. Here, we report that AP-2, and not epsin-1, regulates activated PAR1-stimulated phosphoinositide hydrolysis via two different mechanisms that involve, in part, a subset of R4 subfamily of "regulator of G protein signaling" (RGS) proteins. A significantly greater increase in activated PAR1 signaling was observed in cells depleted of AP-2 using siRNA or in cells expressing a PAR1 (420)AKKAA(424) mutant with defective AP-2 binding. This effect was attributed to AP-2 modulation of PAR1 surface expression and efficiency of G protein coupling. We further found that ectopic expression of R4 subfamily members RGS2, RGS3, RGS4, and RGS5 reduced activated PAR1 wild-type signaling, whereas signaling by the PAR1 AKKAA mutant was minimally affected. Intriguingly, siRNA-mediated depletion analysis revealed a function for RGS5 in the regulation of signaling by the PAR1 wild type but not the AKKAA mutant. Moreover, activation of the PAR1 wild type, and not the AKKAA mutant, induced Gαq association with RGS3 via an AP-2-dependent mechanism. Thus, AP-2 regulates activated PAR1 signaling by altering receptor surface expression and through recruitment of RGS proteins.

  16. A transgenic Drosophila model demonstrates that the Helicobacter pylori CagA protein functions as a eukaryotic Gab adaptor.

    Directory of Open Access Journals (Sweden)

    Crystal M Botham

    2008-05-01

    Full Text Available Infection with the human gastric pathogen Helicobacter pylori is associated with a spectrum of diseases including gastritis, peptic ulcers, gastric adenocarcinoma, and gastric mucosa-associated lymphoid tissue lymphoma. The cytotoxin-associated gene A (CagA protein of H. pylori, which is translocated into host cells via a type IV secretion system, is a major risk factor for disease development. Experiments in gastric tissue culture cells have shown that once translocated, CagA activates the phosphatase SHP-2, which is a component of receptor tyrosine kinase (RTK pathways whose over-activation is associated with cancer formation. Based on CagA's ability to activate SHP-2, it has been proposed that CagA functions as a prokaryotic mimic of the eukaryotic Grb2-associated binder (Gab adaptor protein, which normally activates SHP-2. We have developed a transgenic Drosophila model to test this hypothesis by investigating whether CagA can function in a well-characterized Gab-dependent process: the specification of photoreceptors cells in the Drosophila eye. We demonstrate that CagA expression is sufficient to rescue photoreceptor development in the absence of the Drosophila Gab homologue, Daughter of Sevenless (DOS. Furthermore, CagA's ability to promote photoreceptor development requires the SHP-2 phosphatase Corkscrew (CSW. These results provide the first demonstration that CagA functions as a Gab protein within the tissue of an organism and provide insight into CagA's oncogenic potential. Since many translocated bacterial proteins target highly conserved eukaryotic cellular processes, such as the RTK signaling pathway, the transgenic Drosophila model should be of general use for testing the in vivo function of bacterial effector proteins and for identifying the host genes through which they function.

  17. The Adaptor Protein SAP Directly Associates with CD3ζ Chain and Regulates T Cell Receptor Signaling

    Science.gov (United States)

    Proust, Richard; Bertoglio, Jacques; Gesbert, Franck

    2012-01-01

    Mutations altering the gene encoding the SLAM associated protein (SAP) are responsible for the X-linked lymphoproliferative disease or XLP1. Its absence is correlated with a defective NKT cells development, a decrease in B cell functions and a reduced T cells and NK cells cytotoxic activities, thus leading to an immunodeficiency syndrome. SAP is a small 128 amino-acid long protein that is almost exclusively composed of an SH2 domain. It has been shown to interact with the CD150/SLAM family of receptors, and in a non-canonical manner with SH3 containing proteins such as Fyn, βPIX, PKCθ and Nck1. It would thus play the role of a minimal adaptor protein. It has been shown that SAP plays an important function in the activation of T cells through its interaction with the SLAM family of receptors. Therefore SAP defective T cells display a reduced activation of signaling events downstream of the TCR-CD3 complex triggering. In the present work, we evidence that SAP is a direct interactor of the CD3ζ chain. This direct interaction occurs through the first ITAM of CD3ζ, proximal to the membrane. Additionally, we show that, in the context of the TCR-CD3 signaling, an Sh-RNA mediated silencing of SAP is responsible for a decrease of several canonical T cell signaling pathways including Erk, Akt and PLCγ1 and to a reduced induction of IL-2 and IL-4 mRNA. Altogether, we show that SAP plays a central function in the T cell activation processes through a direct association with the CD3 complex. PMID:22912825

  18. The CRKL gene encoding an adaptor protein is amplified, overexpressed, and a possible therapeutic target in gastric cancer

    Directory of Open Access Journals (Sweden)

    Natsume Hiroko

    2012-07-01

    Full Text Available Abstract Background Genomic DNA amplification is a genetic factor involved in cancer, and some oncogenes, such as ERBB2, are highly amplified in gastric cancer. We searched for the possible amplification of other genes in gastric cancer. Methods and Results A genome-wide single nucleotide polymorphism microarray analysis was performed using three cell lines of differentiated gastric cancers, and 22 genes (including ERBB2 in five highly amplified chromosome regions (with a copy number of more than 6 were identified. Particular attention was paid to the CRKL gene, the product of which is an adaptor protein containing Src homology 2 and 3 (SH2/SH3 domains. An extremely high CRKL copy number was confirmed in the MKN74 gastric cancer cell line using fluorescence in situ hybridization (FISH, and a high level of CRKL expression was also observed in the cells. The RNA-interference-mediated knockdown of CRKL in MKN74 disclosed the ability of CRKL to upregulate gastric cell proliferation. An immunohistochemical analysis revealed that CRKL protein was overexpressed in 24.4% (88/360 of the primary gastric cancers that were analyzed. The CRKL copy number was also examined in 360 primary gastric cancers using a FISH analysis, and CRKL amplification was found to be associated with CRKL overexpression. Finally, we showed that MKN74 cells with CRKL amplification were responsive to the dual Src/BCR-ABL kinase inhibitor BMS354825, likely via the inhibition of CRKL phosphorylation, and that the proliferation of MKN74 cells was suppressed by treatment with a CRKL-targeting peptide. Conclusion These results suggested that CRKL protein is overexpressed in a subset of gastric cancers and is associated with CRKL amplification in gastric cancer. Furthermore, our results suggested that CRKL protein has the ability to regulate gastric cell proliferation and has the potential to serve as a molecular therapy target for gastric cancer.

  19. The Role of Crk Adaptor Proteins in Breast Tumorigenesis and Bone Metastasis

    Science.gov (United States)

    2012-09-01

    phosphoinositide 3-kinase; PR: progesterone receptor; Rac1: ras-related C3 botulinum toxin substrate 1; RLU: relative luciferase units; RNAi: RNA...which Crk proteins are overexpressed.   11   The molecular events contributing to development of the basal subtype of breast cancer are still...the structural organization of Crk proteins. Despite a high degree of homology within the functional domains of CrkII and CrkL, the linker regions

  20. The Fanconi anemia gene product FANCF is a flexible adaptor protein.

    NARCIS (Netherlands)

    Leveille, F.; Blom, E.; Medhurst, A.L. dr.; Bier, P; Laghmani, elH; Johnson, M.; Rooimans, M.A.; Sobeck, A; Waisfisz, Q.; Arwert, F.; Patel, KJ; Hoatlin, M.E.; Joenje, H.; Winter, de J.P.

    2004-01-01

    The Fanconi anemia (FA) protein FANCF is an essential component of a nuclear core complex that protects the genome against chromosomal instability, but the specific function of FANCF is still poorly understood. Based upon the homology between human and Xenopus laevis FANCF, we carried out an

  1. The interaction between the adaptor protein APS and Enigma is involved in actin organisation

    DEFF Research Database (Denmark)

    Barres, Romain; Gonzalez, Teresa; Le Marchand-Brustel, Yannick

    2005-01-01

    that was previously shown to be associated with the actin cytoskeleton. In HEK 293 cells, Enigma interacted specifically with APS, but not with the APS-related protein SH2-B. This interaction required the NPTY motif of APS and the LIM domains of Enigma. In NIH-3T3 cells that express the insulin receptor, Enigma...... cytoskeleton organisation, expression of Enigma alone led to the formation of F-actin clusters. Similar alteration in actin cytoskeleton organisation was observed in cells expressing both Enigma and APS with a mutation in the NPTY motif. These results identify Enigma as a novel APS-binding protein and suggest...... that the APS/Enigma complex plays a critical role in actin cytoskeleton organisation....

  2. Crystal Structures and Thermodynamic Analysis Reveal Distinct Mechanisms of CD28 Phosphopeptide Binding to the Src Homology 2 (SH2) Domains of Three Adaptor Proteins*

    Science.gov (United States)

    Inaba, Satomi; Numoto, Nobutaka; Ogawa, Shuhei; Morii, Hisayuki; Ikura, Teikichi; Abe, Ryo; Ito, Nobutoshi; Oda, Masayuki

    2017-01-01

    Full activation of T cells and differentiation into effector T cells are essential for many immune responses and require co-stimulatory signaling via the CD28 receptor. Extracellular ligand binding to CD28 recruits protein-tyrosine kinases to its cytoplasmic tail, which contains a YMNM motif. Following phosphorylation of the tyrosine, the proteins growth factor receptor-bound protein 2 (Grb2), Grb2-related adaptor downstream of Shc (Gads), and p85 subunit of phosphoinositide 3-kinase may bind to pYMNM (where pY is phosphotyrosine) via their Src homology 2 (SH2) domains, leading to downstream signaling to distinct immune pathways. These three adaptor proteins bind to the same site on CD28 with variable affinity, and all are important for CD28-mediated co-stimulatory function. However, the mechanism of how these proteins recognize and compete for CD28 is unclear. To visualize their interactions with CD28, we have determined the crystal structures of Gads SH2 and two p85 SH2 domains in complex with a CD28-derived phosphopeptide. The high resolution structures obtained revealed that, whereas the CD28 phosphopeptide bound to Gads SH2 is in a bent conformation similar to that when bound to Grb2 SH2, it adopts a more extended conformation when bound to the N- and C-terminal SH2 domains of p85. These differences observed in the peptide-protein interactions correlated well with the affinity and other thermodynamic parameters for each interaction determined by isothermal titration calorimetry. The detailed insight into these interactions reported here may inform the development of compounds that specifically inhibit the association of CD28 with these adaptor proteins to suppress excessive T cell responses, such as in allergies and autoimmune diseases. PMID:27927989

  3. Crystal Structures and Thermodynamic Analysis Reveal Distinct Mechanisms of CD28 Phosphopeptide Binding to the Src Homology 2 (SH2) Domains of Three Adaptor Proteins.

    Science.gov (United States)

    Inaba, Satomi; Numoto, Nobutaka; Ogawa, Shuhei; Morii, Hisayuki; Ikura, Teikichi; Abe, Ryo; Ito, Nobutoshi; Oda, Masayuki

    2017-01-20

    Full activation of T cells and differentiation into effector T cells are essential for many immune responses and require co-stimulatory signaling via the CD28 receptor. Extracellular ligand binding to CD28 recruits protein-tyrosine kinases to its cytoplasmic tail, which contains a YMNM motif. Following phosphorylation of the tyrosine, the proteins growth factor receptor-bound protein 2 (Grb2), Grb2-related adaptor downstream of Shc (Gads), and p85 subunit of phosphoinositide 3-kinase may bind to pYMNM (where pY is phosphotyrosine) via their Src homology 2 (SH2) domains, leading to downstream signaling to distinct immune pathways. These three adaptor proteins bind to the same site on CD28 with variable affinity, and all are important for CD28-mediated co-stimulatory function. However, the mechanism of how these proteins recognize and compete for CD28 is unclear. To visualize their interactions with CD28, we have determined the crystal structures of Gads SH2 and two p85 SH2 domains in complex with a CD28-derived phosphopeptide. The high resolution structures obtained revealed that, whereas the CD28 phosphopeptide bound to Gads SH2 is in a bent conformation similar to that when bound to Grb2 SH2, it adopts a more extended conformation when bound to the N- and C-terminal SH2 domains of p85. These differences observed in the peptide-protein interactions correlated well with the affinity and other thermodynamic parameters for each interaction determined by isothermal titration calorimetry. The detailed insight into these interactions reported here may inform the development of compounds that specifically inhibit the association of CD28 with these adaptor proteins to suppress excessive T cell responses, such as in allergies and autoimmune diseases. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  4. The adaptor protein alpha-syntrophin regulates adipocyte lipid droplet growth

    Energy Technology Data Exchange (ETDEWEB)

    Eisinger, Kristina; Rein-Fischboeck, Lisa; Pohl, Rebekka; Meier, Elisabeth M.; Krautbauer, Sabrina; Buechler, Christa, E-mail: christa.buechler@klinik.uni-regensburg.de

    2016-07-01

    The scaffold protein alpha-syntrophin (SNTA) regulates lipolysis indicating a role in lipid homeostasis. Adipocytes are the main lipid storage cells in the body, and here, the function of SNTA has been analyzed in 3T3-L1 cells. SNTA is expressed in preadipocytes and is induced early during adipogenesis. Knock-down of SNTA in preadipocytes increases their proliferation. Proteins which are induced during adipogenesis like adiponectin and caveolin-1, and the inflammatory cytokine IL-6 are at normal levels in the mature cells differentiated from preadipocytes with low SNTA. This suggests that SNTA does neither affect differentiation nor inflammation. Expression of proteins with a role in cholesterol and triglyceride homeostasis is unchanged. Consequently, basal and epinephrine induced lipolysis as well as insulin stimulated phosphorylation of Akt and ERK1/2 are normal. Importantly, adipocytes with low SNTA form smaller lipid droplets and store less triglycerides. Stearoyl-CoA reductase and MnSOD are reduced upon SNTA knock-down but do not contribute to lower lipid levels. Oleate uptake is even increased in cells with SNTA knock-down. In summary, current data show that SNTA is involved in the expansion of lipid droplets independent of adipogenesis. Enhanced preadipocyte proliferation and capacity to store surplus fatty acids may protect adipocytes with low SNTA from lipotoxicity in obesity. - Highlights: • Alpha-syntrophin (SNTA) is expressed in 3T3-L1adipocytes. • SNTA knock-down in preadipocytes has no effect on adipogenesis. • Mature 3T3-L1 differentiated from cells with low SNTA form small lipid droplets. • SCD1 and MnSOD are reduced in adipocytes with low SNTA. • SCD1 knock-down does not alter triglyceride levels.

  5. Protein kinase A-induced internalization of Slack channels from the neuronal membrane occurs by adaptor protein-2/clathrin-mediated endocytosis.

    Science.gov (United States)

    Gururaj, Sushmitha; Evely, Katherine M; Pryce, Kerri D; Li, Jun; Qu, Jun; Bhattacharjee, Arin

    2017-11-24

    The sodium-activated potassium (K Na ) channel Kcnt1 (Slack) is abundantly expressed in nociceptor (pain-sensing) neurons of the dorsal root ganglion (DRG), where they transmit the large outward conductance I KNa and arbitrate membrane excitability. Slack channel expression at the DRG membrane is necessary for their characteristic firing accommodation during maintained stimulation, and reduced membrane channel density causes hyperexcitability. We have previously shown that in a pro-inflammatory state, a decrease in membrane channel expression leading to reduced Slack-mediated I KNa expression underlies DRG neuronal sensitization. An important component of the inflammatory milieu, PKA internalizes Slack channels from the DRG membrane, reduces I KNa , and produces DRG neuronal hyperexcitability when activated in cultured primary DRG neurons. Here, we show that this PKA-induced retrograde trafficking of Slack channels also occurs in intact spinal cord slices and that it is carried out by adaptor protein-2 (AP-2) via clathrin-mediated endocytosis. We provide mass spectrometric and biochemical evidence of an association of native neuronal AP-2 adaptor proteins with Slack channels, facilitated by a dileucine motif housed in the cytoplasmic Slack C terminus that binds AP-2. By creating a competitive peptide blocker of AP-2-Slack binding, we demonstrated that this interaction is essential for clathrin recruitment to the DRG membrane, Slack channel endocytosis, and DRG neuronal hyperexcitability after PKA activation. Together, these findings uncover AP-2 and clathrin as players in Slack channel regulation. Given the significant role of Slack in nociceptive neuronal excitability, the AP-2 clathrin-mediated endocytosis trafficking mechanism may enable targeting of peripheral and possibly, central neuronal sensitization. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  6. Multiple regulatory roles of the mouse transmembrane adaptor protein NTAL in gene transcription and mast cell physiology.

    Directory of Open Access Journals (Sweden)

    Iva Polakovicova

    Full Text Available Non-T cell activation linker (NTAL; also called LAB or LAT2 is a transmembrane adaptor protein that is expressed in a subset of hematopoietic cells, including mast cells. There are conflicting reports on the role of NTAL in the high affinity immunoglobulin E receptor (FcεRI signaling. Studies carried out on mast cells derived from mice with NTAL knock out (KO and wild type mice suggested that NTAL is a negative regulator of FcεRI signaling, while experiments with RNAi-mediated NTAL knockdown (KD in human mast cells and rat basophilic leukemia cells suggested its positive regulatory role. To determine whether different methodologies of NTAL ablation (KO vs KD have different physiological consequences, we compared under well defined conditions FcεRI-mediated signaling events in mouse bone marrow-derived mast cells (BMMCs with NTAL KO or KD. BMMCs with both NTAL KO and KD exhibited enhanced degranulation, calcium mobilization, chemotaxis, tyrosine phosphorylation of LAT and ERK, and depolymerization of filamentous actin. These data provide clear evidence that NTAL is a negative regulator of FcεRI activation events in murine BMMCs, independently of possible compensatory developmental alterations. To gain further insight into the role of NTAL in mast cells, we examined the transcriptome profiles of resting and antigen-activated NTAL KO, NTAL KD, and corresponding control BMMCs. Through this analysis we identified several genes that were differentially regulated in nonactivated and antigen-activated NTAL-deficient cells, when compared to the corresponding control cells. Some of the genes seem to be involved in regulation of cholesterol-dependent events in antigen-mediated chemotaxis. The combined data indicate multiple regulatory roles of NTAL in gene expression and mast cell physiology.

  7. New function of the adaptor protein SH2B1 in brain-derived neurotrophic factor-induced neurite outgrowth.

    Directory of Open Access Journals (Sweden)

    Chien-Hung Shih

    Full Text Available Neurite outgrowth is an essential process for the establishment of the nervous system. Brain-derived neurotrophic factor (BDNF binds to its receptor TrkB and regulates axonal and dendritic morphology of neurons through signal transduction and gene expression. SH2B1 is a signaling adaptor protein that regulates cellular signaling in various physiological processes. The purpose of this study is to investigate the role of SH2B1 in the development of the central nervous system. In this study, we show that knocking down SH2B1 reduces neurite formation of cortical neurons whereas overexpression of SH2B1β promotes the development of hippocampal neurons. We further demonstrate that SH2B1β promotes BDNF-induced neurite outgrowth and signaling using the established PC12 cells stably expressing TrkB, SH2B1β or SH2B1β mutants. Our data indicate that overexpressing SH2B1β enhances BDNF-induced MEK-ERK1/2, and PI3K-AKT signaling pathways. Inhibition of MEK-ERK1/2 and PI3K-AKT pathways by specific inhibitors suggest that these two pathways are required for SH2B1β-promoted BDNF-induced neurite outgrowth. Moreover, SH2B1β enhances BDNF-stimulated phosphorylation of signal transducer and activator of transcription 3 at serine 727. Finally, our data indicate that the SH2 domain and tyrosine phosphorylation of SH2B1β contribute to BDNF-induced signaling pathways and neurite outgrowth. Taken together, these findings demonstrate that SH2B1β promotes BDNF-induced neurite outgrowth through enhancing pathways involved MEK-ERK1/2 and PI3K-AKT.

  8. Downregulation of adaptor protein MyD88 compromises the angiogenic potential of B16 murine melanoma

    Science.gov (United States)

    Araya, Paula; Nuñez, Nicolás Gonzalo; Mena, Hebe Agustina; Bocco, José Luis; Negrotto, Soledad; Maccioni, Mariana

    2017-01-01

    The mechanisms that link inflammatory responses to cancer development remain a subject of intense investigation, emphasizing the need to better understand the cellular and molecular pathways that create a tumor promoting microenvironment. The myeloid differentiation primary response protein MyD88 acts as a main adaptor molecule for the signaling cascades initiated from Toll-like receptors (TLRs) and the interleukin 1 receptor (IL-1R). MyD88 has been shown to contribute to tumorigenesis in many inflammation-associated cancer models. In this study, we sought to better define the role of MyD88 in neoplastic cells using a murine melanoma model. Herein, we have demonstrated that MyD88 expression is required to maintain the angiogenic switch that supports B16 melanoma growth. By knocking down MyD88 we reduced TLR-mediated NF-κB activation with no evident effects over cell proliferation and survival. In addition, MyD88 downregulation was associated with a decrease of HIF1α levels and its target gene VEGF, in correlation with an impaired capability to induce capillary sprouting and tube formation of endothelial cells. Melanomas developed from cells lacking MyD88 showed an enhanced secretion of chemoattractant ligands such as CCL2, CXCL10 and CXCL1 and have an improved infiltration of macrophages to the tumor site. Our results imply that cell-autonomous signaling through MyD88 is required to sustain tumor growth and underscore its function as an important positive modulator of tumor angiogenesis. PMID:28662055

  9. Localization of the kinesin adaptor proteins trafficking kinesin proteins 1 and 2 in primary cultures of hippocampal pyramidal and cortical neurons.

    Science.gov (United States)

    Loss, Omar; Stephenson, F Anne

    2015-07-01

    Neuronal function requires regulated anterograde and retrograde trafficking of mitochondria along microtubules by using the molecular motors kinesin and dynein. Previous work has established that trafficking kinesin proteins (TRAKs),TRAK1 and TRAK2, are kinesin adaptor proteins that link mitochondria to kinesin motor proteins via an acceptor protein in the mitochondrial outer membrane, etc. the Rho GTPase Miro. Recent studies have shown that TRAK1 preferentially controls mitochondrial transport in axons of hippocampal neurons by virtue of its binding to both kinesin and dynein motor proteins, whereas TRAK2 controls mitochondrial transport in dendrites resulting from its binding to dynein. This study further investigates the subcellular localization of TRAK1 and TRAK2 in primary cultures of hippocampal and cortical neurons by using both commercial antibodies and anti-TRAK1 and anti-TRAK2 antibodies raised in our own laboratory (in-house). Whereas TRAK1 was prevalently localized in axons of hippocampal and cortical neurons, TRAK2 was more prevalent in dendrites of hippocampal neurons. In cortical neurons, TRAK2 was equally distributed between axons and dendrites. Some qualitative differences were observed between commercial and in-house-generated antibody immunostaining. © 2015 Wiley Periodicals, Inc.

  10. Adaptor proteins intersectin 1 and 2 bind similar proline-rich ligands but are differentially recognized by SH2 domain-containing proteins.

    Directory of Open Access Journals (Sweden)

    Olga Novokhatska

    Full Text Available BACKGROUND: Scaffolding proteins of the intersectin (ITSN family, ITSN1 and ITSN2, are crucial for the initiation stage of clathrin-mediated endocytosis. These proteins are closely related but have implications in distinct pathologies. To determine how these proteins could be separated in certain cell pathways we performed a comparative study of ITSNs. METHODOLOGY/PRINCIPAL FINDINGS: We have shown that endogenous ITSN1 and ITSN2 colocalize and form a complex in cells. A structural comparison of five SH3 domains, which mediated most ITSNs protein-protein interactions, demonstrated a similarity of their ligand-binding sites. We showed that the SH3 domains of ITSN2 bound well-established interactors of ITSN1 as well as newly identified ITSNs protein partners. A search for a novel interacting interface revealed multiple tyrosines that could be phosphorylated in ITSN2. Phosphorylation of ITSN2 isoforms but not ITSN1 short isoform was observed in various cell lines. EGF stimulation of HeLa cells enhanced tyrosine phosphorylation of ITSN2 isoforms and enabled their recognition by the SH2 domains of the Fyn, Fgr and Abl1 kinases, the regulatory subunit of PI3K, the adaptor proteins Grb2 and Crk, and phospholipase C gamma. The SH2 domains mentioned were unable to bind ITSN1 short isoform. CONCLUSIONS/SIGNIFICANCE: Our results indicate that during evolution of vertebrates ITSN2 acquired a novel protein-interaction interface that allows its specific recognition by the SH2 domains of signaling proteins. We propose that these data could be important to understand the functional diversity of paralogous ITSN proteins.

  11. Adaptor protein containing PH domain, PTB domain and leucine zipper (APPL1) regulates the protein level of EGFR by modulating its trafficking

    International Nuclear Information System (INIS)

    Lee, Jae-Rin; Hahn, Hwa-Sun; Kim, Young-Hoon; Nguyen, Hong-Hoa; Yang, Jun-Mo; Kang, Jong-Sun; Hahn, Myong-Joon

    2011-01-01

    Highlights: ► APPL1 regulates the protein level of EGFR in response to EGF stimulation. ► Depletion of APPL1 accelerates the movement of EGF/EGFR from the cell surface to the perinuclear region in response to EGF. ► Knockdown of APPL1 enhances the activity of Rab5. -- Abstract: The EGFR-mediated signaling pathway regulates multiple biological processes such as cell proliferation, survival and differentiation. Previously APPL1 (adaptor protein containing PH domain, PTB domain and leucine zipper 1) has been reported to function as a downstream effector of EGF-initiated signaling. Here we demonstrate that APPL1 regulates EGFR protein levels in response to EGF stimulation. Overexpression of APPL1 enhances EGFR stabilization while APPL1 depletion by siRNA reduces EGFR protein levels. APPL1 depletion accelerates EGFR internalization and movement of EGF/EGFR from cell surface to the perinuclear region in response to EGF treatment. Conversely, overexpression of APPL1 decelerates EGFR internalization and translocation of EGF/EGFR to the perinuclear region. Furthermore, APPL1 depletion enhances the activity of Rab5 which is involved in internalization and trafficking of EGFR and inhibition of Rab5 in APPL1-depleted cells restored EGFR levels. Consistently, APPL1 depletion reduced activation of Akt, the downstream signaling effector of EGFR and this is restored by inhibition of Rab5. These findings suggest that APPL1 is required for EGFR signaling by regulation of EGFR stabilities through inhibition of Rab5.

  12. Adaptor Protein Complex-2 (AP-2) and Epsin-1 Mediate Protease-activated Receptor-1 Internalization via Phosphorylation- and Ubiquitination-dependent Sorting Signals*

    Science.gov (United States)

    Chen, Buxin; Dores, Michael R.; Grimsey, Neil; Canto, Isabel; Barker, Breann L.; Trejo, JoAnn

    2011-01-01

    Signaling by protease-activated receptor-1 (PAR1), a G protein-coupled receptor (GPCR) for thrombin, is regulated by desensitization and internalization. PAR1 desensitization is mediated by β-arrestins, like most classic GPCRs. In contrast, internalization of PAR1 occurs through a clathrin- and dynamin-dependent pathway independent of β-arrestins. PAR1 displays two modes of internalization. Constitutive internalization of unactivated PAR1 is mediated by the clathrin adaptor protein complex-2 (AP-2), where the μ2-adaptin subunit binds directly to a tyrosine-based motif localized within the receptor C-tail domain. However, AP-2 depletion only partially inhibits agonist-induced internalization of PAR1, suggesting a function for other clathrin adaptors in this process. Here, we now report that AP-2 and epsin-1 are both critical mediators of agonist-stimulated PAR1 internalization. We show that ubiquitination of PAR1 and the ubiquitin-interacting motifs of epsin-1 are required for epsin-1-dependent internalization of activated PAR1. In addition, activation of PAR1 promotes epsin-1 de-ubiquitination, which may increase its endocytic adaptor activity to facilitate receptor internalization. AP-2 also regulates activated PAR1 internalization via recognition of distal C-tail phosphorylation sites rather than the canonical tyrosine-based motif. Thus, AP-2 and epsin-1 are both required to promote efficient internalization of activated PAR1 and recognize discrete receptor sorting signals. This study defines a new pathway for internalization of mammalian GPCRs. PMID:21965661

  13. Shc adaptor proteins are key transducers of mitogenic signaling mediated by the G protein-coupled thrombin receptor

    DEFF Research Database (Denmark)

    Chen, Y; Grall, D; Salcini, A E

    1996-01-01

    The serine protease thrombin activates G protein signaling systems that lead to Ras activation and, in certain cells, proliferation. Whereas the steps leading to Ras activation by G protein-coupled receptors are not well defined, the mechanisms of Ras activation by receptor tyrosine kinases have......-insensitive proteins appear to mediate this effect, since (i) pertussis toxin pre-treatment of cells does not blunt the action of thrombin and (ii) Shc phosphorylation on tyrosine can be stimulated by the muscarinic m1 receptor. Shc phosphorylation does not appear to involve protein kinase C, since the addition of 4...

  14. Adaptor protein complex 2-mediated, clathrin-dependent endocytosis, and related gene activities, are a prominent feature during maturation stage amelogenesis.

    Science.gov (United States)

    Lacruz, Rodrigo S; Brookes, Steven J; Wen, Xin; Jimenez, Jaime M; Vikman, Susanna; Hu, Ping; White, Shane N; Lyngstadaas, S Petter; Okamoto, Curtis T; Smith, Charles E; Paine, Michael L

    2013-03-01

    Molecular events defining enamel matrix removal during amelogenesis are poorly understood. Early reports have suggested that adaptor proteins (AP) participate in ameloblast-mediated endocytosis. Enamel formation involves the secretory and maturation stages, with an increase in resorptive function during the latter. Here, using real-time PCR, we show that the expression of clathrin and adaptor protein subunits are upregulated in maturation stage rodent enamel organ cells. AP complex 2 (AP-2) is the most upregulated of the four distinct adaptor protein complexes. Immunolocalization confirms the presence of AP-2 and clathrin in ameloblasts, with strongest reactivity at the apical pole. These data suggest that the resorptive functions of enamel cells involve AP-2 mediated, clathrin-dependent endocytosis, thus implying the likelihood of specific membrane-bound receptor(s) of enamel matrix protein debris. The mRNA expression of other endocytosis-related gene products is also upregulated during maturation including: lysosomal-associated membrane protein 1 (Lamp1); cluster of differentiation 63 and 68 (Cd63 and Cd68); ATPase, H(+) transporting, lysosomal V0 subunit D2 (Atp6v0d2); ATPase, H(+) transporting, lysosomal V1 subunit B2 (Atp6v1b2); chloride channel, voltage-sensitive 7 (Clcn7); and cathepsin K (Ctsk). Immunohistologic data confirms the expression of a number of these proteins in maturation stage ameloblasts. The enamel of Cd63-null mice was also examined. Despite increased mRNA and protein expression in the enamel organ during maturation, the enamel of Cd63-null mice appeared normal. This may suggest inherent functional redundancies between Cd63 and related gene products, such as Lamp1 and Cd68. Ameloblast-like LS8 cells treated with the enamel matrix protein complex Emdogain showed upregulation of AP-2 and clathrin subunits, further supporting the existence of a membrane-bound receptor-regulated pathway for the endocytosis of enamel matrix proteins. These data

  15. Adaptor Protein Complex 2 (AP-2) Mediated, Clathrin Dependent Endocytosis, And Related Gene Activities, Are A Prominent Feature During Maturation Stage Amelogenesis

    Science.gov (United States)

    LACRUZ, Rodrigo S.; BROOKES, Steven J.; WEN, Xin; JIMENEZ, Jaime M.; VIKMAN, Susanna; HU, Ping; WHITE, Shane N.; LYNGSTADAAS, S. Petter; OKAMOTO, Curtis T.; SMITH, Charles E.; PAINE, Michael L.

    2012-01-01

    Molecular events defining enamel matrix removal during amelogenesis are poorly understood. Early reports have suggested that adaptor proteins (AP) participate in ameloblast-mediated endocytosis. Enamel formation involves the secretory and maturation stages, with an increase in resorptive function during the latter. Here, using real time PCR, we show that the expression of clathrin and adaptor protein subunits are up-regulated in maturation stage rodent enamel organ cells. AP-2 is the most up-regulated of the four distinct adaptor protein complexes. Immunolocalization confirms the presence of AP-2 and clathrin in ameloblasts with strongest reactivity at the apical pole. These data suggest that the resorptive functions of enamel cells involve AP-2 mediated, clathrin dependent endocytosis, thus implying the likelihood of a specific membrane-bound receptor(s) of enamel matrix protein debris. The mRNA expression of other endocytosis-related gene products is also up-regulated during maturation including: lysosomal-associated membrane protein 1 (Lamp1), cluster of differentiation 63 and 68 (Cd63 and Cd68), ATPase, H+ transporting, lysosomal V0 subunit D2 (Atp6v0d2), ATPase, H+ transporting, lysosomal V1 subunit B2 (Atp6v1b2), chloride channel, voltage-sensitive 7 (Clcn7) and cathepsin K (Ctsk). Immunohistological data confirms the expression of a number of these proteins in maturation stage ameloblasts. The enamel of Cd63-null mice was also examined. Despite increased mRNA and protein expression in the enamel organ during maturation, the enamel of Cd63-null mice appeared normal. This may suggest inherent functional redundancies between Cd63 and related gene products, such as Lamp1 and Cd68. Ameloblast-like LS8 cells treated with the enamel matrix protein complex Emdogain® showed up-regulation of AP-2 and clathrin subunits, further supporting the existence of a membrane-bound receptor regulated pathway for the endocytosis of enamel matrix proteins. These data together

  16. The AP2 clathrin adaptor protein complex regulates the abundance of GLR-1 glutamate receptors in the ventral nerve cord of Caenorhabditis elegans.

    Science.gov (United States)

    Garafalo, Steven D; Luth, Eric S; Moss, Benjamin J; Monteiro, Michael I; Malkin, Emily; Juo, Peter

    2015-05-15

    Regulation of glutamate receptor (GluR) abundance at synapses by clathrin-mediated endocytosis can control synaptic strength and plasticity. We take advantage of viable, null mutations in subunits of the clathrin adaptor protein 2 (AP2) complex in Caenorhabditis elegans to characterize the in vivo role of AP2 in GluR trafficking. In contrast to our predictions for an endocytic adaptor, we found that levels of the GluR GLR-1 are decreased at synapses in the ventral nerve cord (VNC) of animals with mutations in the AP2 subunits APM-2/μ2, APA-2/α, or APS-2/σ2. Rescue experiments indicate that APM-2/μ2 functions in glr-1-expressing interneurons and the mature nervous system to promote GLR-1 levels in the VNC. Genetic analyses suggest that APM-2/μ2 acts upstream of GLR-1 endocytosis in the VNC. Consistent with this, GLR-1 accumulates in cell bodies of apm-2 mutants. However, GLR-1 does not appear to accumulate at the plasma membrane of the cell body as expected, but instead accumulates in intracellular compartments including Syntaxin-13- and RAB-14-labeled endosomes. This study reveals a novel role for the AP2 clathrin adaptor in promoting the abundance of GluRs at synapses in vivo, and implicates AP2 in the regulation of GluR trafficking at an early step in the secretory pathway. © 2015 Garafalo et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  17. WW domain-binding protein 2: an adaptor protein closely linked to the development of breast cancer.

    Science.gov (United States)

    Chen, Shuai; Wang, Han; Huang, Yu-Fan; Li, Ming-Li; Cheng, Jiang-Hong; Hu, Peng; Lu, Chuan-Hui; Zhang, Ya; Liu, Na; Tzeng, Chi-Meng; Zhang, Zhi-Ming

    2017-07-19

    The WW domain is composed of 38 to 40 semi-conserved amino acids shared with structural, regulatory, and signaling proteins. WW domain-binding protein 2 (WBP2), as a binding partner of WW domain protein, interacts with several WW-domain-containing proteins, such as Yes kinase-associated protein (Yap), paired box gene 8 (Pax8), WW-domain-containing transcription regulator protein 1 (TAZ), and WW-domain-containing oxidoreductase (WWOX) through its PPxY motifs within C-terminal region, and further triggers the downstream signaling pathway in vitro and in vivo. Studies have confirmed that phosphorylated form of WBP2 can move into nuclei and activate the transcription of estrogen receptor (ER) and progesterone receptor (PR), whose expression were the indicators of breast cancer development, indicating that WBP2 may participate in the progression of breast cancer. Both overexpression of WBP2 and activation of tyrosine phosphorylation upregulate the signal cascades in the cross-regulation of the Wnt and ER signaling pathways in breast cancer. Following the binding of WBP2 to the WW domain region of TAZ which can accelerate migration, invasion and is required for the transformed phenotypes of breast cancer cells, the transformation of epithelial to mesenchymal of MCF10A is activated, suggesting that WBP2 is a key player in regulating cell migration. When WBP2 binds with WWOX, a tumor suppressor, ER transactivation and tumor growth can be suppressed. Thus, WBP2 may serve as a molecular on/off switch that controls the crosstalk between E2, WWOX, Wnt, TAZ, and other oncogenic signaling pathways. This review interprets the relationship between WBP2 and breast cancer, and provides comprehensive views about the function of WBP2 in the regulation of the pathogenesis of breast cancer and endocrine therapy in breast cancer treatment.

  18. RECOMBINANT FLUORESCENT SENSOR OF HYDROGEN PEROXIDE HyPer FUSED WITH ADAPTOR PROTEIN Ruk/CIN85: DESIGNING OF EXPRESSION VECTOR AND ITS FUNCTIONAL CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    А. V. Bazalii

    2015-10-01

    Full Text Available The aim of this study was to design the expression vector encoding fluorescent sensor of hydrogen peroxide HyPer fused with adaptor protein Ruk/CIN85 as well as to check its subcellular distribution and ability to sense hydrogen peroxide. It was demonstrated that in transiently transfected HEK293 and MCF-7 cells Ruk/CIN85-HyPer is concentrated in dot-like vesicular structures of different size while HyPer is diffusely distributed throughout the cell. Using live cell fluorescence microscopy we observed gradual increase in hydrogen peroxide concentration in representative vesicular structures during the time of experiment. Thus, the developed genetic construction encoding the chimeric Ruk/CIN85-HyPer fluorescent protein represents a new tool to study localized H2O2 production in living cells.

  19. Expression of the neuronal adaptor protein X11alpha protects against memory dysfunction in a transgenic mouse model of Alzheimer's disease.

    LENUS (Irish Health Repository)

    Mitchell, Jacqueline C

    2010-01-01

    X11alpha is a neuronal-specific adaptor protein that binds to the amyloid-beta protein precursor (AbetaPP). Overexpression of X11alpha reduces Abeta production but whether X11alpha also protects against Abeta-related memory dysfunction is not known. To test this possibility, we crossed X11alpha transgenic mice with AbetaPP-Tg2576 mice. AbetaPP-Tg2576 mice produce high levels of brain Abeta and develop age-related defects in memory function that correlate with increasing Abeta load. Overexpression of X11alpha alone had no detectable adverse effect upon behavior. However, X11alpha reduced brain Abeta levels and corrected spatial reference memory defects in aged X11alpha\\/AbetaPP double transgenics. Thus, X11alpha may be a therapeutic target for Alzheimer\\'s disease.

  20. Adaptor protein 1 B mu subunit does not contribute to the recycling of kAE1 protein in polarized renal epithelial cells.

    Science.gov (United States)

    Almomani, Ensaf Y; Touret, Nicolas; Cordat, Emmanuelle

    2018-04-13

    Mutations in the gene encoding the kidney anion exchanger 1 (kAE1) can lead to distal renal tubular acidosis (dRTA). dRTA mutations reported within the carboxyl (C)-terminal tail of kAE1 result in apical mis-targeting of the exchanger in polarized renal epithelial cells. As kAE1 physically interacts with the μ subunit of epithelial adaptor protein 1 B (AP-1B), we investigated the role of heterologously expressed μ1B subunit of the AP-1B complex for kAE1 retention to the basolateral membrane in polarized porcine LLC-PK1 renal epithelial cells that are devoid of endogenous AP-1B. We confirmed the interaction and close proximity between kAE1 and μ1B using immunoprecipitation and proximity ligation assay, respectively. Expressing the human μ1B subunit in these cells decreased significantly the amount of cell surface kAE1 at the steady state, but had no significant effect on kAE1 recycling and endocytosis. We show that (i) heterologous expression of μ1B displaces the physical interaction of endogenous GAPDH with kAE1 WT supporting that both AP-1B and GAPDH proteins bind to an overlapping site on kAE1 and (ii) phosphorylation of tyrosine 904 within the potential YDEV interaction motif does not alter the kAE1/AP-1B interaction. We conclude that μ1B subunit is not involved in recycling of kAE1.

  1. Seneca Valley Virus Suppresses Host Type I Interferon Production by Targeting Adaptor Proteins MAVS, TRIF, and TANK for Cleavage.

    Science.gov (United States)

    Qian, Suhong; Fan, Wenchun; Liu, Tingting; Wu, Mengge; Zhang, Huawei; Cui, Xiaofang; Zhou, Yun; Hu, Junjie; Wei, Shaozhong; Chen, Huanchun; Li, Xiangmin; Qian, Ping

    2017-08-15

    Seneca Valley virus (SVV) is an oncolytic RNA virus belonging to the Picornaviridae family. Its nucleotide sequence is highly similar to those of members of the Cardiovirus genus. SVV is also a neuroendocrine cancer-selective oncolytic picornavirus that can be used for anticancer therapy. However, the interaction between SVV and its host is yet to be fully characterized. In this study, SVV inhibited antiviral type I interferon (IFN) responses by targeting different host adaptors, including mitochondrial antiviral signaling (MAVS), Toll/interleukin 1 (IL-1) receptor domain-containing adaptor inducing IFN-β (TRIF), and TRAF family member-associated NF-κB activator (TANK), via viral 3C protease (3C pro ). SVV 3C pro mediated the cleavage of MAVS, TRIF, and TANK at specific sites, which required its protease activity. The cleaved MAVS, TRIF, and TANK lost the ability to regulate pattern recognition receptor (PRR)-mediated IFN production. The cleavage of TANK also facilitated TRAF6-induced NF-κB activation. SVV was also found to be sensitive to IFN-β. Therefore, SVV suppressed antiviral IFN production to escape host antiviral innate immune responses by cleaving host adaptor molecules. IMPORTANCE Host cells have developed various defenses against microbial pathogen infection. The production of IFN is the first line of defense against microbial infection. However, viruses have evolved many strategies to disrupt this host defense. SVV, a member of the Picornavirus genus, is an oncolytic virus that shows potential functions in anticancer therapy. It has been demonstrated that IFN can be used in anticancer therapy for certain tumors. However, the relationship between oncolytic virus and innate immune response in anticancer therapy is still not well known. In this study, we showed that SVV has evolved as an effective mechanism to inhibit host type I IFN production by using its 3C pro to cleave the molecules MAVS, TRIF, and TANK directly. These molecules are crucial for

  2. Adaptor protein GRB2 promotes Src tyrosine kinase activation and podosomal organization by protein-tyrosine phosphatase ϵ in osteoclasts.

    Science.gov (United States)

    Levy-Apter, Einat; Finkelshtein, Eynat; Vemulapalli, Vidyasiri; Li, Shawn S-C; Bedford, Mark T; Elson, Ari

    2014-12-26

    The non-receptor isoform of protein-tyrosine phosphatase ϵ (cyt-PTPe) supports adhesion of bone-resorbing osteoclasts by activating Src downstream of integrins. Loss of cyt-PTPe reduces Src activity in osteoclasts, reduces resorption of mineralized matrix both in vivo and in cell culture, and induces mild osteopetrosis in young female PTPe KO mice. Activation of Src by cyt-PTPe is dependent upon this phosphatase undergoing phosphorylation at its C-terminal Tyr-638 by partially active Src. To understand how cyt-PTPe activates Src, we screened 73 Src homology 2 (SH2) domains for binding to Tyr(P)-638 of cyt-PTPe. The SH2 domain of GRB2 bound Tyr(P)-638 of cyt-PTPe most prominently, whereas the Src SH2 domain did not bind at all, suggesting that GRB2 may link PTPe with downstream molecules. Further studies indicated that GRB2 is required for activation of Src by cyt-PTPe in osteoclast-like cells (OCLs) in culture. Overexpression of GRB2 in OCLs increased activating phosphorylation of Src at Tyr-416 and of cyt-PTPe at Tyr-638; opposite results were obtained when GRB2 expression was reduced by shRNA or by gene inactivation. Phosphorylation of cyt-PTPe at Tyr-683 and its association with GRB2 are integrin-driven processes in OCLs, and cyt-PTPe undergoes autodephosphorylation at Tyr-683, thus limiting Src activation by integrins. Reduced GRB2 expression also reduced the ability of bone marrow precursors to differentiate into OCLs and reduced the fraction of OCLs in which podosomal adhesion structures assume organization typical of active, resorbing cells. We conclude that GRB2 physically links cyt-PTPe with Src and enables cyt-PTPe to activate Src downstream of activated integrins in OCLs. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

  3. Human kidney anion exchanger 1 interacts with adaptor-related protein complex 1 μ1A (AP-1 mu1A)

    International Nuclear Information System (INIS)

    Sawasdee, Nunghathai; Junking, Mutita; Ngaojanlar, Piengpaga; Sukomon, Nattakan; Ungsupravate, Duangporn; Limjindaporn, Thawornchai; Akkarapatumwong, Varaporn; Noisakran, Sansanee; Yenchitsomanus, Pa-thai

    2010-01-01

    Research highlights: → Trafficking defect of kAE1 is a cause of dRTA but trafficking pathway of kAE1 has not been clearly described. → Adaptor-related protein complex 1 μ1A (AP-1 mu1A) was firstly reported to interact with kAE1. → The interacting site for AP-1 mu1A on Ct-kAE1 was found to be Y904DEV907, a subset of YXXO motif. → AP-1 mu1A knockdown showed a marked reduction of kAE1 on the cell membrane and its accumulation in endoplasmic reticulum. → AP-1 mu1A has a critical role in kAE1 trafficking to the plasma membrane. -- Abstract: Kidney anion exchanger 1 (kAE1) mediates chloride (Cl - ) and bicarbonate (HCO 3 - ) exchange at the basolateral membrane of kidney α-intercalated cells. Impaired trafficking of kAE1 leads to defect of the Cl - /HCO 3 - exchange at the basolateral membrane and failure of proton (H + ) secretion at the apical membrane, causing a kidney disease - distal renal tubular acidosis (dRTA). To gain a better insight into kAE1 trafficking, we searched for proteins physically interacting with the C-terminal region of kAE1 (Ct-kAE1), which contains motifs crucial for intracellular trafficking, by a yeast two-hybrid (Y2H) system. An adaptor-related protein complex 1 μ1A (AP-1 mu1A) subunit was found to interact with Ct-kAE1. The interaction between either Ct-kAE1 or full-length kAE1 and AP-1 mu1A were confirmed in human embryonic kidney (HEK) 293T by co-immunoprecipitation, affinity co-purification, co-localization, yellow fluorescent protein (YFP)-based protein fragment complementation assay (PCA) and GST pull-down assay. The interacting site for AP-1 mu1A on Ct-kAE1 was found to be Y904DEV907, a subset of YXXO motif. Interestingly, suppression of endogenous AP-1 mu1A in HEK 293T by small interfering RNA (siRNA) decreased membrane localization of kAE1 and increased its intracellular accumulation, suggesting for the first time that AP-1 mu1A is involved in the kAE1 trafficking of kidney α-intercalated cells.

  4. Human kidney anion exchanger 1 interacts with adaptor-related protein complex 1 {mu}1A (AP-1 mu1A)

    Energy Technology Data Exchange (ETDEWEB)

    Sawasdee, Nunghathai; Junking, Mutita [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Ngaojanlar, Piengpaga [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Department of Immunology and Graduate Program in Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Sukomon, Nattakan; Ungsupravate, Duangporn [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Limjindaporn, Thawornchai [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Department of Anatomy, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Akkarapatumwong, Varaporn [Institute of Molecular Biosciences, Mahidol University at Salaya Campus, Nakorn Pathom 73170 (Thailand); Noisakran, Sansanee [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand); Yenchitsomanus, Pa-thai, E-mail: grpye@mahidol.ac.th [Division of Medical Molecular Biology and BIOTEC-Medical Biotechnology Unit, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700 (Thailand)

    2010-10-08

    Research highlights: {yields} Trafficking defect of kAE1 is a cause of dRTA but trafficking pathway of kAE1 has not been clearly described. {yields} Adaptor-related protein complex 1 {mu}1A (AP-1 mu1A) was firstly reported to interact with kAE1. {yields} The interacting site for AP-1 mu1A on Ct-kAE1 was found to be Y904DEV907, a subset of YXXO motif. {yields} AP-1 mu1A knockdown showed a marked reduction of kAE1 on the cell membrane and its accumulation in endoplasmic reticulum. {yields} AP-1 mu1A has a critical role in kAE1 trafficking to the plasma membrane. -- Abstract: Kidney anion exchanger 1 (kAE1) mediates chloride (Cl{sup -}) and bicarbonate (HCO{sub 3}{sup -}) exchange at the basolateral membrane of kidney {alpha}-intercalated cells. Impaired trafficking of kAE1 leads to defect of the Cl{sup -}/HCO{sub 3}{sup -} exchange at the basolateral membrane and failure of proton (H{sup +}) secretion at the apical membrane, causing a kidney disease - distal renal tubular acidosis (dRTA). To gain a better insight into kAE1 trafficking, we searched for proteins physically interacting with the C-terminal region of kAE1 (Ct-kAE1), which contains motifs crucial for intracellular trafficking, by a yeast two-hybrid (Y2H) system. An adaptor-related protein complex 1 {mu}1A (AP-1 mu1A) subunit was found to interact with Ct-kAE1. The interaction between either Ct-kAE1 or full-length kAE1 and AP-1 mu1A were confirmed in human embryonic kidney (HEK) 293T by co-immunoprecipitation, affinity co-purification, co-localization, yellow fluorescent protein (YFP)-based protein fragment complementation assay (PCA) and GST pull-down assay. The interacting site for AP-1 mu1A on Ct-kAE1 was found to be Y904DEV907, a subset of YXXO motif. Interestingly, suppression of endogenous AP-1 mu1A in HEK 293T by small interfering RNA (siRNA) decreased membrane localization of kAE1 and increased its intracellular accumulation, suggesting for the first time that AP-1 mu1A is involved in the kAE1

  5. Autophagy adaptor protein p62/SQSTM1 and autophagy-related gene Atg5 mediate autophagosome formation in response to Mycobacterium tuberculosis infection in dendritic cells.

    Directory of Open Access Journals (Sweden)

    Shintaro Seto

    Full Text Available Mycobacterium tuberculosis is an intracellular pathogen that can survive within phagocytic cells by inhibiting phagolysosome biogenesis. However, host cells can control the intracellular M. tuberculosis burden by the induction of autophagy. The mechanism of autophagosome formation to M. tuberculosis has been well studied in macrophages, but remains unclear in dendritic cells. We therefore characterized autophagosome formation in response to M. tuberculosis infection in dendritic cells. Autophagy marker protein LC3, autophagy adaptor protein p62/SQSTM1 (p62 and ubiquitin co-localized to M. tuberculosis in dendritic cells. Mycobacterial autophagosomes fused with lysosomes during infection, and major histcompatibility complex class II molecules (MHC II also localized to mycobacterial autophagosomes. The proteins p62 and Atg5 function in the initiation and progression of autophagosome formation to M. tuberculosis, respectively; p62 mediates ubiquitination of M. tuberculosis and Atg5 is involved in the trafficking of degradative vesicles and MHC II to mycobacterial autophagosomes. These results imply that the autophagosome formation to M. tuberculosis in dendritic cells promotes the antigen presentation of mycobacterial peptides to CD4(+ T lymphocytes via MHC II.

  6. Evidence against roles for phorbol binding protein Munc13-1, ADAM adaptor Eve-1, or vesicle trafficking phosphoproteins Munc18 or NSF as phospho-state-sensitive modulators of phorbol/PKC-activated Alzheimer APP ectodomain shedding

    Directory of Open Access Journals (Sweden)

    Lovestone Simon

    2007-12-01

    Full Text Available Abstract Background Shedding of the Alzheimer amyloid precursor protein (APP ectodomain can be accelerated by phorbol esters, compounds that act via protein kinase C (PKC or through unconventional phorbol-binding proteins such as Munc13-1. We have previously demonstrated that application of phorbol esters or purified PKC potentiates budding of APP-bearing secretory vesicles at the trans-Golgi network (TGN and toward the plasma membrane where APP becomes a substrate for enzymes responsible for shedding, known collectively as α-secretase(s. However, molecular identification of the presumptive "phospho-state-sensitive modulators of ectodomain shedding" (PMES responsible for regulated shedding has been challenging. Here, we examined the effects on APP ectodomain shedding of four phorbol-sensitive proteins involved in regulation of vesicular membrane trafficking of APP: Munc13-1, Munc18, NSF, and Eve-1. Results Overexpression of either phorbol-sensitive wildtype Munc13-1 or phorbol-insensitive Munc13-1 H567K resulted in increased basal APP ectodomain shedding. However, in contrast to the report of Roßner et al (2004, phorbol ester-dependent APP ectodomain shedding from cells overexpressing APP and Munc13-1 wildtype was indistinguishable from that observed following application of phorbol to cells overexpressing APP and Munc13-1 H567K mutant. This pattern of similar effects on basal and stimulated APP shedding was also observed for Munc18 and NSF. Eve-1, an ADAM adaptor protein reported to be essential for PKC-regulated shedding of pro-EGF, was found to play no obvious role in regulated shedding of sAPPα. Conclusion Our results indicate that, in the HEK293 system, Munc13-1, Munc18, NSF, and EVE-1 fail to meet essential criteria for identity as PMES for APP.

  7. Evidence against roles for phorbol binding protein Munc13-1, ADAM adaptor Eve-1, or vesicle trafficking phosphoproteins Munc18 or NSF as phospho-state-sensitive modulators of phorbol/PKC-activated Alzheimer APP ectodomain shedding.

    Science.gov (United States)

    Ikin, Annat F; Causevic, Mirsada; Pedrini, Steve; Benson, Lyndsey S; Buxbaum, Joseph D; Suzuki, Toshiharu; Lovestone, Simon; Higashiyama, Shigeki; Mustelin, Tomas; Burgoyne, Robert D; Gandy, Sam

    2007-12-09

    Shedding of the Alzheimer amyloid precursor protein (APP) ectodomain can be accelerated by phorbol esters, compounds that act via protein kinase C (PKC) or through unconventional phorbol-binding proteins such as Munc13-1. We have previously demonstrated that application of phorbol esters or purified PKC potentiates budding of APP-bearing secretory vesicles at the trans-Golgi network (TGN) and toward the plasma membrane where APP becomes a substrate for enzymes responsible for shedding, known collectively as alpha-secretase(s). However, molecular identification of the presumptive "phospho-state-sensitive modulators of ectodomain shedding" (PMES) responsible for regulated shedding has been challenging. Here, we examined the effects on APP ectodomain shedding of four phorbol-sensitive proteins involved in regulation of vesicular membrane trafficking of APP: Munc13-1, Munc18, NSF, and Eve-1. Overexpression of either phorbol-sensitive wildtype Munc13-1 or phorbol-insensitive Munc13-1 H567K resulted in increased basal APP ectodomain shedding. However, in contrast to the report of Rossner et al (2004), phorbol ester-dependent APP ectodomain shedding from cells overexpressing APP and Munc13-1 wildtype was indistinguishable from that observed following application of phorbol to cells overexpressing APP and Munc13-1 H567K mutant. This pattern of similar effects on basal and stimulated APP shedding was also observed for Munc18 and NSF. Eve-1, an ADAM adaptor protein reported to be essential for PKC-regulated shedding of pro-EGF, was found to play no obvious role in regulated shedding of sAPPalpha. Our results indicate that, in the HEK293 system, Munc13-1, Munc18, NSF, and EVE-1 fail to meet essential criteria for identity as PMES for APP.

  8. The Ras suppressor Rsu-1 binds to the LIM 5 domain of the adaptor protein PINCH1 and participates in adhesion-related functions

    International Nuclear Information System (INIS)

    Dougherty, Gerard W.; Chopp, Treasa; Qi Shengmei; Cutler, Mary Lou

    2005-01-01

    Rsu-1 is a highly conserved leucine rich repeat (LRR) protein that is expressed ubiquitously in mammalian cells. Rsu-1 was identified based on its ability to inhibit transformation by Ras, and previous studies demonstrated that ectopic expression of Rsu-1 inhibited anchorage-independent growth of Ras-transformed cells and human tumor cell lines. Using GAL4-based yeast two-hybrid screening, the LIM domain protein, PINCH1, was identified as the binding partner of Rsu-1. PINCH1 is an adaptor protein that localizes to focal adhesions and it has been implicated in the regulation of adhesion functions. Subdomain mapping in yeast revealed that Rsu-1 binds to the LIM 5 domain of PINCH1, a region not previously identified as a specific binding domain for any other protein. Additional testing demonstrated that PINCH2, which is highly homologous to PINCH1, except in the LIM 5 domain, does not interact with Rsu-1. Glutathione transferase fusion protein binding studies determined that the LRR region of Rsu-1 interacts with PINCH1. Transient expression studies using epitope-tagged Rsu-1 and PINCH1 revealed that Rsu-1 co-immunoprecipitated with PINCH1 and colocalized with vinculin at sites of focal adhesions in mammalian cells. In addition, endogenous P33 Rsu-1 from 293T cells co-immunoprecipitated with transiently expressed myc-tagged PINCH1. Furthermore, RNAi-induced reduction in Rsu-1 RNA and protein inhibited cell attachment, and while previous studies demonstrated that ectopic expression of Rsu-1 inhibited Jun kinase activation, the depletion of Rsu-1 resulted in activation of Jun and p38 stress kinases. These studies demonstrate that Rsu-1 interacts with PINCH1 in mammalian cells and functions, in part, by altering cell adhesion

  9. Disruption of the podosome adaptor protein TKS4 (SH3PXD2B) causes the skeletal dysplasia, eye, and cardiac abnormalities of Frank-Ter Haar Syndrome.

    Science.gov (United States)

    Iqbal, Zafar; Cejudo-Martin, Pilar; de Brouwer, Arjan; van der Zwaag, Bert; Ruiz-Lozano, Pilar; Scimia, M Cecilia; Lindsey, James D; Weinreb, Robert; Albrecht, Beate; Megarbane, Andre; Alanay, Yasemin; Ben-Neriah, Ziva; Amenduni, Mariangela; Artuso, Rosangela; Veltman, Joris A; van Beusekom, Ellen; Oudakker, Astrid; Millán, José Luis; Hennekam, Raoul; Hamel, Ben; Courtneidge, Sara A; van Bokhoven, Hans

    2010-02-12

    Frank-Ter Haar syndrome (FTHS), also known as Ter Haar syndrome, is an autosomal-recessive disorder characterized by skeletal, cardiovascular, and eye abnormalities, such as increased intraocular pressure, prominent eyes, and hypertelorism. We have conducted homozygosity mapping on patients representing 12 FTHS families. A locus on chromosome 5q35.1 was identified for which patients from nine families shared homozygosity. For one family, a homozygous deletion mapped exactly to the smallest region of overlapping homozygosity, which contains a single gene, SH3PXD2B. This gene encodes the TKS4 protein, a phox homology (PX) and Src homology 3 (SH3) domain-containing adaptor protein and Src substrate. This protein was recently shown to be involved in the formation of actin-rich membrane protrusions called podosomes or invadopodia, which coordinate pericellular proteolysis with cell migration. Mice lacking Tks4 also showed pronounced skeletal, eye, and cardiac abnormalities and phenocopied the majority of the defects associated with FTHS. These findings establish a role for TKS4 in FTHS and embryonic development. Mutation analysis revealed five different homozygous mutations in SH3PXD2B in seven FTHS families. No SH3PXD2B mutations were detected in six other FTHS families, demonstrating the genetic heterogeneity of this condition. Interestingly however, dermal fibroblasts from one of the individuals without an SH3PXD2B mutation nevertheless expressed lower levels of the TKS4 protein, suggesting a common mechanism underlying disease causation. Copyright (c) 2010 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  10. Crystal structure of Src-like adaptor protein 2 reveals close association of SH3 and SH2 domains through β-sheet formation.

    Science.gov (United States)

    Wybenga-Groot, Leanne E; McGlade, C Jane

    2013-12-01

    The Src-like adaptor proteins (SLAP/SLAP2) are key components of Cbl-dependent downregulation of antigen receptor, cytokine receptor, and receptor tyrosine kinase signaling in hematopoietic cells. SLAP and SLAP2 consist of adjacent SH3 and SH2 domains that are most similar in sequence to Src family kinases (SFKs). Notably, the SH3-SH2 connector sequence is significantly shorter in SLAP/SLAP2 than in SFKs. To understand the structural implication of a short SH3-SH2 connector sequence, we solved the crystal structure of a protein encompassing the SH3 domain, SH3-SH2 connector, and SH2 domain of SLAP2 (SLAP2-32). While both domains adopt typical folds, the short SH3-SH2 connector places them in close association. Strand βe of the SH3 domain interacts with strand βA of the SH2 domain, resulting in the formation of a continuous β sheet that spans the length of the protein. Disruption of the SH3/SH2 interface through mutagenesis decreases SLAP-32 stability in vitro, consistent with inter-domain binding being an important component of SLAP2 structure and function. The canonical peptide binding pockets of the SH3 and SH2 domains are fully accessible, in contrast to other protein structures that display direct interaction between SH3 and SH2 domains, in which either peptide binding surface is obstructed by the interaction. Our results reveal potential sites of novel interaction for SH3 and SH2 domains, and illustrate the adaptability of SH2 and SH3 domains in mediating interactions. As well, our results suggest that the SH3 and SH2 domains of SLAP2 function interdependently, with implications on their mode of substrate binding. © 2013.

  11. Identification of Atg2 and ArfGAP1 as Candidate Genetic Modifiers of the Eye Pigmentation Phenotype of Adaptor Protein-3 (AP-3) Mutants in Drosophila melanogaster.

    Science.gov (United States)

    Rodriguez-Fernandez, Imilce A; Dell'Angelica, Esteban C

    2015-01-01

    The Adaptor Protein (AP)-3 complex is an evolutionary conserved, molecular sorting device that mediates the intracellular trafficking of proteins to lysosomes and related organelles. Genetic defects in AP-3 subunits lead to impaired biogenesis of lysosome-related organelles (LROs) such as mammalian melanosomes and insect eye pigment granules. In this work, we have performed a forward screening for genetic modifiers of AP-3 function in the fruit fly, Drosophila melanogaster. Specifically, we have tested collections of large multi-gene deletions--which together covered most of the autosomal chromosomes-to identify chromosomal regions that, when deleted in single copy, enhanced or ameliorated the eye pigmentation phenotype of two independent AP-3 subunit mutants. Fine-mapping led us to define two non-overlapping, relatively small critical regions within fly chromosome 3. The first critical region included the Atg2 gene, which encodes a conserved protein involved in autophagy. Loss of one functional copy of Atg2 ameliorated the pigmentation defects of mutants in AP-3 subunits as well as in two other genes previously implicated in LRO biogenesis, namely Blos1 and lightoid, and even increased the eye pigment content of wild-type flies. The second critical region included the ArfGAP1 gene, which encodes a conserved GTPase-activating protein with specificity towards GTPases of the Arf family. Loss of a single functional copy of the ArfGAP1 gene ameliorated the pigmentation phenotype of AP-3 mutants but did not to modify the eye pigmentation of wild-type flies or mutants in Blos1 or lightoid. Strikingly, loss of the second functional copy of the gene did not modify the phenotype of AP-3 mutants any further but elicited early lethality in males and abnormal eye morphology when combined with mutations in Blos1 and lightoid, respectively. These results provide genetic evidence for new functional links connecting the machinery for biogenesis of LROs with molecules implicated in

  12. Protein Networks in Alzheimer's Disease

    DEFF Research Database (Denmark)

    Carlsen, Eva Meier; Rasmussen, Rune

    2017-01-01

    Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans......Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans...

  13. The adaptor protein SAP directly associates with PECAM-1 and regulates PECAM-1-mediated-cell adhesion in T-like cell lines.

    Science.gov (United States)

    Proust, Richard; Crouin, Catherine; Gandji, Leslie Yewakon; Bertoglio, Jacques; Gesbert, Franck

    2014-04-01

    SAP is a small cytosolic adaptor protein expressed in hematopoietic lineages whose main function is to regulate intracellular signaling pathways induced by the triggering of members of the SLAM receptor family. In this paper, we have identified the adhesion molecule PECAM-1 as a new partner for SAP in a conditional yeast two-hybrid screen. PECAM-1 is an immunoglobulin-like molecule expressed by endothelial cells and leukocytes, which possesses both pro- and anti-inflammatory properties. However, little is known about PECAM-1 functions in T cells. We show that SAP directly and specifically interacts with the cytosolic tyrosine 686 of PECAM-1. We generated different T-like cell lines in which SAP or PECAM-1 are expressed or down modulated and we demonstrate that a diminished SAP expression correlates with a diminished PECAM-1-mediated adhesion. Although SAP has mainly been shown to associate with SLAM receptors, we evidence here that SAP is a new actor downstream of PECAM-1. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Enigma interacts with adaptor protein with PH and SH2 domains to control insulin-induced actin cytoskeleton remodeling and glucose transporter 4 translocation.

    Science.gov (United States)

    Barrès, Romain; Grémeaux, Thierry; Gual, Philippe; Gonzalez, Teresa; Gugenheim, Jean; Tran, Albert; Le Marchand-Brustel, Yannick; Tanti, Jean-François

    2006-11-01

    APS (adaptor protein with PH and SH2 domains) initiates a phosphatidylinositol 3-kinase-independent pathway involved in insulin-stimulated glucose transport. We recently identified Enigma, a PDZ and LIM domain-containing protein, as a partner of APS and showed that APS-Enigma complex plays a critical role in actin cytoskeleton organization in fibroblastic cells. Because actin rearrangement is important for insulin-induced glucose transporter 4 (Glut 4) translocation, we studied the potential involvement of Enigma in insulin-induced glucose transport in 3T3-L1 adipocytes. Enigma mRNA was expressed in differentiated adipocytes and APS and Enigma were colocalized with cortical actin. Expression of an APS mutant unable to bind Enigma increased the insulin-induced Glut 4 translocation to the plasma membrane. By contrast, overexpression of Enigma inhibited insulin-stimulated glucose transport and Glut 4 translocation without alterations in proximal insulin signaling. This inhibitory effect was prevented with the deletion of the LIM domains of Enigma. Using time-lapse fluorescent microscopy of green fluorescent protein-actin, we demonstrated that the overexpression of Enigma altered insulin-induced actin rearrangements, whereas the expression of Enigma without its LIM domains was without effect. A physiological link between increased expression of Enigma and an alteration in insulin-induced glucose uptake was suggested by the increase in Enigma mRNA expression in adipose tissue of diabetic obese patients. Taken together, these data strongly suggest that the interaction between APS and Enigma is involved in insulin-induced Glut 4 translocation by regulating cortical actin remodeling and raise the possibility that modification of APS/Enigma ratio could participate in the alteration of insulin-induced glucose uptake in adipose tissue.

  15. Enhancement of B-cell receptor signaling by a point mutation of adaptor protein 3BP2 identified in human inherited disease cherubism.

    Science.gov (United States)

    Ogi, Kazuhiro; Nakashima, Kenji; Chihara, Kazuyasu; Takeuchi, Kenji; Horiguchi, Tomoko; Fujieda, Shigeharu; Sada, Kiyonao

    2011-09-01

    Tyrosine phosphorylation of adaptor protein c-Abl-Src homology 3 (SH3) domain-binding protein-2 (3BP2, also referred to SH3BP2) positively regulates the B-cell antigen receptor (BCR)-mediated signal transduction, leading to the activation of nuclear factor of activated T cells (NFAT). Here we showed the effect of the proline to arginine substitution of 3BP2 in which is the most common mutation in patients with cherubism (P418R) on B-cell receptor signaling. Comparing to the wild type, overexpression of the mutant form of 3BP2 (3BP2-P416R, corresponding to P418R in human protein) enhanced BCR-mediated activation of NFAT. 3BP2-P416R increased the signaling complex formation with Syk, phospholipase C-γ2 (PLC-γ2), and Vav1. In contrast, 3BP2-P416R could not change the association with the negative regulator 14-3-3. Loss of the association mutant that was incapable to associate with 14-3-3 could not mimic BCR-mediated NFAT activation in Syk-deficient cells. Moreover, BCR-mediated phosphorylation of extracellular signal regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) was not affected by P416R mutation. These results showed that P416R mutation of 3BP2 causes the gain of function in B cells by increasing the interaction with specific signaling molecules. © 2011 The Authors. Journal compilation © 2011 by the Molecular Biology Society of Japan/Blackwell Publishing Ltd.

  16. Dimerization of the docking/adaptor protein HEF1 via a carboxy-terminal helix-loop-helix domain.

    Science.gov (United States)

    Law, S F; Zhang, Y Z; Fashena, S J; Toby, G; Estojak, J; Golemis, E A

    1999-10-10

    HEF1, p130(Cas), and Efs define a family of multidomain docking proteins which plays a central coordinating role for tyrosine-kinase-based signaling related to cell adhesion. HEF1 function has been specifically implicated in signaling pathways important for cell adhesion and differentiation in lymphoid and epithelial cells. While the SH3 domains and SH2-binding site domains (substrate domains) of HEF1 family proteins are well characterized and binding partners known, to date the highly conserved carboxy-terminal domains of the three proteins have lacked functional definition. In this study, we have determined that the carboxy-terminal domain of HEF1 contains a divergent helix-loop-helix (HLH) motif. This motif mediates HEF1 homodimerization and HEF1 heterodimerization with a recognition specificity similar to that of the transcriptional regulatory HLH proteins Id2, E12, and E47. We had previously demonstrated that the HEF1 carboxy-terminus expressed as a separate domain in yeast reprograms cell division patterns, inducing constitutive pseudohyphal growth. Here we show that pseudohyphal induction by HEF1 requires an intact HLH, further supporting the idea that this motif has an effector activity for HEF1, and implying that HEF1 pseudohyphal activity derives in part from interactions with yeast helix-loop-helix proteins. These combined results provide initial insight into the mode of function of the HEF1 carboxy-terminal domain and suggest that the HEF1 protein may interact with cellular proteins which control differentiation. Copyright 1999 Academic Press.

  17. Interplays between Sumoylation, SUMO-Targeted Ubiquitin Ligases, and the Ubiquitin-Adaptor Protein Ufd1 in Fission Yeast

    DEFF Research Database (Denmark)

    Køhler, Julie Bonne

    and the specific molecular interactions and sequence of events linking sumoylation, ubiquitylation and substrate degradation, has been largely uncovered. Using the fission yeast model organism I here present evidence for a role of the Ufd1 (ubiquitinfusion degradation 1) protein, and by extension of the Cdc48-Ufd1...... proteasome mediates direct cross-talk between the two modification systems. By contributing to the dynamic turnover of SUMO conjugated species these SUMO-targeted ubiquitin ligases (STUbLs) fulfills essential roles in both yeast and man. However, the specific sumoylated proteins affected by STUbL activity...... either in STUbL or Ufd1 function. In addition to identifying more than 900 unique sumoylated sites, these efforts revealed a number of proteins with upregulated sumoylation either in STUbL and/or Ufd1 mutant cells. These findings propose specific candidate substrates through which STUbL and Cdc48-Ufd1...

  18. Spectral affinity in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-11-29

    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. 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. 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 quickly find nodes closest to a queried vertex in any protein

  19. Spectral affinity in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-11-01

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

  20. A novel glutamine-rich putative transcriptional adaptor protein (TIG-1), preferentially expressed in placental and bone-marrow tissues.

    Science.gov (United States)

    Abraham, S; Solomon, W B

    2000-09-19

    We used a subtractive hybridization protocol to identify novel expressed sequence tags (ESTs) corresponding to mRNAs whose expression was induced upon exposure of the human leukemia cell line K562 to the phorbol ester 12-O-tetradecanolyphorbol-13-acetate (TPA). The complete open reading frame of one of the novel ESTs, named TIG-1, was obtained by screening K562 cell and placental cDNA libraries. The deduced open reading frame of the TIG-1 cDNA encodes for a glutamine repeat-rich protein with a predicted molecular weight of 63kDa. The predicted open reading frame also contains a consensus bipartite nuclear localization signal, though no specific DNA-binding domain is found. The corresponding TIG-1 mRNA is ubiquitously expressed. Placental tissue expresses the TIG-1 mRNA 200 times more than the lowest expressing tissues such as kidney and lung. There is also preferential TIG-1 mRNA expression in cells of bone-marrow lineage.In-vitro transcription/translation of the TIG-1 cDNA yielded a polypeptide with an apparent molecular weight of 97kDa. Using polyclonal antibodies obtained from a rabbit immunized with the carboxy-terminal portion of bacterially expressed TIG-1 protein, a polypeptide with molecular weight of 97kDa was identified by Western blot analyses of protein lysates obtained from K562 cells. Cotransfection assays of K562 cells, using a GAL4-TIG-1 fusion gene and GAL4 operator-CAT, indicate that the TIG-1 protein may have transcriptional regulatory activity when tethered to DNA. We hypothesize that this novel glutamine-rich protein participates in a protein complex that regulates gene transcription. It has been demonstrated by Naar et al. (Naar, A.M., Beaurang, P.A., Zhou, S., Abraham, S., Solomon, W.B., Tjian, R., 1999, Composite co-activator ARC mediates chromatin-directed transcriptional activation. Nature 398, 828-830) that the amino acid sequences of peptide fragments obtained from a polypeptide found in a complex of proteins that alters chromatin

  1. SmShb, the SH2-Containing Adaptor Protein B of Schistosoma mansoni Regulates Venus Kinase Receptor Signaling Pathways.

    Directory of Open Access Journals (Sweden)

    Marion Morel

    Full Text Available Venus kinase receptors (VKRs are invertebrate receptor tyrosine kinases (RTKs formed by an extracellular Venus Fly Trap (VFT ligand binding domain associated via a transmembrane domain with an intracellular tyrosine kinase (TK domain. Schistosoma mansoni VKRs, SmVKR1 and SmVKR2, are both implicated in reproductive activities of the parasite. In this work, we show that the SH2 domain-containing protein SmShb is a partner of the phosphorylated form of SmVKR1. Expression of these proteins in Xenopus oocytes allowed us to demonstrate that the SH2 domain of SmShb interacts with the phosphotyrosine residue (pY979 located in the juxtamembrane region of SmVKR1. This interaction leads to phosphorylation of SmShb on tyrosines and promotes SmVKR1 signaling towards the JNK pathway. SmShb transcripts are expressed in all parasite stages and they were found in ovary and testes of adult worms, suggesting a possible colocalization of SmShb and SmVKR1 proteins. Silencing of SmShb in adult S. mansoni resulted in an accumulation of mature sperm in testes, indicating a possible role of SmShb in gametogenesis.

  2. SmShb, the SH2-Containing Adaptor Protein B of Schistosoma mansoni Regulates Venus Kinase Receptor Signaling Pathways.

    Science.gov (United States)

    Morel, Marion; Vanderstraete, Mathieu; Cailliau, Katia; Hahnel, Steffen; Grevelding, Christoph G; Dissous, Colette

    2016-01-01

    Venus kinase receptors (VKRs) are invertebrate receptor tyrosine kinases (RTKs) formed by an extracellular Venus Fly Trap (VFT) ligand binding domain associated via a transmembrane domain with an intracellular tyrosine kinase (TK) domain. Schistosoma mansoni VKRs, SmVKR1 and SmVKR2, are both implicated in reproductive activities of the parasite. In this work, we show that the SH2 domain-containing protein SmShb is a partner of the phosphorylated form of SmVKR1. Expression of these proteins in Xenopus oocytes allowed us to demonstrate that the SH2 domain of SmShb interacts with the phosphotyrosine residue (pY979) located in the juxtamembrane region of SmVKR1. This interaction leads to phosphorylation of SmShb on tyrosines and promotes SmVKR1 signaling towards the JNK pathway. SmShb transcripts are expressed in all parasite stages and they were found in ovary and testes of adult worms, suggesting a possible colocalization of SmShb and SmVKR1 proteins. Silencing of SmShb in adult S. mansoni resulted in an accumulation of mature sperm in testes, indicating a possible role of SmShb in gametogenesis.

  3. Identification of Atg2 and ArfGAP1 as Candidate Genetic Modifiers of the Eye Pigmentation Phenotype of Adaptor Protein-3 (AP-3 Mutants in Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Imilce A Rodriguez-Fernandez

    Full Text Available The Adaptor Protein (AP-3 complex is an evolutionary conserved, molecular sorting device that mediates the intracellular trafficking of proteins to lysosomes and related organelles. Genetic defects in AP-3 subunits lead to impaired biogenesis of lysosome-related organelles (LROs such as mammalian melanosomes and insect eye pigment granules. In this work, we have performed a forward screening for genetic modifiers of AP-3 function in the fruit fly, Drosophila melanogaster. Specifically, we have tested collections of large multi-gene deletions--which together covered most of the autosomal chromosomes-to identify chromosomal regions that, when deleted in single copy, enhanced or ameliorated the eye pigmentation phenotype of two independent AP-3 subunit mutants. Fine-mapping led us to define two non-overlapping, relatively small critical regions within fly chromosome 3. The first critical region included the Atg2 gene, which encodes a conserved protein involved in autophagy. Loss of one functional copy of Atg2 ameliorated the pigmentation defects of mutants in AP-3 subunits as well as in two other genes previously implicated in LRO biogenesis, namely Blos1 and lightoid, and even increased the eye pigment content of wild-type flies. The second critical region included the ArfGAP1 gene, which encodes a conserved GTPase-activating protein with specificity towards GTPases of the Arf family. Loss of a single functional copy of the ArfGAP1 gene ameliorated the pigmentation phenotype of AP-3 mutants but did not to modify the eye pigmentation of wild-type flies or mutants in Blos1 or lightoid. Strikingly, loss of the second functional copy of the gene did not modify the phenotype of AP-3 mutants any further but elicited early lethality in males and abnormal eye morphology when combined with mutations in Blos1 and lightoid, respectively. These results provide genetic evidence for new functional links connecting the machinery for biogenesis of LROs with

  4. The Pseudomonas aeruginosa lectin LecA triggers host cell signalling by glycosphingolipid-dependent phosphorylation of the adaptor protein CrkII.

    Science.gov (United States)

    Zheng, Shuangshuang; Eierhoff, Thorsten; Aigal, Sahaja; Brandel, Annette; Thuenauer, Roland; de Bentzmann, Sophie; Imberty, Anne; Römer, Winfried

    2017-07-01

    The human pathogen Pseudomonas aeruginosa induces phosphorylation of the adaptor protein CrkII by activating the non-receptor tyrosine kinase Abl to promote its uptake into host cells. So far, specific factors of P. aeruginosa, which induce Abl/CrkII signalling, are entirely unknown. In this research, we employed human lung epithelial cells H1299, Chinese hamster ovary cells and P. aeruginosa wild type strain PAO1 to study the invasion process of P. aeruginosa into host cells by using microbiological, biochemical and cell biological approaches such as Western Blot, immunofluorescence microscopy and flow cytometry. Here, we demonstrate that the host glycosphingolipid globotriaosylceramide, also termed Gb3, represents a signalling receptor for the P. aeruginosa lectin LecA to induce CrkII phosphorylation at tyrosine 221. Alterations in Gb3 expression and LecA function correlate with CrkII phosphorylation. Interestingly, phosphorylation of CrkII Y221 occurs independently of Abl kinase. We further show that Src family kinases transduce the signal induced by LecA binding to Gb3, leading to Crk Y221 phosphorylation. In summary, we identified LecA as a bacterial factor, which utilizes a so far unrecognized mechanism for phospho-CrkII Y221 induction by binding to the host glycosphingolipid receptor Gb3. The LecA/Gb3 interaction highlights the potential of glycolipids to mediate signalling processes across the plasma membrane and should be further elucidated to gain deeper insights into this non-canonical mechanism of activating host cell processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Adaptor protein SH2-B linking receptor-tyrosine kinase and Akt promotes adipocyte differentiation by regulating peroxisome proliferator-activated receptor gamma messenger ribonucleic acid levels.

    Science.gov (United States)

    Yoshiga, Daigo; Sato, Naoichi; Torisu, Takehiro; Mori, Hiroyuki; Yoshida, Ryoko; Nakamura, Seiji; Takaesu, Giichi; Kobayashi, Takashi; Yoshimura, Akihiko

    2007-05-01

    Adipocyte differentiation is regulated by insulin and IGF-I, which transmit signals by activating their receptor tyrosine kinase. SH2-B is an adaptor protein containing pleckstrin homology and Src homology 2 (SH2) domains that have been implicated in insulin and IGF-I receptor signaling. In this study, we found a strong link between SH2-B levels and adipogenesis. The fat mass and expression of adipogenic genes including peroxisome proliferator-activated receptor gamma (PPARgamma) were reduced in white adipose tissue of SH2-B-/- mice. Reduced adipocyte differentiation of SH2-B-deficient mouse embryonic fibroblasts (MEFs) was observed in response to insulin and dexamethasone, whereas retroviral SH2-B overexpression enhanced differentiation of 3T3-L1 preadipocytes to adipocytes. SH2-B overexpression enhanced mRNA level of PPARgamma in 3T3-L1 cells, whereas PPARgamma levels were reduced in SH2-B-deficient MEFs in response to insulin. SH2-B-mediated up-regulation of PPARgamma mRNA was blocked by a phosphatidylinositol 3-kinase inhibitor, but not by a MAPK kinase inhibitor. Insulin-induced Akt activation and the phosphorylation of forkhead transcription factor (FKHR/Foxo1), a negative regulator of PPARgamma transcription, were up-regulated by SH2-B overexpression, but reduced in SH2-B-deficient MEFs. These data indicate that SH2-B is a key regulator of adipogenesis both in vivo and in vitro by regulating the insulin/IGF-I receptor-Akt-Foxo1-PPARgamma pathway.

  6. The adaptor protein SAP regulates type II NKT-cell development, cytokine production, and cytotoxicity against lymphoma.

    Science.gov (United States)

    Weng, Xiufang; Liao, Chia-Min; Bagchi, Sreya; Cardell, Susanna L; Stein, Paul L; Wang, Chyung-Ru

    2014-12-01

    CD1d-restricted NKT cells represent a unique lineage of immunoregulatory T cells that are divided into two groups, type I and type II, based on their TCR usage. Because there are no specific tools to identify type II NKT cells, little is known about their developmental requirements and functional regulation. In our previous study, we showed that signaling lymphocytic activation molecule associated protein (SAP) is essential for the development of type II NKT cells. Here, using a type II NKT-cell TCR transgenic mouse model, we demonstrated that CD1d-expressing hematopoietic cells, but not thymic epithelial cells, meditate efficient selection of type II NKT cells. Furthermore, we showed that SAP regulates type II NKT-cell development by controlling early growth response 2 protein and promyelocytic leukemia zinc finger expression. SAP-deficient 24αβ transgenic T cells (24αβ T cells) exhibited an immature phenotype with reduced Th2 cytokine-producing capacity and diminished cytotoxicity to CD1d-expressing lymphoma cells. The impaired IL-4 production by SAP-deficient 24αβ T cells was associated with reduced IFN regulatory factor 4 and GATA-3 induction following TCR stimulation. Collectively, these data suggest that SAP is critical for regulating type II NKT cell responses. Aberrant responses of these T cells may contribute to the immune dysregulation observed in X-linked lymphoproliferative disease caused by mutations in SAP. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. The Adaptor Protein SAP Regulates Type II NKT Cell Development, Cytokine Production and Cytotoxicity Against Lymphoma1

    Science.gov (United States)

    Weng, Xiufang; Liao, Chia-Min; Bagchi, Sreya; Cardell, Susanna L.; Stein, Paul L.; Wang, Chyung-Ru

    2014-01-01

    CD1d-restricted NKT cells represent a unique lineage of immunoregulatory T cells that are divided into two groups, type I and type II, based on their TCR usage. Because there are no specific tools to identify type II NKT cells, little is known about their developmental requirements and functional regulation. In our previous study, we showed that signaling lymphocytic activation molecule-associated protein (SAP) is essential for the development of type II NKT cells. Here, using a type II NKT cell TCR transgenic mouse model (24αβTg), we demonstrated that CD1d-expressing hematopoietic cells but not thymic epithelial cells meditate efficient selection of type II NKT cells. Further, we showed that SAP regulates type II NKT cell development by controlling Egr2 and PLZF expression. SAP-deficient 24αβ transgenic T cells (24αβ T cells) exhibited an immature phenotype with reduced Th2 cytokine-producing capacity and diminished cytotoxicity to CD1d-expressing lymphoma cells. The impaired IL-4 production by SAP-deficient 24αβ T cells was associated with reduced IRF4 and GATA-3 induction following TCR stimulation. Collectively, these data suggest that SAP is critical for regulating type II NKT cell responses. Aberrant responses of these T cells may contribute to the immune dysregulation observed in X-linked lymphoproliferative disease caused by mutations in SAP. PMID:25236978

  8. MyD88 Adaptor Protein Is Required for Appropriate Hepcidin Induction in Response to Dietary Iron Overload in Mice

    Directory of Open Access Journals (Sweden)

    Antonio Layoun

    2018-03-01

    Full Text Available Iron homeostasis is tightly regulated to provide virtually all cells in the body, particularly red blood cells, with this essential element while defending against its toxicity. The peptide hormone hepcidin is central to the control of the amount of iron absorbed from the diet and iron recycling from macrophages. Previously, we have shown that hepcidin induction in macrophages following Toll-like receptor (TLR stimulation depends on the presence of myeloid differentiation primary response gene 88 (MyD88. In this study, we analyzed the regulation of iron metabolism in MyD88−/− mice to further investigate MyD88 involvement in iron sensing and hepcidin induction. We show that mice lacking MyD88 accumulate significantly more iron in their livers than wild-type counterparts in response to dietary iron loading as they are unable to appropriately control hepcidin levels. The defect was associated with inappropriately low levels of Smad4 protein and Smad1/5/8 phosphorylation in liver samples found in the MyD88−/− mice compared to wild-type mice. In conclusion, our results reveal a previously unknown link between MyD88 and iron homeostasis, and provide new insights into the regulation of hepcidin through the iron-sensing pathway.

  9. Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected.

    Science.gov (United States)

    Koytiger, Grigoriy; Kaushansky, Alexis; Gordus, Andrew; Rush, John; Sorger, Peter K; MacBeath, Gavin

    2013-05-01

    Mutation and overexpression of receptor tyrosine kinases or the proteins they regulate serve as oncogenic drivers in diverse cancers. To better understand receptor tyrosine kinase signaling and its link to oncogenesis, we used protein microarrays to systematically and quantitatively measure interactions between virtually every SH2 or PTB domain encoded in the human genome and all known sites of tyrosine phosphorylation on 40 receptor tyrosine kinases and on most of the SH2 and PTB domain-containing adaptor proteins. We found that adaptor proteins, like RTKs, have many high affinity bindings sites for other adaptor proteins. In addition, proteins that drive cancer, including both receptors and adaptor proteins, tend to be much more highly interconnected via networks of SH2 and PTB domain-mediated interactions than nononcogenic proteins. Our results suggest that network topological properties such as connectivity can be used to prioritize new drug targets in this well-studied family of signaling proteins.

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

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

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

  11. Structural analysis of intermolecular interactions in the kinesin adaptor complex fasciculation and elongation protein zeta 1/ short coiled-coil protein (FEZ1/SCOCO.

    Directory of Open Access Journals (Sweden)

    Marcos Rodrigo Alborghetti

    Full Text Available Cytoskeleton and protein trafficking processes, including vesicle transport to synapses, are key processes in neuronal differentiation and axon outgrowth. The human protein FEZ1 (fasciculation and elongation protein zeta 1 / UNC-76, in C. elegans, SCOCO (short coiled-coil protein / UNC-69 and kinesins (e.g. kinesin heavy chain / UNC116 are involved in these processes. Exploiting the feature of FEZ1 protein as a bivalent adapter of transport mediated by kinesins and FEZ1 protein interaction with SCOCO (proteins involved in the same path of axonal growth, we investigated the structural aspects of intermolecular interactions involved in this complex formation by NMR (Nuclear Magnetic Resonance, cross-linking coupled with mass spectrometry (MS, SAXS (Small Angle X-ray Scattering and molecular modelling. The topology of homodimerization was accessed through NMR (Nuclear Magnetic Resonance studies of the region involved in this process, corresponding to FEZ1 (92-194. Through studies involving the protein in its monomeric configuration (reduced and dimeric state, we propose that homodimerization occurs with FEZ1 chains oriented in an anti-parallel topology. We demonstrate that the interaction interface of FEZ1 and SCOCO defined by MS and computational modelling is in accordance with that previously demonstrated for UNC-76 and UNC-69. SAXS and literature data support a heterotetrameric complex model. These data provide details about the interaction interfaces probably involved in the transport machinery assembly and open perspectives to understand and interfere in this assembly and its involvement in neuronal differentiation and axon outgrowth.

  12. Vaccinia virus protein C6 is a virulence factor that binds TBK-1 adaptor proteins and inhibits activation of IRF3 and IRF7.

    Directory of Open Access Journals (Sweden)

    Leonie Unterholzner

    2011-09-01

    Full Text Available Recognition of viruses by pattern recognition receptors (PRRs causes interferon-β (IFN-β induction, a key event in the anti-viral innate immune response, and also a target of viral immune evasion. Here the vaccinia virus (VACV protein C6 is identified as an inhibitor of PRR-induced IFN-β expression by a functional screen of select VACV open reading frames expressed individually in mammalian cells. C6 is a member of a family of Bcl-2-like poxvirus proteins, many of which have been shown to inhibit innate immune signalling pathways. PRRs activate both NF-κB and IFN regulatory factors (IRFs to activate the IFN-β promoter induction. Data presented here show that C6 inhibits IRF3 activation and translocation into the nucleus, but does not inhibit NF-κB activation. C6 inhibits IRF3 and IRF7 activation downstream of the kinases TANK binding kinase 1 (TBK1 and IκB kinase-ε (IKKε, which phosphorylate and activate these IRFs. However, C6 does not inhibit TBK1- and IKKε-independent IRF7 activation or the induction of promoters by constitutively active forms of IRF3 or IRF7, indicating that C6 acts at the level of the TBK1/IKKε complex. Consistent with this notion, C6 immunoprecipitated with the TBK1 complex scaffold proteins TANK, SINTBAD and NAP1. C6 is expressed early during infection and is present in both nucleus and cytoplasm. Mutant viruses in which the C6L gene is deleted, or mutated so that the C6 protein is not expressed, replicated normally in cell culture but were attenuated in two in vivo models of infection compared to wild type and revertant controls. Thus C6 contributes to VACV virulence and might do so via the inhibition of PRR-induced activation of IRF3 and IRF7.

  13. Protein Annotation from Protein Interaction Networks and Gene Ontology

    OpenAIRE

    Nguyen, Cao D.; Gardiner, Katheleen J.; Cios, Krzysztof J.

    2011-01-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precis...

  14. Hepatitis C virus infection protein network.

    Science.gov (United States)

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

    2008-01-01

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

  15. Unified Alignment of Protein-Protein Interaction Networks.

    Science.gov (United States)

    Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

    2017-04-19

    Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

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

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available 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 network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

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

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

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

  18. The proto-oncogene product p120CBL and the adaptor proteins CRKL and c-CRK link c-ABL, p190BCR/ABL and p210BCR/ABL to the phosphatidylinositol-3' kinase pathway.

    Science.gov (United States)

    Sattler, M; Salgia, R; Okuda, K; Uemura, N; Durstin, M A; Pisick, E; Xu, G; Li, J L; Prasad, K V; Griffin, J D

    1996-02-15

    Chronic myelogenous leukemia (CML) and some acute lymphoblastic leukemias (ALL) are caused by the t(9;22) chromosome translocation, which produces the constitutively activated BCR/ABL tyrosine kinase. When introduced into factor dependent hematopoietic cell lines, BCR/ABL induces the tyrosine phosphorylation of many cellular proteins. One prominent BCR/ABL substrate is p120CBL, the cellular homolog of the v-Cbl oncoprotein. In an effort to understand the possible contribution of p120CBL to transformation by BCR/ABL, we looked for cellular proteins which associate with p120CBL in hematopoietic cell lines transformed by BCR/ABL. In addition to p210BCR/ABL and c-ABL, p120CBL coprecipitated with an 85 kDa phosphoprotein, which was identified as the p85 subunit of PI3K. Anti-p120CBL immunoprecipitates from BCR/ABL-transformed, but not from untransformed, cell lines contained PI3K lipid kinase activity. Interestingly, the adaptor proteins CRKL and c-CRK were also found in these complexes. In vitro binding studies indicated that the SH2 domains of CRKL and c-CRK bound directly to p120CBL, while the SH3 domains of c-CRK and CRKL bound to BCR/ABL and c-ABL. The N-terminal and the C-terminal SH2 and the SH3 domain of p85PI3K bound directly in vitro to p120CBL. The ABL-SH2, but not ABL-SH3, could also bind to p120CBL. These data suggest that BCR/ABL may induce the formation of multimeric complexes of signaling proteins which include p120CBL, PI3K, c-CRK or CRKL, c-ABL and BCR/ABL itself.

  19. NAPS: Network Analysis of Protein Structures

    Science.gov (United States)

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

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

  20. Characterization of Toll-like receptors in primary lung epithelial cells: strong impact of the TLR3 ligand poly(I:C on the regulation of Toll-like receptors, adaptor proteins and inflammatory response

    Directory of Open Access Journals (Sweden)

    Weith Andreas

    2005-11-01

    Full Text Available Abstract Background Bacterial and viral exacerbations play a crucial role in a variety of lung diseases including COPD or asthma. Since the lung epithelium is a major source of various inflammatory mediators that affect the immune response, we analyzed the inflammatory reaction of primary lung epithelial cells to different microbial molecules that are recognized by Toll-like receptors (TLR. Methods The effects of TLR ligands on primary small airway epithelial cells were analyzed in detail with respect to cytokine, chemokine and matrix metalloproteinase secretion. In addition, the regulation of the expression of TLRs and their adaptor proteins in small airway epithelial cells was investigated. Results Our data demonstrate that poly(I:C, a synthetic analog of viral dsRNA, mediated the strongest proinflammatory effects among the tested ligands, including an increased secretion of IL-6, IL-8, TNF-α, GM-CSF, GRO-α, TARC, MCP-1, MIP-3α, RANTES, IFN-β, IP-10 and ITAC as well as an increased release of MMP-1, MMP-8, MMP-9, MMP-10 and MMP-13. Furthermore, our data show that poly(I:C as well as type-1 and type-2 cytokines have a pronounced effect on the expression of TLRs and molecules involved in TLR signaling in small airway epithelial cells. Poly(I:C induced an elevated expression of TLR1, TLR2 and TLR3 and increased the gene expression of the general TLR adaptor MyD88 and IRAK-2. Simultaneously, poly(I:C decreased the expression of TLR5, TLR6 and TOLLIP. Conclusion Poly(I:C, an analog of viral dsRNA and a TLR3 ligand, triggers a strong inflammatory response in small airway epithelial cells that is likely to contribute to viral exacerbations of pulmonary diseases like asthma or COPD. The pronounced effects of poly(I:C on the expression of Toll-like receptors and molecules involved in TLR signaling is assumed to influence the immune response of the lung epithelium to viral and bacterial infections. Likewise, the regulation of TLR expression by type

  1. The microRNA mir-71 inhibits calcium signaling by targeting the TIR-1/Sarm1 adaptor protein to control stochastic L/R neuronal asymmetry in C. elegans.

    Directory of Open Access Journals (Sweden)

    Yi-Wen Hsieh

    Full Text Available The Caenorhabditis elegans left and right AWC olfactory neurons communicate to establish stochastic asymmetric identities, AWC(ON and AWC(OFF, by inhibiting a calcium-mediated signaling pathway in the future AWC(ON cell. NSY-4/claudin-like protein and NSY-5/innexin gap junction protein are the two parallel signals that antagonize the calcium signaling pathway to induce the AWC(ON fate. However, it is not known how the calcium signaling pathway is downregulated by nsy-4 and nsy-5 in the AWC(ON cell. Here we identify a microRNA, mir-71, that represses the TIR-1/Sarm1 adaptor protein in the calcium signaling pathway to promote the AWC(ON identity. Similar to tir-1 loss-of-function mutants, overexpression of mir-71 generates two AWC(ON neurons. tir-1 expression is downregulated through its 3' UTR in AWC(ON, in which mir-71 is expressed at a higher level than in AWC(OFF. In addition, mir-71 is sufficient to inhibit tir-1 expression in AWC through the mir-71 complementary site in the tir-1 3' UTR. Our genetic studies suggest that mir-71 acts downstream of nsy-4 and nsy-5 to promote the AWC(ON identity in a cell autonomous manner. Furthermore, the stability of mature mir-71 is dependent on nsy-4 and nsy-5. Together, these results provide insight into the mechanism by which nsy-4 and nsy-5 inhibit calcium signaling to establish stochastic asymmetric AWC differentiation.

  2. Protein annotation from protein interaction networks and Gene Ontology.

    Science.gov (United States)

    Nguyen, Cao D; Gardiner, Katheleen J; Cios, Krzysztof J

    2011-10-01

    We introduce a novel method for annotating protein function that combines Naïve Bayes and association rules, and takes advantage of the underlying topology in protein interaction networks and the structure of graphs in the Gene Ontology. We apply our method to proteins from the Human Protein Reference Database (HPRD) and show that, in comparison with other approaches, it predicts protein functions with significantly higher recall with no loss of precision. Specifically, it achieves 51% precision and 60% recall versus 45% and 26% for Majority and 24% and 61% for χ²-statistics, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Association between receptor protein-tyrosine phosphatase RPTPalpha and the Grb2 adaptor. Dual Src homology (SH) 2/SH3 domain requirement and functional consequences

    DEFF Research Database (Denmark)

    Su, J; Yang, L T; Sap, J

    1996-01-01

    domain in Grb2 (, ). We show here that association of Grb2 with RPTPalpha also involves a critical function for the C-terminal SH3 domain of Grb2. Furthermore, Grb2 SH3 binding peptides interfere with RPTPalpha-Grb2 association in vitro, and the RPTPalpha protein can dissociate the Grb2-Sos complex...... in vivo. These observations constitute a novel mode of Grb2 association and suggest a model in which association with a tyrosine-phosphorylated protein restricts the repertoire of SH3 binding proteins with which Grb2 can simultaneously interact. The function of the Tyr798 tyrosine phosphorylation/Grb2...... binding site in RPTPalpha was studied further by expression of wild type or mutant RPTPalpha proteins in PC12 cells. In these cells, wild type RPTPalpha interferes with acidic fibroblast growth factor-induced neurite outgrowth; this effect requires both the catalytic activity and the Grb2 binding Tyr798...

  4. 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...

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

    Science.gov (United States)

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

    2016-02-01

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

  6. The adaptor molecule signaling lymphocytic activation molecule (SLAM)-associated protein (SAP) is essential in mechanisms involving the Fyn tyrosine kinase for induction and progression of collagen-induced arthritis.

    Science.gov (United States)

    Zhong, Ming-Chao; Veillette, André

    2013-11-01

    Signaling lymphocytic activation molecule-associated protein (SAP) is an Src homology 2 domain-only adaptor involved in multiple immune cell functions. It has also been linked to immunodeficiencies and autoimmune diseases, such as systemic lupus erythematosus. Here, we examined the role and mechanism of action of SAP in autoimmunity using a mouse model of autoimmune arthritis, collagen-induced arthritis (CIA). We found that SAP was essential for development of CIA in response to collagen immunization. It was also required for production of collagen-specific antibodies, which play a key role in disease pathogenesis. These effects required SAP expression in T cells, not in B cells. In mice immunized with a high dose of collagen, the activity of SAP was nearly independent of its ability to bind the protein tyrosine kinase Fyn and correlated with the capacity of SAP to promote full differentiation of follicular T helper (TFH) cells. However, with a lower dose of collagen, the role of SAP was more dependent on Fyn binding, suggesting that additional mechanisms other than TFH cell differentiation were involved. Further studies suggested that this might be due to a role of the SAP-Fyn interaction in natural killer T cell development through the ability of SAP-Fyn to promote Vav-1 activation. We also found that removal of SAP expression during progression of CIA attenuated disease severity. However, it had no effect on disease when CIA was clinically established. Together, these results indicate that SAP plays an essential role in CIA because of Fyn-independent and Fyn-dependent effects on TFH cells and, possibly, other T cell types.

  7. Molecular cloning of the mouse grb2 gene: differential interaction of the Grb2 adaptor protein with epidermal growth factor and nerve growth factor receptors.

    OpenAIRE

    Suen, K L; Bustelo, X R; Pawson, T; Barbacid, M

    1993-01-01

    We report the isolation and molecular characterization of the mouse grb2 gene. The product of this gene, the Grb2 protein, is highly related to the Caenorhabditis elegans sem-5 gene product and the human GRB2 protein and displays the same SH3-SH2-SH3 structural motifs. In situ hybridization studies revealed that the mouse grb2 gene is widely expressed throughout embryonic development (E9.5 to P0). However, grb2 transcripts are not uniformly distributed, and in certain tissues (e.g., thymus) t...

  8. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  9. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  10. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

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

    Science.gov (United States)

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

    2012-01-01

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

  12. 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

  13. Coupling between p210bcr-abl and Shc and Grb2 adaptor proteins in hematopoietic cells permits growth factor receptor-independent link to ras activation pathway.

    Science.gov (United States)

    Tauchi, T; Boswell, H S; Leibowitz, D; Broxmeyer, H E

    1994-01-01

    Enforced expression of p210bcr-abl transforms interleukin 3 (IL-3)-dependent hematopoietic cell lines to growth factor-independent proliferation. It has been demonstrated that nonreceptor tyrosine kinase oncogenes may couple to the p21ras pathway to exert their transforming effect. In particular, p210bcr-abl was recently found to effect p21ras activation in hematopoietic cells. In this context, experiments were performed to evaluate a protein signaling pathway by which p210bcr-abl might regulate p21ras. It was asked whether Shc p46/p52, a protein containing a src-homology region 2 (SH2) domain, and known to function upstream from p21ras, might form specific complexes with p210bcr-abl and thus, possibly alter p21ras activity by coupling to the guanine nucleotide exchange factor (Sos/CDC25) through the Grb2 protein-Sos complex. This latter complex has been previously demonstrated to occur ubiquitously. We found that p210bcr-abl formed a specific complex with Shc and with Grb2 in three different murine cell lines transfected with a p210bcr-abl expression vector. There appeared to be a higher order complex containing Shc, Grb2, and bcr-abl proteins. In contrast to p210bcr-abl transformed cells, in which there was constitutive tight association between Grb2 and Shc, binding between Grb2 and Shc was Steel factor (SLF)-dependent in a SLF-responsive, nontransformed parental cell line. The SLF-dependent association between Grb2 and Shc in nontransformed cells involved formation of a complex of Grb2 with c-kit receptor after SLF treatment. Thus, p210bcr-abl appears to function in a hematopoietic p21ras activation pathway to allow growth factor-independent coupling between Grb2, which exists in a complex with the guanine nucleotide exchange factor (Sos), and p21ras. Shc may not be required for Grb2-c-kit interaction, because it fails to bind strongly to c-kit.

  14. Phosphorylation of the adaptor protein SH2B1β regulates its ability to enhance growth hormone-dependent macrophage motility

    OpenAIRE

    Su, Hsiao-Wen; Lanning, Nathan J.; Morris, David L.; Argetsinger, Lawrence S.; Lumeng, Carey N.; Carter-Su, Christin

    2013-01-01

    Previous studies have shown that growth hormone (GH) recruits the adapter protein SH2B1β to the GH-activated, GH receptor-associated tyrosine kinase JAK2, implicating SH2B1β in GH-dependent actin cytoskeleton remodeling, and suggesting that phosphorylation at serines 161 and 165 in SH2B1β releases SH2B1β from the plasma membrane. Here, we examined the role of SH2B1β in GH regulation of macrophage migration. We show that GH stimulates migration of cultured RAW264.7 macrophages, and primary cul...

  15. Differential association of the Na+/H+ Exchanger Regulatory Factor (NHERF) family of adaptor proteins with the raft- and the non-raft brush border membrane fractions of NHE3.

    Science.gov (United States)

    Sultan, Ayesha; Luo, Min; Yu, Qin; Riederer, Brigitte; Xia, Weiliang; Chen, Mingmin; Lissner, Simone; Gessner, Johannes E; Donowitz, Mark; Yun, C Chris; deJonge, Hugo; Lamprecht, Georg; Seidler, Ursula

    2013-01-01

    Trafficking, brush border membrane (BBM) retention, and signal-specific regulation of the Na+/H+ exchanger NHE3 is regulated by the Na+/H+ Exchanger Regulatory Factor (NHERF) family of PDZ-adaptor proteins, which enable the formation of multiprotein complexes. It is unclear, however, what determines signal specificity of these NHERFs. Thus, we studied the association of NHE3, NHERF1 (EBP50), NHERF2 (E3KARP), and NHERF3 (PDZK1) with lipid rafts in murine small intestinal BBM. Detergent resistant membranes ("lipid rafts") were isolated by floatation of Triton X-incubated small intestinal BBM from a variety of knockout mouse strains in an Optiprep step gradient. Acid-activated NHE3 activity was measured fluorometrically in BCECF-loaded microdissected villi, or by assessment of CO2/HCO3(-) mediated increase in fluid absorption in perfused jejunal loops of anethetized mice. NHE3 was found to partially associate with lipid rafts in the native BBM, and NHE3 raft association had an impact on NHE3 transport activity and regulation in vivo. NHERF1, 2 and 3 were differentially distributed to rafts and non-rafts, with NHERF2 being most raft-associated and NHERF3 entirely non-raft associated. NHERF2 expression enhanced the localization of NHE3 to membrane rafts. The use of acid sphingomyelinase-deficient mice, which have altered membrane lipid as well as lipid raft composition, allowed us to test the validity of the lipid raft concept in vivo. The differential association of the NHERFs with the raft-associated and the non-raft fraction of NHE3 in the brush border membrane is one component of the differential and signal-specific NHE3 regulation by the different NHERFs. © 2013 S. Karger AG, Basel.

  16. Finding local communities in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-09-18

    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. 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. 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, making our application useful for biologists who wish to

  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. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  20. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  1. Adaptor assembly for coupling turbine blades to rotor disks

    Science.gov (United States)

    Garcia-Crespo, Andres Jose; Delvaux, John McConnell

    2014-09-23

    An adaptor assembly for coupling a blade root of a turbine blade to a root slot of a rotor disk is described. The adaptor assembly includes a turbine blade having a blade root and an adaptor body having an adaptor root. The adaptor body defines a slot having an open end configured to receive the blade root of the turbine blade such that the adaptor root of the adaptor body and the blade root of the turbine blade are adjacent to one another when the blade root of the turbine blade is positioned within the slot. Both the adaptor root of the adaptor body and the blade root of the turbine blade are configured to be received within the root slot of the rotor disk.

  2. Transmembrane adaptor proteins: organizers of immunoreceptor signalling

    Czech Academy of Sciences Publication Activity Database

    Hořejší, Václav; Zhang, W.; Schraven, B.

    2004-01-01

    Roč. 4, č. 8 (2004), s. 603-616 ISSN 1474-1733 R&D Projects: GA MŠk LN00A026 Institutional research plan: CEZ:AV0Z5052915 Keywords : immunoreceptor * signalling Subject RIV: EC - Immunology Impact factor: 32.695, year: 2004

  3. Pythoscape: A framework for generation of large protein similarity networks

    OpenAIRE

    Babbitt, Patricia; Barber, AE; Babbitt, PC

    2012-01-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among pr

  4. Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

    Directory of Open Access Journals (Sweden)

    Guang Hu

    2017-01-01

    Full Text Available The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM and Protein Contact Network (PCN are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

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

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available 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 uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  6. Construction of ontology augmented networks for protein complex prediction.

    Science.gov (United States)

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

    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 augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  7. A scored human protein-protein interaction network to catalyze genomic interpretation

    DEFF Research Database (Denmark)

    Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B

    2017-01-01

    Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...

  8. 21 CFR 870.3620 - Pacemaker lead adaptor.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Pacemaker lead adaptor. 870.3620 Section 870.3620...) MEDICAL DEVICES CARDIOVASCULAR DEVICES Cardiovascular Prosthetic Devices § 870.3620 Pacemaker lead adaptor. (a) Identification. A pacemaker lead adaptor is a device used to adapt a pacemaker lead so that it...

  9. Styles of Creativity: Adaptors and Innovators in a Singapore Context

    Science.gov (United States)

    Ee, Jessie; Seng, Tan Oon; Kwang, Ng Aik

    2007-01-01

    Kirton (1976) described two creative styles, namely adaptors and innovators. Adaptors prefer to "do things better" whilst, innovators prefer to "do things differently". This study explored the relationship between two creative styles (adaptor and innovator) and the Big Five personality traits (extraversion, agreeableness, conscientiousness,…

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

    Science.gov (United States)

    Tripathi, Vijay; Gupta, Dwijendra Kumar

    2014-01-01

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

  11. Protein-Protein Interaction Network and Gene Ontology

    Science.gov (United States)

    Choi, Yunkyu; Kim, Seok; Yi, Gwan-Su; Park, Jinah

    Evolution of computer technologies makes it possible to access a large amount and various kinds of biological data via internet such as DNA sequences, proteomics data and information discovered about them. It is expected that the combination of various data could help researchers find further knowledge about them. Roles of a visualization system are to invoke human abilities to integrate information and to recognize certain patterns in the data. Thus, when the various kinds of data are examined and analyzed manually, an effective visualization system is an essential part. One instance of these integrated visualizations can be combination of protein-protein interaction (PPI) data and Gene Ontology (GO) which could help enhance the analysis of PPI network. We introduce a simple but comprehensive visualization system that integrates GO and PPI data where GO and PPI graphs are visualized side-by-side and supports quick reference functions between them. Furthermore, the proposed system provides several interactive visualization methods for efficiently analyzing the PPI network and GO directedacyclic- graph such as context-based browsing and common ancestors finding.

  12. Expression analysis of the Toll-like receptor and TIR domain adaptor families of zebrafish.

    NARCIS (Netherlands)

    Meijer, A.H.; Krens, SF Gabby; Rodriguez, IA Medina; He, S; Bitter, W.; Snaar-Jagalska, B Ewa; Spaink, H.P.

    2004-01-01

    The zebrafish genomic sequence database was analysed for the presence of genes encoding members of the Toll-like receptors (TLR) and interleukin receptors (IL-R) and associated adaptor proteins containing a TIR domain. The resulting predictions show the presence of one or more counterparts for the

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

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

  15. Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

    NARCIS (Netherlands)

    Rahmani, Hossein

    2012-01-01

    This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large

  16. Interrogating the architecture of protein assemblies and protein interaction networks by cross-linking mass spectrometry

    NARCIS (Netherlands)

    Liu, Fan; Heck, Albert J R

    2015-01-01

    Proteins are involved in almost all processes of the living cell. They are organized through extensive networks of interaction, by tightly bound macromolecular assemblies or more transiently via signaling nodes. Therefore, revealing the architecture of protein complexes and protein interaction

  17. 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

  18. The Role of the Clathrin Adaptor AP-1: Polarized Sorting and Beyond

    Directory of Open Access Journals (Sweden)

    Fubito Nakatsu

    2014-11-01

    Full Text Available The selective transport of proteins or lipids by vesicular transport is a fundamental process supporting cellular physiology. The budding process involves cargo sorting and vesicle formation at the donor membrane and constitutes an important process in vesicular transport. This process is particularly important for the polarized sorting in epithelial cells, in which the cargo molecules need to be selectively sorted and transported to two distinct destinations, the apical or basolateral plasma membrane. Adaptor protein (AP-1, a member of the AP complex family, which includes the ubiquitously expressed AP-1A and the epithelium-specific AP-1B, regulates polarized sorting at the trans-Golgi network and/or at the recycling endosomes. A growing body of evidence, especially from studies using model organisms and animals, demonstrates that the AP-1-mediated polarized sorting supports the development and physiology of multi-cellular units as functional organs and tissues (e.g., cell fate determination, inflammation and gut immune homeostasis. Furthermore, a possible involvement of AP-1B in the pathogenesis of human diseases, such as Crohn’s disease and cancer, is now becoming evident. These data highlight the significant contribution of AP-1 complexes to the physiology of multicellular organisms, as master regulators of polarized sorting in epithelial cells.

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

    Science.gov (United States)

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

    2014-12-01

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

  20. Conformational changes in the AAA ATPase p97–p47 adaptor complex

    Science.gov (United States)

    Beuron, Fabienne; Dreveny, Ingrid; Yuan, Xuemei; Pye, Valerie E; Mckeown, Ciaran; Briggs, Louise C; Cliff, Matthew J; Kaneko, Yayoi; Wallis, Russell; Isaacson, Rivka L; Ladbury, John E; Matthews, Steve J; Kondo, Hisao; Zhang, Xiaodong; Freemont, Paul S

    2006-01-01

    The AAA+ATPase p97/VCP, helped by adaptor proteins, exerts its essential role in cellular events such as endoplasmic reticulum-associated protein degradation or the reassembly of Golgi, ER and the nuclear envelope after mitosis. Here, we report the three-dimensional cryo-electron microscopy structures at ∼20 Å resolution in two nucleotide states of the endogenous hexameric p97 in complex with a recombinant p47 trimer, one of the major p97 adaptor proteins involved in membrane fusion. Depending on the nucleotide state, we observe the p47 trimer to be in two distinct arrangements on top of the p97 hexamer. By combining the EM data with NMR and other biophysical measurements, we propose a model of ATP-dependent p97(N) domain motions that lead to a rearrangement of p47 domains, which could result in the disassembly of target protein complexes. PMID:16601695

  1. Unveiling protein functions through the dynamics of the interaction network.

    Directory of Open Access Journals (Sweden)

    Irene Sendiña-Nadal

    Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.

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

    Science.gov (United States)

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

    2013-08-23

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

  3. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  4. Enhancing the Functional Content of Eukaryotic Protein Interaction Networks

    Science.gov (United States)

    Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean

    2014-01-01

    Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489

  5. A tensegrity model for hydrogen bond networks in proteins

    OpenAIRE

    Bywater, Robert P.

    2017-01-01

    Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger − c...

  6. 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.

  7. Pythoscape: a framework for generation of large protein similarity networks.

    Science.gov (United States)

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.

  8. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  9. Manufacturing and QA of adaptors for LHC

    International Nuclear Information System (INIS)

    Madhu Murthy, V.; Dwivedi, J.; Goswami, S.G.; Soni, H.C.; Mainaud Durand, H.; Quesnel, J.P.; )

    2006-01-01

    The LHC low beta quadrupoles, have very tight alignment tolerances and are located in areas with strong radiation field. They require remote re-alignment, by motorized jacks, based on the feedback of alignment sensors of each magnet. Jacks designed to support arc cryomagnets of LHC are modified and motorized with the help of adaptors. Two types of adapters, for vertical and transverse axes of the jacks, were developed and supplied through collaboration between RRCAT, DAE, India and CERN, Geneva. This paper describes their functional requirements, manufacture and quality assurance (QA). (author)

  10. Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

    Science.gov (United States)

    Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

    2017-07-25

    Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

  11. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  12. Analysis of protein folds using protein contact networks

    Indian Academy of Sciences (India)

    is a well-recognized classification system of proteins, which is based on manual in- ... can easily correspond to the information in the 2D matrix. ..... [7] U K Muppirala and Zhijun Li, Protein Engineering, Design & Selection 19, 265 (2006).

  13. TRAM is involved in IL-18 signaling and functions as a sorting adaptor for MyD88.

    Directory of Open Access Journals (Sweden)

    Hidenori Ohnishi

    Full Text Available MyD88, a Toll/interleukin-1 receptor homology (TIR domain-containing adaptor protein, mediates signals from the Toll-like receptors (TLR or IL-1/IL-18 receptors to downstream kinases. In MyD88-dependent TLR4 signaling, the function of MyD88 is enhanced by another TIR domain-containing adaptor, Mal/TIRAP, which brings MyD88 to the plasma membrane and promotes its interaction with the cytosolic region of TLR4. Hence, Mal is recognized as the "sorting adaptor" for MyD88. In this study, a direct interaction between MyD88-TIR and another membrane-sorting adaptor, TRAM/TICAM-2, was demonstrated in vitro. Cell-based assays including RNA interference experiments and TRAM deficient mice revealed that the interplay between MyD88 and TRAM in cells is important in mediating IL-18 signal transduction. Live cell imaging further demonstrated the co-localized accumulation of MyD88 and TRAM in the membrane regions in HEK293 cells. These findings suggest that TRAM serves as the sorting adaptor for MyD88 in IL-18 signaling, which then facilitates the signal transduction. The binding sites for TRAM are located in the TIR domain of MyD88 and actually overlap with the binding sites for Mal. MyD88, the multifunctional signaling adaptor that works together with most of the TLR members and with the IL-1/IL-18 receptors, can interact with two distinct sorting adaptors, TRAM and Mal, in a conserved manner in a distinct context.

  14. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

  15. Evolution of a protein domain interaction network

    International Nuclear Information System (INIS)

    Li-Feng, Gao; Jian-Jun, Shi; Shan, Guan

    2010-01-01

    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)

  16. 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

  17. NatalieQ: A web server for protein-protein interaction network querying

    NARCIS (Netherlands)

    El-Kebir, M.; Brandt, B.W.; Heringa, J.; Klau, G.W.

    2014-01-01

    Background Molecular interactions need to be taken into account to adequately model the complex behavior of biological systems. These interactions are captured by various types of biological networks, such as metabolic, gene-regulatory, signal transduction and protein-protein interaction networks.

  18. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  19. Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design.

    Science.gov (United States)

    Maguire, Jack B; Boyken, Scott E; Baker, David; Kuhlman, Brian

    2018-05-08

    Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

  20. Network based approaches reveal clustering in protein point patterns

    Science.gov (United States)

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

    2014-03-01

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

  1. Topological properties of complex networks in protein structures

    Science.gov (United States)

    Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

    2014-03-01

    We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

  2. In silico modeling of the yeast protein and protein family interaction network

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  3. Functional modules by relating protein interaction networks and gene expression.

    Science.gov (United States)

    Tornow, Sabine; Mewes, H W

    2003-11-01

    Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

  4. 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

    2016-01-01

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

  5. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    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.

  6. Advanced path sampling of the kinetic network of small proteins

    NARCIS (Netherlands)

    Du, W.

    2014-01-01

    This thesis is focused on developing advanced path sampling simulation methods to study protein folding and unfolding, and to build kinetic equilibrium networks describing these processes. In Chapter 1 the basic knowledge of protein structure and folding theories were introduced and a brief overview

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

    Science.gov (United States)

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

    2015-06-01

    Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-12-01

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

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

    Science.gov (United States)

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

    2010-07-01

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

  10. Emergence of modularity and disassortativity in protein-protein interaction networks.

    Science.gov (United States)

    Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

    2010-12-01

    In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

  11. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

    Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

    2017-01-01

    Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

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

    International Nuclear Information System (INIS)

    Engberg, Kristin; Frank, Curtis W

    2011-01-01

    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 gel = 0.16 ± 0.02 x 10 -8 cm 2 s -1 ) and the fastest diffusion occurring through higher molecular weight, low-solid-content networks (D gel = 11.05 ± 0.43 x 10 -8 cm 2 s -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.

  13. A tensegrity model for hydrogen bond networks in proteins

    Directory of Open Access Journals (Sweden)

    Robert P. Bywater

    2017-05-01

    Full Text Available Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger − covalent bonds connecting the atoms in the molecular skeleton or weaker forces like the so-called hydrophobic interactions. It has been demonstrated that the latter operate independently from hydrogen bonds. Each category of interaction must, if the protein is to have a stable structure, balance out. The hypothesis here is that the entire hydrogen bond network is in balance without any compensating contributions from other types of interaction. For sidechain-sidechain, sidechain-backbone and backbone-backbone hydrogen bonds in proteins, tensegrity balance (“closure” is required over the entire length of the polypeptide chain that defines individually folding units in globular proteins (“domains” as well as within the repeating elements in fibrous proteins that consist of extended chain structures. There is no closure to be found in extended structures that do not have repeating elements. This suggests an explanation as to why globular domains, as well as the repeat units in fibrous proteins, have to have a defined number of residues. Apart from networks of sidechain-sidechain hydrogen bonds there are certain key points at which this closure is achieved in the sidechain-backbone hydrogen bonds and these are associated with demarcation points at the start or end of stretches of secondary structure. Together, these three categories of hydrogen bond achieve the closure that is necessary for the stability of globular protein domains as well as repeating

  14. A tensegrity model for hydrogen bond networks in proteins.

    Science.gov (United States)

    Bywater, Robert P

    2017-05-01

    Hydrogen-bonding networks in proteins considered as structural tensile elements are in balance separately from any other stabilising interactions that may be in operation. The hydrogen bond arrangement in the network is reminiscent of tensegrity structures in architecture and sculpture. Tensegrity has been discussed before in cells and tissues and in proteins. In contrast to previous work only hydrogen bonds are studied here. The other interactions within proteins are either much stronger - covalent bonds connecting the atoms in the molecular skeleton or weaker forces like the so-called hydrophobic interactions. It has been demonstrated that the latter operate independently from hydrogen bonds. Each category of interaction must, if the protein is to have a stable structure, balance out. The hypothesis here is that the entire hydrogen bond network is in balance without any compensating contributions from other types of interaction. For sidechain-sidechain, sidechain-backbone and backbone-backbone hydrogen bonds in proteins, tensegrity balance ("closure") is required over the entire length of the polypeptide chain that defines individually folding units in globular proteins ("domains") as well as within the repeating elements in fibrous proteins that consist of extended chain structures. There is no closure to be found in extended structures that do not have repeating elements. This suggests an explanation as to why globular domains, as well as the repeat units in fibrous proteins, have to have a defined number of residues. Apart from networks of sidechain-sidechain hydrogen bonds there are certain key points at which this closure is achieved in the sidechain-backbone hydrogen bonds and these are associated with demarcation points at the start or end of stretches of secondary structure. Together, these three categories of hydrogen bond achieve the closure that is necessary for the stability of globular protein domains as well as repeating elements in fibrous proteins.

  15. Specificity and evolvability in eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2007-02-01

    Full Text Available Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10(-5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 10(3 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell's requirement for different degrees of protein binding specificity.

  16. 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.

  17. Droplet networks with incorporated protein diodes show collective properties

    Science.gov (United States)

    Maglia, Giovanni; Heron, Andrew J.; Hwang, William L.; Holden, Matthew A.; Mikhailova, Ellina; Li, Qiuhong; Cheley, Stephen; Bayley, Hagan

    2009-07-01

    Recently, we demonstrated that submicrolitre aqueous droplets submerged in an apolar liquid containing lipid can be tightly connected by means of lipid bilayers to form networks. Droplet interface bilayers have been used for rapid screening of membrane proteins and to form asymmetric bilayers with which to examine the fundamental properties of channels and pores. Networks, meanwhile, have been used to form microscale batteries and to detect light. Here, we develop an engineered protein pore with diode-like properties that can be incorporated into droplet interface bilayers in droplet networks to form devices with electrical properties including those of a current limiter, a half-wave rectifier and a full-wave rectifier. The droplet approach, which uses unsophisticated components (oil, lipid, salt water and a simple pore), can therefore be used to create multidroplet networks with collective properties that cannot be produced by droplet pairs.

  18. The DIMA web resource--exploring the protein domain network.

    Science.gov (United States)

    Pagel, Philipp; Oesterheld, Matthias; Stümpflen, Volker; Frishman, Dmitrij

    2006-04-15

    Conserved domains represent essential building blocks of most known proteins. Owing to their role as modular components carrying out specific functions they form a network based both on functional relations and direct physical interactions. We have previously shown that domain interaction networks provide substantially novel information with respect to networks built on full-length protein chains. In this work we present a comprehensive web resource for exploring the Domain Interaction MAp (DIMA), interactively. The tool aims at integration of multiple data sources and prediction techniques, two of which have been implemented so far: domain phylogenetic profiling and experimentally demonstrated domain contacts from known three-dimensional structures. A powerful yet simple user interface enables the user to compute, visualize, navigate and download domain networks based on specific search criteria. http://mips.gsf.de/genre/proj/dima

  19. Discovering disease-associated genes in weighted protein-protein interaction networks

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Stanley, H. Eugene

    2018-04-01

    Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

  20. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  1. Chaperone-protease networks in mitochondrial protein homeostasis.

    Science.gov (United States)

    Voos, Wolfgang

    2013-02-01

    As essential organelles, mitochondria are intimately integrated into the metabolism of a eukaryotic cell. The maintenance of the functional integrity of the mitochondrial proteome, also termed protein homeostasis, is facing many challenges both under normal and pathological conditions. First, since mitochondria are derived from bacterial ancestor cells, the proteins in this endosymbiotic organelle have a mixed origin. Only a few proteins are encoded on the mitochondrial genome, most genes for mitochondrial proteins reside in the nuclear genome of the host cell. This distribution requires a complex biogenesis of mitochondrial proteins, which are mostly synthesized in the cytosol and need to be imported into the organelle. Mitochondrial protein biogenesis usually therefore comprises complex folding and assembly processes to reach an enzymatically active state. In addition, specific protein quality control (PQC) processes avoid an accumulation of damaged or surplus polypeptides. Mitochondrial protein homeostasis is based on endogenous enzymatic components comprising a diverse set of chaperones and proteases that form an interconnected functional network. This review describes the different types of mitochondrial proteins with chaperone functions and covers the current knowledge of their roles in protein biogenesis, folding, proteolytic removal and prevention of aggregation, the principal reactions of protein homeostasis. This article is part of a Special Issue entitled: Protein Import and Quality Control in Mitochondria and Plastids. Copyright © 2012 Elsevier B.V. All rights reserved.

  2. 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.

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

    KAUST Repository

    Chowdhary, Rajesh; Tan, Sin Lam; Zhang, Jinfeng; Karnik, Shreyas; Bajic, Vladimir B.; Liu, Jun S.

    2012-01-01

    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.

  4. The Oncogenic Palmitoyi-Protein Network in Prostate Cancer

    Science.gov (United States)

    2015-06-01

    was performed by comparing LFQ intensities computed by MaxQuant.16 After statistical analysis, we identified 29 significantly downregulated and 32... statistical analysis, 30 candidate palmitoyl-proteins with an H/L ratio cutoff of 0.667 were accepted as candidate DHHC3 substrates (Table 1). Among...proteomics, we identified a gigantic palmitoyl-protein network regulated by caveolin-1. Moreover, by integrating RNA interference (RNAi), triplex SILAC, and

  5. Protein interaction networks by proteome peptide scanning.

    Directory of Open Access Journals (Sweden)

    Christiane Landgraf

    2004-01-01

    Full Text Available A substantial proportion of protein interactions relies on small domains binding to short peptides in the partner proteins. Many of these interactions are relatively low affinity and transient, and they impact on signal transduction. However, neither the number of potential interactions mediated by each domain nor the degree of promiscuity at a whole proteome level has been investigated. We have used a combination of phage display and SPOT synthesis to discover all the peptides in the yeast proteome that have the potential to bind to eight SH3 domains. We first identified the peptides that match a relaxed consensus, as deduced from peptides selected by phage display experiments. Next, we synthesized all the matching peptides at high density on a cellulose membrane, and we probed them directly with the SH3 domains. The domains that we have studied were grouped by this approach into five classes with partially overlapping specificity. Within the classes, however, the domains display a high promiscuity and bind to a large number of common targets with comparable affinity. We estimate that the yeast proteome contains as few as six peptides that bind to the Abp1 SH3 domain with a dissociation constant lower than 100 microM, while it contains as many as 50-80 peptides with corresponding affinity for the SH3 domain of Yfr024c. All the targets of the Abp1 SH3 domain, identified by this approach, bind to the native protein in vivo, as shown by coimmunoprecipitation experiments. Finally, we demonstrate that this strategy can be extended to the analysis of the entire human proteome. We have developed an approach, named WISE (whole interactome scanning experiment, that permits rapid and reliable identification of the partners of any peptide recognition module by peptide scanning of a proteome. Since the SPOT synthesis approach is semiquantitative and provides an approximation of the dissociation constants of the several thousands of interactions that are

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

    Science.gov (United States)

    Young-Rae Cho; Yanan Xin; Speegle, Greg

    2015-01-01

    Because most complex genetic diseases are caused by defects of cell signaling, illuminating a signaling cascade is essential for understanding their mechanisms. We present three novel computational algorithms to reconstruct signaling networks between a starting protein and an ending protein using genome-wide protein-protein interaction (PPI) networks and gene ontology (GO) annotation data. A signaling network is represented as a directed acyclic graph in a merged form of multiple linear pathways. An advanced semantic similarity metric is applied for weighting PPIs as the preprocessing of all three methods. The first algorithm repeatedly extends the list of nodes based on path frequency towards an ending protein. The second algorithm repeatedly appends edges based on the occurrence of network motifs which indicate the link patterns more frequently appearing in a PPI network than in a random graph. The last algorithm uses the information propagation technique which iteratively updates edge orientations based on the path strength and merges the selected directed edges. Our experimental results demonstrate that the proposed algorithms achieve higher accuracy than previous methods when they are tested on well-studied pathways of S. cerevisiae. Furthermore, we introduce an interactive web application tool, called P-Finder, to visualize reconstructed signaling networks.

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

    Science.gov (United States)

    Poirot, Olivier; Timsit, Youri

    2016-05-01

    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.

  8. Evolution of an intricate J-protein network driving protein disaggregation in eukaryotes.

    Science.gov (United States)

    Nillegoda, Nadinath B; Stank, Antonia; Malinverni, Duccio; Alberts, Niels; Szlachcic, Anna; Barducci, Alessandro; De Los Rios, Paolo; Wade, Rebecca C; Bukau, Bernd

    2017-05-15

    Hsp70 participates in a broad spectrum of protein folding processes extending from nascent chain folding to protein disaggregation. This versatility in function is achieved through a diverse family of J-protein cochaperones that select substrates for Hsp70. Substrate selection is further tuned by transient complexation between different classes of J-proteins, which expands the range of protein aggregates targeted by metazoan Hsp70 for disaggregation. We assessed the prevalence and evolutionary conservation of J-protein complexation and cooperation in disaggregation. We find the emergence of a eukaryote-specific signature for interclass complexation of canonical J-proteins. Consistently, complexes exist in yeast and human cells, but not in bacteria, and correlate with cooperative action in disaggregation in vitro. Signature alterations exclude some J-proteins from networking, which ensures correct J-protein pairing, functional network integrity and J-protein specialization. This fundamental change in J-protein biology during the prokaryote-to-eukaryote transition allows for increased fine-tuning and broadening of Hsp70 function in eukaryotes.

  9. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

    Directory of Open Access Journals (Sweden)

    Wan Li

    Full Text Available The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial. Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

  10. A Global Protein Kinase and Phosphatase Interaction Network in Yeast

    Science.gov (United States)

    Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

    2011-01-01

    The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

  11. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    Science.gov (United States)

    Li, Hui; Liu, Chunmei

    2014-06-14

    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 internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

  12. A Physical Interaction Network of Dengue Virus and Human Proteins*

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

    2011-01-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

  13. A physical interaction network of dengue virus and human proteins.

    Science.gov (United States)

    Khadka, Sudip; Vangeloff, Abbey D; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J; Perera, Rushika; LaCount, Douglas J

    2011-12-01

    Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

  14. Completing sparse and disconnected protein-protein network by deep learning.

    Science.gov (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H

    2018-03-22

    Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

  15. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks.

    Science.gov (United States)

    Benzekry, Sebastian; Tuszynski, Jack A; Rietman, Edward A; Lakka Klement, Giannoula

    2015-05-28

    The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. We observed a linear correlation of R = -0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger

  16. ATP binding to p97/VCP D1 domain regulates selective recruitment of adaptors to its proximal N-domain.

    Directory of Open Access Journals (Sweden)

    Wei Sheng Chia

    Full Text Available p97/Valosin-containing protein (VCP is a member of the AAA-ATPase family involved in many cellular processes including cell division, intracellular trafficking and extraction of misfolded proteins in endoplasmic reticulum-associated degradation (ERAD. It is a homohexamer with each subunit containing two tandem D1 and D2 ATPase domains and N- and C-terminal regions that function as adaptor protein binding domains. p97/VCP is directed to its many different functional pathways by associating with various adaptor proteins. The regulation of the recruitment of the adaptor proteins remains unclear. Two adaptor proteins, Ufd1/Npl4 and p47, which bind exclusively to the p97/VCP N-domain and direct p97/VCP to either ERAD-related processes or homotypic fusion of Golgi fragments, were studied here. Surface plasmon resonance biosensor-based assays allowed the study of binding kinetics in real time. In competition experiments, it was observed that in the presence of ATP, Ufd1/Npl4 was able to compete more effectively with p47 for binding to p97/VCP. By using non-hydrolysable ATP analogues and the hexameric truncated p97/N-D1 fragment, it was shown that binding rather than hydrolysis of ATP to the proximal D1 domain strengthened the Ufd1/Npl4 association with the N-domain, thus regulating the recruitment of either Ufd1/Npl4 or p47. This novel role of ATP and an assigned function to the D1 AAA-ATPase domain link the multiple functions of p97/VCP to the metabolic status of the cell.

  17. ATP binding to p97/VCP D1 domain regulates selective recruitment of adaptors to its proximal N-domain.

    Science.gov (United States)

    Chia, Wei Sheng; Chia, Diana Xueqi; Rao, Feng; Bar Nun, Shoshana; Geifman Shochat, Susana

    2012-01-01

    p97/Valosin-containing protein (VCP) is a member of the AAA-ATPase family involved in many cellular processes including cell division, intracellular trafficking and extraction of misfolded proteins in endoplasmic reticulum-associated degradation (ERAD). It is a homohexamer with each subunit containing two tandem D1 and D2 ATPase domains and N- and C-terminal regions that function as adaptor protein binding domains. p97/VCP is directed to its many different functional pathways by associating with various adaptor proteins. The regulation of the recruitment of the adaptor proteins remains unclear. Two adaptor proteins, Ufd1/Npl4 and p47, which bind exclusively to the p97/VCP N-domain and direct p97/VCP to either ERAD-related processes or homotypic fusion of Golgi fragments, were studied here. Surface plasmon resonance biosensor-based assays allowed the study of binding kinetics in real time. In competition experiments, it was observed that in the presence of ATP, Ufd1/Npl4 was able to compete more effectively with p47 for binding to p97/VCP. By using non-hydrolysable ATP analogues and the hexameric truncated p97/N-D1 fragment, it was shown that binding rather than hydrolysis of ATP to the proximal D1 domain strengthened the Ufd1/Npl4 association with the N-domain, thus regulating the recruitment of either Ufd1/Npl4 or p47. This novel role of ATP and an assigned function to the D1 AAA-ATPase domain link the multiple functions of p97/VCP to the metabolic status of the cell.

  18. Interplay between chaperones and protein disorder promotes the evolution of protein networks.

    Directory of Open Access Journals (Sweden)

    Sebastian Pechmann

    2014-06-01

    Full Text Available Evolution is driven by mutations, which lead to new protein functions but come at a cost to protein stability. Non-conservative substitutions are of interest in this regard because they may most profoundly affect both function and stability. Accordingly, organisms must balance the benefit of accepting advantageous substitutions with the possible cost of deleterious effects on protein folding and stability. We here examine factors that systematically promote non-conservative mutations at the proteome level. Intrinsically disordered regions in proteins play pivotal roles in protein interactions, but many questions regarding their evolution remain unanswered. Similarly, whether and how molecular chaperones, which have been shown to buffer destabilizing mutations in individual proteins, generally provide robustness during proteome evolution remains unclear. To this end, we introduce an evolutionary parameter λ that directly estimates the rate of non-conservative substitutions. Our analysis of λ in Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens sequences reveals how co- and post-translationally acting chaperones differentially promote non-conservative substitutions in their substrates, likely through buffering of their destabilizing effects. We further find that λ serves well to quantify the evolution of intrinsically disordered proteins even though the unstructured, thus generally variable regions in proteins are often flanked by very conserved sequences. Crucially, we show that both intrinsically disordered proteins and highly re-wired proteins in protein interaction networks, which have evolved new interactions and functions, exhibit a higher λ at the expense of enhanced chaperone assistance. Our findings thus highlight an intricate interplay of molecular chaperones and protein disorder in the evolvability of protein networks. Our results illuminate the role of chaperones in enabling protein evolution, and underline the

  19. The Pch2 AAA+ ATPase promotes phosphorylation of the Hop1 meiotic checkpoint adaptor in response to synaptonemal complex defects.

    Science.gov (United States)

    Herruzo, Esther; Ontoso, David; González-Arranz, Sara; Cavero, Santiago; Lechuga, Ana; San-Segundo, Pedro A

    2016-09-19

    Meiotic cells possess surveillance mechanisms that monitor critical events such as recombination and chromosome synapsis. Meiotic defects resulting from the absence of the synaptonemal complex component Zip1 activate a meiosis-specific checkpoint network resulting in delayed or arrested meiotic progression. Pch2 is an evolutionarily conserved AAA+ ATPase required for the checkpoint-induced meiotic block in the zip1 mutant, where Pch2 is only detectable at the ribosomal DNA array (nucleolus). We describe here that high levels of the Hop1 protein, a checkpoint adaptor that localizes to chromosome axes, suppress the checkpoint defect of a zip1 pch2 mutant restoring Mek1 activity and meiotic cell cycle delay. We demonstrate that the critical role of Pch2 in this synapsis checkpoint is to sustain Mec1-dependent phosphorylation of Hop1 at threonine 318. We also show that the ATPase activity of Pch2 is essential for its checkpoint function and that ATP binding to Pch2 is required for its localization. Previous work has shown that Pch2 negatively regulates Hop1 chromosome abundance during unchallenged meiosis. Based on our results, we propose that, under checkpoint-inducing conditions, Pch2 also possesses a positive action on Hop1 promoting its phosphorylation and its proper distribution on unsynapsed chromosome axes. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks

    Science.gov (United States)

    LAVALLÉE-ADAM, MATHIEU; COULOMBE, BENOIT; BLANCHETTE, MATHIEU

    2015-01-01

    High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com. PMID:20377456

  1. 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.

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

    International Nuclear Information System (INIS)

    Goudarzi, Atta; Gokgoz, Nalan; Gill, Mona; Pinnaduwage, Dushanthi; Merico, Daniele; Wunder, Jay S.; Andrulis, Irene L.

    2013-01-01

    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

  3. In vitro and in vivo Analysis of the Binding of the C Terminus of the HDL Receptor Scavenger Receptor Class B type I (SR-BI) to the PDZ1 Domain of its Cytoplasmic Adaptor Protein PDZK1

    Energy Technology Data Exchange (ETDEWEB)

    O Kocher; G Birrane; K Tsukamoto; S Fenske; A Yesilaltay; R Pal; K Daniels; J Ladias; M Krieger

    2011-12-31

    The PDZ1 domain of the four PDZ domain-containing protein PDZK1 has been reported to bind the C terminus of the HDL receptor scavenger receptor class B, type I (SR-BI), and to control hepatic SR-BI expression and function. We generated wild-type (WT) and mutant murine PDZ1 domains, the mutants bearing single amino acid substitutions in their carboxylate binding loop (Lys(14)-Xaa(4)-Asn(19)-Tyr-Gly-Phe-Phe-Leu(24)), and measured their binding affinity for a 7-residue peptide corresponding to the C terminus of SR-BI ((503)VLQEAKL(509)). The Y20A and G21Y substitutions abrogated all binding activity. Surprisingly, binding affinities (K(d)) of the K14A and F22A mutants were 3.2 and 4.0 ?M, respectively, similar to 2.6 ?M measured for the WT PDZ1. To understand these findings, we determined the high resolution structure of WT PDZ1 bound to a 5-residue sequence from the C-terminal SR-BI ((505)QEAKL(509)) using x-ray crystallography. In addition, we incorporated the K14A and Y20A substitutions into full-length PDZK1 liver-specific transgenes and expressed them in WT and PDZK1 knock-out mice. In WT mice, the transgenes did not alter endogenous hepatic SR-BI protein expression (intracellular distribution or amount) or lipoprotein metabolism (total plasma cholesterol, lipoprotein size distribution). In PDZK1 knock-out mice, as expected, the K14A mutant behaved like wild-type PDZK1 and completely corrected their hepatic SR-BI and plasma lipoprotein abnormalities. Unexpectedly, the 10-20-fold overexpressed Y20A mutant also substantially, but not completely, corrected these abnormalities. The results suggest that there may be an additional site(s) within PDZK1 that bind(s) SR-BI and mediate(s) productive SR-BI-PDZK1 interaction previously attributed exclusively to the canonical binding of the C-terminal SR-BI to PDZ1.

  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. 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.

  6. Improving Protein Fold Recognition by Deep Learning Networks.

    Science.gov (United States)

    Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

    2015-12-04

    For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

  7. The construction of an amino acid network for understanding protein structure and function.

    Science.gov (United States)

    Yan, Wenying; Zhou, Jianhong; Sun, Maomin; Chen, Jiajia; Hu, Guang; Shen, Bairong

    2014-06-01

    Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

  8. p130Cas scaffolds the signalosome to direct adaptor-effector cross talk during Kaposi's sarcoma-associated herpesvirus trafficking in human microvascular dermal endothelial cells.

    Science.gov (United States)

    Bandyopadhyay, Chirosree; Veettil, Mohanan Valiya; Dutta, Sujoy; Chandran, Bala

    2014-12-01

    enzymatic activity, are well known to allow a great diversity of specific and coordinated protein-protein interactions imparting signal amplification to different networks for physiological and pathological signaling. They are involved in integrating signals from growth factors, extracellular matrix molecules, bacterial pathogens, and apoptotic cells. The present study identifies human microvascular dermal endothelial (HMVEC-d) cellular scaffold protein p130Cas (Crk-associated substrate) as a platform to promote Kaposi's sarcoma-associated herpesvirus (KSHV) trafficking. Early during KSHV de novo infection, p130Cas associates with lipid rafts and scaffolds EphrinA2 (EphA2)-associated critical adaptor members to downstream effector molecules, promoting successful nuclear delivery of the KSHV genome. Hence, simultaneous targeting of the receptor EphA2 and scaffolding action of p130Cas can potentially uncouple the signal cross talk of the KSHV entry-associated upstream signal complex from the immediate downstream trafficking-associated signalosome, consequently routing KSHV toward lysosomal degradation and eventually blocking KSHV infection and associated malignancies. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

  9. Peptide microarrays to probe for competition for binding sites in a protein interaction network

    NARCIS (Netherlands)

    Sinzinger, M.D.S.; Ruttekolk, I.R.R.; Gloerich, J.; Wessels, H.; Chung, Y.D.; Adjobo-Hermans, M.J.W.; Brock, R.E.

    2013-01-01

    Cellular protein interaction networks are a result of the binding preferences of a particular protein and the entirety of interactors that mutually compete for binding sites. Therefore, the reconstruction of interaction networks by the accumulation of interaction networks for individual proteins

  10. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  11. Unveiling network-based functional features through integration of gene expression into protein networks.

    Science.gov (United States)

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  13. 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.

  14. DMPD: The SAP family of adaptors in immune regulation. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 15541655 The SAP family of adaptors in immune regulation. Latour S, Veillette A. Se...min Immunol. 2004 Dec;16(6):409-19. (.png) (.svg) (.html) (.csml) Show The SAP family of adaptors in immune ...regulation. PubmedID 15541655 Title The SAP family of adaptors in immune regulation. Authors Latour S, Veill

  15. An automated approach to network features of protein structure ensembles

    Science.gov (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

  16. Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

    Science.gov (United States)

    Kanwal, Attiya; Fazal, Sahar

    2018-01-05

    Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

  17. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  18. Unravelling Protein-Protein Interaction Networks Linked to Aliphatic and Indole Glucosinolate Biosynthetic Pathways in Arabidopsis

    Directory of Open Access Journals (Sweden)

    Sebastian J. Nintemann

    2017-11-01

    Full Text Available Within the cell, biosynthetic pathways are embedded in protein-protein interaction networks. In Arabidopsis, the biosynthetic pathways of aliphatic and indole glucosinolate defense compounds are well-characterized. However, little is known about the spatial orchestration of these enzymes and their interplay with the cellular environment. To address these aspects, we applied two complementary, untargeted approaches—split-ubiquitin yeast 2-hybrid and co-immunoprecipitation screens—to identify proteins interacting with CYP83A1 and CYP83B1, two homologous enzymes specific for aliphatic and indole glucosinolate biosynthesis, respectively. Our analyses reveal distinct functional networks with substantial interconnection among the identified interactors for both pathway-specific markers, and add to our knowledge about how biochemical pathways are connected to cellular processes. Specifically, a group of protein interactors involved in cell death and the hypersensitive response provides a potential link between the glucosinolate defense compounds and defense against biotrophic pathogens, mediated by protein-protein interactions.

  19. 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.

  20. Duplicate retention in signalling proteins and constraints from network dynamics.

    Science.gov (United States)

    Soyer, O S; Creevey, C J

    2010-11-01

    Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

  1. 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

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

    Directory of Open Access Journals (Sweden)

    David I Spivak

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

  3. 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.

  4. 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.

  5. Deep recurrent conditional random field network for protein secondary prediction

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae

    2017-01-01

    Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...

  6. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Science.gov (United States)

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  7. 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.

  8. 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

  9. 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

  10. Graph theoretic analysis of protein interaction networks of eukaryotes

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2005-11-01

    Owing to the recent progress in high-throughput experimental techniques, the datasets of large-scale protein interactions of prototypical multicellular species, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, have been assayed. The datasets are obtained mainly by using the yeast hybrid method, which contains false-positive and false-negative simultaneously. Accordingly, while it is desirable to test such datasets through further wet experiments, here we invoke recent developed network theory to test such high-throughput datasets in a simple way. Based on the fact that the key biological processes indispensable to maintaining life are conserved across eukaryotic species, and the comparison of structural properties of the protein interaction networks (PINs) of the two species with those of the yeast PIN, we find that while the worm and yeast PIN datasets exhibit similar structural properties, the current fly dataset, though most comprehensively screened ever, does not reflect generic structural properties correctly as it is. The modularity is suppressed and the connectivity correlation is lacking. Addition of interologs to the current fly dataset increases the modularity and enhances the occurrence of triangular motifs as well. The connectivity correlation function of the fly, however, remains distinct under such interolog additions, for which we present a possible scenario through an in silico modeling.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-07-31

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

  12. Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

    Directory of Open Access Journals (Sweden)

    Guo Hao

    2011-05-01

    Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

  13. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

    Directory of Open Access Journals (Sweden)

    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  14. Clustering and visualizing similarity networks of membrane proteins.

    Science.gov (United States)

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

    2015-08-01

    We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

  15. The Hypoxic Regulator of Sterol Synthesis Nro1 Is a Nuclear Import Adaptor

    Energy Technology Data Exchange (ETDEWEB)

    T Yeh; C Lee; L Amzel; P Espenshade; M Bianchet

    2011-12-31

    Fission yeast protein Sre1, the homolog of the mammalian sterol regulatory element-binding protein (SREBP), is a hypoxic transcription factor required for sterol homeostasis and low-oxygen growth. Nro1 regulates the stability of the N-terminal transcription factor domain of Sre1 (Sre1N) by inhibiting the action of the prolyl 4-hydroxylase-like Ofd1 in an oxygen-dependent manner. The crystal structure of Nro1 determined at 2.2 {angstrom} resolution shows an all-{alpha}-helical fold that can be divided into two domains: a small N-terminal domain, and a larger C-terminal HEAT-repeat domain. Follow-up studies showed that Nro1 defines a new class of nuclear import adaptor that functions both in Ofd1 nuclear localization and in the oxygen-dependent inhibition of Ofd1 to control the hypoxic response.

  16. Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2016-10-06

    Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

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

    Directory of Open Access Journals (Sweden)

    Yashna Paul

    2016-01-01

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

  18. Weighted Protein Interaction Network Analysis of Frontotemporal Dementia.

    Science.gov (United States)

    Ferrari, Raffaele; Lovering, Ruth C; Hardy, John; Lewis, Patrick A; Manzoni, Claudia

    2017-02-03

    The genetic analysis of complex disorders has undoubtedly led to the identification of a wealth of associations between genes and specific traits. However, moving from genetics to biochemistry one gene at a time has, to date, rather proved inefficient and under-powered to comprehensively explain the molecular basis of phenotypes. Here we present a novel approach, weighted protein-protein interaction network analysis (W-PPI-NA), to highlight key functional players within relevant biological processes associated with a given trait. This is exemplified in the current study by applying W-PPI-NA to frontotemporal dementia (FTD): We first built the state of the art FTD protein network (FTD-PN) and then analyzed both its topological and functional features. The FTD-PN resulted from the sum of the individual interactomes built around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty nine of 4198 nodes, called inter-interactome hubs (IIHs), represented those interactors able to bridge over 60% of the individual interactomes. Functional annotation analysis not only reiterated and reinforced previous findings from single genes and gene-coexpression analyses but also indicated a number of novel potential disease related mechanisms, including DNA damage response, gene expression regulation, and cell waste disposal and potential biomarkers or therapeutic targets including EP300. These processes and targets likely represent the functional core impacted in FTD, reflecting the underlying genetic architecture contributing to disease. The approach presented in this study can be applied to other complex traits for which risk-causative genes are known as it provides a promising tool for setting the foundations for collating genomics and wet laboratory data in a bidirectional manner. This is and will be critical to accelerate molecular target prioritization and drug discovery.

  19. Prediction of the Ebola Virus Infection Related Human Genes Using Protein-Protein Interaction Network.

    Science.gov (United States)

    Cao, HuanHuan; Zhang, YuHang; Zhao, Jia; Zhu, Liucun; Wang, Yi; Li, JiaRui; Feng, Yuan-Ming; Zhang, Ning

    2017-01-01

    Ebola hemorrhagic fever (EHF) is caused by Ebola virus (EBOV). It is reported that human could be infected by EBOV with a high fatality rate. However, association factors between EBOV and host still tend to be ambiguous. According to the "guilt by association" (GBA) principle, proteins interacting with each other are very likely to function similarly or the same. Based on this assumption, we tried to obtain EBOV infection-related human genes in a protein-protein interaction network using Dijkstra algorithm. We hope it could contribute to the discovery of novel effective treatments. Finally, 15 genes were selected as potential EBOV infection-related human genes. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  20. Absence of the inflammasome adaptor ASC reduces hypoxia-induced pulmonary hypertension in mice.

    Science.gov (United States)

    Cero, Fadila Telarevic; Hillestad, Vigdis; Sjaastad, Ivar; Yndestad, Arne; Aukrust, Pål; Ranheim, Trine; Lunde, Ida Gjervold; Olsen, Maria Belland; Lien, Egil; Zhang, Lili; Haugstad, Solveig Bjærum; Løberg, Else Marit; Christensen, Geir; Larsen, Karl-Otto; Skjønsberg, Ole Henning

    2015-08-15

    Pulmonary hypertension is a serious condition that can lead to premature death. The mechanisms involved are incompletely understood although a role for the immune system has been suggested. Inflammasomes are part of the innate immune system and consist of the effector caspase-1 and a receptor, where nucleotide-binding oligomerization domain-like receptor pyrin domain-containing 3 (NLRP3) is the best characterized and interacts with the adaptor protein apoptosis-associated speck-like protein containing a caspase-recruitment domain (ASC). To investigate whether ASC and NLRP3 inflammasome components are involved in hypoxia-induced pulmonary hypertension, we utilized mice deficient in ASC and NLRP3. Active caspase-1, IL-18, and IL-1β, which are regulated by inflammasomes, were measured in lung homogenates in wild-type (WT), ASC(-/-), and NLRP3(-/-) mice, and phenotypical changes related to pulmonary hypertension and right ventricular remodeling were characterized after hypoxic exposure. Right ventricular systolic pressure (RVSP) of ASC(-/-) mice was significantly lower than in WT exposed to hypoxia (40.8 ± 1.5 mmHg vs. 55.8 ± 2.4 mmHg, P right ventricular remodeling. RVSP of NLRP3(-/-) mice exposed to hypoxia was not significantly altered compared with WT hypoxia. Whereas hypoxia increased protein levels of caspase-1, IL-18, and IL-1β in WT and NLRP3(-/-) mice, this response was absent in ASC(-/-) mice. Moreover, ASC(-/-) mice displayed reduced muscularization and collagen deposition around arteries. In conclusion, hypoxia-induced elevated right ventricular pressure and remodeling were attenuated in mice lacking the inflammasome adaptor protein ASC, suggesting that inflammasomes play an important role in the pathogenesis of pulmonary hypertension. Copyright © 2015 the American Physiological Society.

  1. APS, an adaptor molecule containing PH and SH2 domains, has a negative regulatory role in B cell proliferation

    International Nuclear Information System (INIS)

    Iseki, Masanori; Kubo-Akashi, Chiyomi; Kwon, Sang-Mo; Yamaguchi, Akiko; Takatsu, Kiyoshi; Takaki, Satoshi

    2005-01-01

    Adaptor molecule containing PH and SH2 domains (APS) is an intracellular adaptor protein that forms part of an adaptor family along with Lnk and SH2-B. APS transcripts are expressed in various tissues including brain, kidney, and muscle, as well as in splenic B cells but not in T cells. We investigated the functions of APS in B cell development and activation by generating APS-transgenic (APS-Tg) mice that overexpressed APS in lymphocytes. The number of B-1 cells in the peritoneal cavity was reduced in APS-Tg mice, as were B-2 cells in the spleen. B cell development in the bone marrow was partially impaired at the transition stage from proliferating large pre-B to small pre-B cells. B cell proliferation induced by B cell receptor (BCR) crosslinking but not by other B cell mitogens was also impaired in APS-Tg mice. APS co-localized with BCR complexes and filamentous actin in activated APS-Tg B cells. Thus, APS appears to play novel negative regulatory roles in BCR signaling, actin reorganization pathways, and control of compartment sizes of B-lineage cells

  2. Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks

    Science.gov (United States)

    2012-09-21

    Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks Paulo Shakarian1*, J. Kenneth Wickiser2 1 Paulo Shakarian...significantly attacked. Citation: Shakarian P, Wickiser JK (2012) Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks...to 00-00-2012 4. TITLE AND SUBTITLE Similar Pathogen Targets in Arabidopsis thaliana and Homo sapiens Protein Networks 5a. CONTRACT NUMBER 5b

  3. NSP-CAS Protein Complexes: Emerging Signaling Modules in Cancer.

    Science.gov (United States)

    Wallez, Yann; Mace, Peter D; Pasquale, Elena B; Riedl, Stefan J

    2012-05-01

    The CAS (CRK-associated substrate) family of adaptor proteins comprises 4 members, which share a conserved modular domain structure that enables multiple protein-protein interactions, leading to the assembly of intracellular signaling platforms. Besides their physiological role in signal transduction downstream of a variety of cell surface receptors, CAS proteins are also critical for oncogenic transformation and cancer cell malignancy through associations with a variety of regulatory proteins and downstream effectors. Among the regulatory partners, the 3 recently identified adaptor proteins constituting the NSP (novel SH2-containing protein) family avidly bind to the conserved carboxy-terminal focal adhesion-targeting (FAT) domain of CAS proteins. NSP proteins use an anomalous nucleotide exchange factor domain that lacks catalytic activity to form NSP-CAS signaling modules. Additionally, the NSP SH2 domain can link NSP-CAS signaling assemblies to tyrosine-phosphorylated cell surface receptors. NSP proteins can potentiate CAS function by affecting key CAS attributes such as expression levels, phosphorylation state, and subcellular localization, leading to effects on cell adhesion, migration, and invasion as well as cell growth. The consequences of these activities are well exemplified by the role that members of both families play in promoting breast cancer cell invasiveness and resistance to antiestrogens. In this review, we discuss the intriguing interplay between the NSP and CAS families, with a particular focus on cancer signaling networks.

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

    KAUST Repository

    Cannistraci, Carlo; Alanis Lobato, Gregorio; Ravasi, Timothy

    2013-01-01

    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

  5. The adaptor CRADD/RAIDD controls activation of endothelial cells by proinflammatory stimuli.

    Science.gov (United States)

    Qiao, Huan; Liu, Yan; Veach, Ruth A; Wylezinski, Lukasz; Hawiger, Jacek

    2014-08-08

    A hallmark of inflammation, increased vascular permeability, is induced in endothelial cells by multiple agonists through stimulus-coupled assembly of the CARMA3 signalosome, which contains the adaptor protein BCL10. Previously, we reported that BCL10 in immune cells is targeted by the "death" adaptor CRADD/RAIDD (CRADD), which negatively regulates nuclear factor κB (NFκB)-dependent cytokine and chemokine expression in T cells (Lin, Q., Liu, Y., Moore, D. J., Elizer, S. K., Veach, R. A., Hawiger, J., and Ruley, H. E. (2012) J. Immunol. 188, 2493-2497). This novel anti-inflammatory CRADD-BCL10 axis prompted us to analyze CRADD expression and its potential anti-inflammatory action in non-immune cells. We focused our study on microvascular endothelial cells because they play a key role in inflammation. We found that CRADD-deficient murine endothelial cells display heightened BCL10-mediated expression of the pleotropic proinflammatory cytokine IL-6 and chemokine monocyte chemoattractant protein-1 (MCP-1/CCL2) in response to LPS and thrombin. Moreover, these agonists also induce significantly increased permeability in cradd(-/-), as compared with cradd(+/+), primary murine endothelial cells. CRADD-deficient cells displayed more F-actin polymerization with concomitant disruption of adherens junctions. In turn, increasing intracellular CRADD by delivery of a novel recombinant cell-penetrating CRADD protein (CP-CRADD) restored endothelial barrier function and suppressed the induction of IL-6 and MCP-1 evoked by LPS and thrombin. Likewise, CP-CRADD enhanced barrier function in CRADD-sufficient endothelial cells. These results indicate that depletion of endogenous CRADD compromises endothelial barrier function in response to inflammatory signals. Thus, we define a novel function for CRADD in endothelial cells as an inducible suppressor of BCL10, a key mediator of responses to proinflammatory agonists. © 2014 by The American Society for Biochemistry and Molecular Biology

  6. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

    Science.gov (United States)

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

    2018-06-14

    Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

    Science.gov (United States)

    Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

    2011-11-01

    A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

  8. Bottom nozzle for nuclear reactor fuel assembly having an adaptor plate and a coupled filtration plate

    International Nuclear Information System (INIS)

    Verdier, M.; Mortgat, R.

    1992-01-01

    The bottom nozzle includes an adaptor plate with openings to allow the passage of water and a filtration plate with small holes. The openings in the adaptor plate are symmetrical with regard to medians and diagonals. Within each zone, some of the openings are rectangular and some may be circular. The small holes in the filtration plate coincide with the rectangular openings in the adaptor plate

  9. Building alternate protein structures using the elastic network model.

    Science.gov (United States)

    Yang, Qingyi; Sharp, Kim A

    2009-02-15

    We describe a method for efficiently generating ensembles of alternate, all-atom protein structures that (a) differ significantly from the starting structure, (b) have good stereochemistry (bonded geometry), and (c) have good steric properties (absence of atomic overlap). The method uses reconstruction from a series of backbone framework structures that are obtained from a modified elastic network model (ENM) by perturbation along low-frequency normal modes. To ensure good quality backbone frameworks, the single force parameter ENM is modified by introducing two more force parameters to characterize the interaction between the consecutive carbon alphas and those within the same secondary structure domain. The relative stiffness of the three parameters is parameterized to reproduce B-factors, while maintaining good bonded geometry. After parameterization, violations of experimental Calpha-Calpha distances and Calpha-Calpha-Calpha pseudo angles along the backbone are reduced to less than 1%. Simultaneously, the average B-factor correlation coefficient improves to R = 0.77. Two applications illustrate the potential of the approach. (1) 102,051 protein backbones spanning a conformational space of 15 A root mean square deviation were generated from 148 nonredundant proteins in the PDB database, and all-atom models with minimal bonded and nonbonded violations were produced from this ensemble of backbone structures using the SCWRL side chain building program. (2) Improved backbone templates for homology modeling. Fifteen query sequences were each modeled on two targets. For each of the 30 target frameworks, dozens of improved templates could be produced In all cases, improved full atom homology models resulted, of which 50% could be identified blind using the D-Fire statistical potential. (c) 2008 Wiley-Liss, Inc.

  10. Arabidopsis protein phosphatase DBP1 nucleates a protein network with a role in regulating plant defense.

    Directory of Open Access Journals (Sweden)

    José Luis Carrasco

    Full Text Available Arabidopsis thaliana DBP1 belongs to the plant-specific family of DNA-binding protein phosphatases. Although recently identified as a novel host factor mediating susceptibility to potyvirus, little is known about DBP1 targets and partners and the molecular mechanisms underlying its function. Analyzing changes in the phosphoproteome of a loss-of-function dbp1 mutant enabled the identification of 14-3-3λ isoform (GRF6, a previously reported DBP1 interactor, and MAP kinase (MAPK MPK11 as components of a small protein network nucleated by DBP1, in which GRF6 stability is modulated by MPK11 through phosphorylation, while DBP1 in turn negatively regulates MPK11 activity. Interestingly, grf6 and mpk11 loss-of-function mutants showed altered response to infection by the potyvirus Plum pox virus (PPV, and the described molecular mechanism controlling GRF6 stability was recapitulated upon PPV infection. These results not only contribute to a better knowledge of the biology of DBP factors, but also of MAPK signalling in plants, with the identification of GRF6 as a likely MPK11 substrate and of DBP1 as a protein phosphatase regulating MPK11 activity, and unveils the implication of this protein module in the response to PPV infection in Arabidopsis.

  11. Safeguards of Neurotransmission: Endocytic Adaptors as Regulators of Synaptic Vesicle Composition and Function

    Directory of Open Access Journals (Sweden)

    Natalie Kaempf

    2017-10-01

    Full Text Available Communication between neurons relies on neurotransmitters which are released from synaptic vesicles (SVs upon Ca2+ stimuli. To efficiently load neurotransmitters, sense the rise in intracellular Ca2+ and fuse with the presynaptic membrane, SVs need to be equipped with a stringently controlled set of transmembrane proteins. In fact, changes in SV protein composition quickly compromise neurotransmission and most prominently give rise to epileptic seizures. During exocytosis SVs fully collapse into the presynaptic membrane and consequently have to be replenished to sustain neurotransmission. Therefore, surface-stranded SV proteins have to be efficiently retrieved post-fusion to be used for the generation of a new set of fully functional SVs, a process in which dedicated endocytic sorting adaptors play a crucial role. The question of how the precise reformation of SVs is achieved is intimately linked to how SV membranes are retrieved. For a long time both processes were believed to be two sides of the same coin since Clathrin-mediated endocytosis (CME, the proposed predominant SV recycling mode, will jointly retrieve SV membranes and proteins. However, with the recent proposal of Clathrin-independent SV recycling pathways SV membrane retrieval and SV reformation turn into separable events. This review highlights the progress made in unraveling the molecular mechanisms mediating the high-fidelity retrieval of SV proteins and discusses how the gathered knowledge about SV protein recycling fits in with the new notions of SV membrane endocytosis.

  12. Discovering functional interdependence relationship in PPI networks for protein complex identification.

    Science.gov (United States)

    Lam, Winnie W M; Chan, Keith C C

    2012-04-01

    Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

  13. Classification of protein-protein interaction full-text documents using text and citation network features.

    Science.gov (United States)

    Kolchinsky, Artemy; Abi-Haidar, Alaa; Kaur, Jasleen; Hamed, Ahmed Abdeen; Rocha, Luis M

    2010-01-01

    We participated (as Team 9) in the Article Classification Task of the Biocreative II.5 Challenge: binary classification of full-text documents relevant for protein-protein interaction. We used two distinct classifiers for the online and offline challenges: 1) the lightweight Variable Trigonometric Threshold (VTT) linear classifier we successfully introduced in BioCreative 2 for binary classification of abstracts and 2) a novel Naive Bayes classifier using features from the citation network of the relevant literature. We supplemented the supplied training data with full-text documents from the MIPS database. The lightweight VTT classifier was very competitive in this new full-text scenario: it was a top-performing submission in this task, taking into account the rank product of the Area Under the interpolated precision and recall Curve, Accuracy, Balanced F-Score, and Matthew's Correlation Coefficient performance measures. The novel citation network classifier for the biomedical text mining domain, while not a top performing classifier in the challenge, performed above the central tendency of all submissions, and therefore indicates a promising new avenue to investigate further in bibliome informatics.

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

    International Nuclear Information System (INIS)

    Zhu Xiaodong; Guo Ya; Qu Song; Li Ling; Huang Shiting; Li Danrong; Zhang Wei

    2012-01-01

    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)

  15. PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

    Science.gov (United States)

    Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

    2017-02-03

    The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Protein complex detection in PPI networks based on data integration and supervised learning method.

    Science.gov (United States)

    Yu, Feng; Yang, Zhi; Hu, Xiao; Sun, Yuan; Lin, Hong; Wang, Jian

    2015-01-01

    Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

  17. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  18. 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.

  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

    proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and 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...

  20. MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

    Science.gov (United States)

    Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

    2018-03-10

    Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

  1. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

    Science.gov (United States)

    Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

    2017-02-01

    Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

  2. Stoichiometric balance of protein copy numbers is measurable and functionally significant in a protein-protein interaction network for yeast endocytosis.

    Science.gov (United States)

    Holland, David O; Johnson, Margaret E

    2018-03-01

    Stoichiometric balance, or dosage balance, implies that proteins that are subunits of obligate complexes (e.g. the ribosome) should have copy numbers expressed to match their stoichiometry in that complex. Establishing balance (or imbalance) is an important tool for inferring subunit function and assembly bottlenecks. We show here that these correlations in protein copy numbers can extend beyond complex subunits to larger protein-protein interactions networks (PPIN) involving a range of reversible binding interactions. We develop a simple method for quantifying balance in any interface-resolved PPINs based on network structure and experimentally observed protein copy numbers. By analyzing such a network for the clathrin-mediated endocytosis (CME) system in yeast, we found that the real protein copy numbers were significantly more balanced in relation to their binding partners compared to randomly sampled sets of yeast copy numbers. The observed balance is not perfect, highlighting both under and overexpressed proteins. We evaluate the potential cost and benefits of imbalance using two criteria. First, a potential cost to imbalance is that 'leftover' proteins without remaining functional partners are free to misinteract. We systematically quantify how this misinteraction cost is most dangerous for strong-binding protein interactions and for network topologies observed in biological PPINs. Second, a more direct consequence of imbalance is that the formation of specific functional complexes depends on relative copy numbers. We therefore construct simple kinetic models of two sub-networks in the CME network to assess multi-protein assembly of the ARP2/3 complex and a minimal, nine-protein clathrin-coated vesicle forming module. We find that the observed, imperfectly balanced copy numbers are less effective than balanced copy numbers in producing fast and complete multi-protein assemblies. However, we speculate that strategic imbalance in the vesicle forming module

  3. Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

    Science.gov (United States)

    Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

    2011-01-01

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

  4. 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.

  5. A Big-Five Personality Profile of the Adaptor and Innovator.

    Science.gov (United States)

    Kwang, Ng Aik; Rodrigues, Daphne

    2002-01-01

    A study explored the relationship between two creative types (adaptor and innovator) and the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), in 164 teachers in Singapore. Adaptors were significantly more conscientious than innovators, while innovators were significantly more…

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

    Directory of Open Access Journals (Sweden)

    Paulo Shakarian

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

  7. An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

    Science.gov (United States)

    Mallik, Mrinmay Kumar

    2018-02-07

    Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that

  8. 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.

  9. 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

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

    Science.gov (United States)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

  12. Phospho-dependent binding of the clathrin AP2 adaptor complex to GABAA receptors regulates the efficacy of inhibitory synaptic transmission

    OpenAIRE

    Kittler, Josef T.; Chen, Guojun; Honing, Stephan; Bogdanov, Yury; McAinsh, Kristina; Arancibia-Carcamo, I. Lorena; Jovanovic, Jasmina N.; Pangalos, Menelas N.; Haucke, Volker; Yan, Zhen; Moss, Stephen J.

    2005-01-01

    The efficacy of synaptic inhibition depends on the number of γ-aminobutyric acid type A receptors (GABAARs) expressed on the cell surface of neurons. The clathrin adaptor protein 2 (AP2) complex is a critical regulator of GABAAR endocytosis and, hence, surface receptor number. Here, we identify a previously uncharacterized atypical AP2 binding motif conserved within the intracellular domains of all GABAAR β subunit isoforms. This AP2 binding motif (KTHLRRRSSQLK in the β3 subunit) incorporates...

  13. 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.

  14. Structural complementarity of Toll/interleukin-1 receptor domains in Toll-like receptors and the adaptors Mal and MyD88.

    Science.gov (United States)

    Dunne, Aisling; Ejdeback, Mikael; Ludidi, Phumzile L; O'Neill, Luke A J; Gay, Nicholas J

    2003-10-17

    The Toll/interleukin 1 receptor (TIR) domain is a region found in the cytoplasmic tails of members of the Toll-like receptor/interleukin-1 receptor superfamily. The domain is essential for signaling and is also found in the adaptor proteins Mal (MyD88 adaptor-like) and MyD88, which function to couple activation of the receptor to downstream signaling components. Experimental structures of two Toll/interleukin 1 receptor domains reveal a alpha-beta-fold similar to that of the bacterial chemotaxis protein CheY, and other evidence suggests that the adaptors can make heterotypic interactions with both the receptors and themselves. Here we show that the purified TIR domains of Mal and MyD88 can form stable heterodimers and also that Mal homodimers and oligomers are dissociated in the presence of ATP. To identify structural features that may contribute to the formation of signaling complexes, we produced models of the TIR domains from human Toll-like receptor 4 (TLR4), Mal, and MyD88. We found that although the overall fold is conserved the electrostatic surface potentials are quite distinct. Docking studies of the models suggest that Mal and MyD88 bind to different regions in TLRs 2 and 4, a finding consistent with a cooperative role of the two adaptors in signaling. Mal and MyD88 are predicted to interact at a third non-overlapping site, suggesting that the receptor and adaptors may form heterotetrameric complexes. The theoretical model of the interactions is supported by experimental data from glutathione S-transferase pull-downs and co-immunoprecipitations. Neither theoretical nor experimental data suggest a direct role for the conserved proline in the BB-loop in the association of TLR4, Mal, and MyD88. Finally we show a sequence relationship between the Drosophila protein Tube and Mal that may indicate a functional equivalence of these two adaptors in the Drosophila and vertebrate Toll pathways.

  15. Genetic variation shapes protein networks mainly through non-transcriptional mechanisms.

    Directory of Open Access Journals (Sweden)

    Eric J Foss

    2011-09-01

    Full Text Available Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

  16. Nonsense mutations in ADTB3A cause complete deficiency of the beta3A subunit of adaptor complex-3 and severe Hermansky-Pudlak syndrome type 2.

    Science.gov (United States)

    Huizing, Marjan; Scher, Charles D; Strovel, Erin; Fitzpatrick, Diana L; Hartnell, Lisa M; Anikster, Yair; Gahl, William A

    2002-02-01

    Hermansky-Pudlak syndrome (HPS) is an autosomal recessive disease consisting of oculocutaneous albinism and a storage pool deficiency resulting from absent platelet dense bodies. The disorder is genetically heterogeneous. The majority of patients, including members of a large genetic isolate in northwest Puerto Rico, have mutations in HPS1. Another gene, ADTB3A, was shown to cause HPS-2 in two brothers having compound heterozygous mutations that allowed for residual production of the gene product, the beta3A subunit of adaptor complex-3 (AP-3). This heterotetrameric complex serves as a coat protein-mediating formation of intracellular vesicles, e.g. the melanosome and platelet dense body, from membranes of the trans-Golgi network. We determined the genomic organization of the human ADTB3A gene, with intron/exon boundaries, and describe a third patient with beta3A deficiency. This 5-y-old boy has two nonsense mutations, C1578T (R-->X) and G2028T (E-->X), which produce no ADTB3A mRNA and no beta3A protein. The associated mu3 subunit of AP-3 is also entirely absent. In fibroblasts, the cell biologic concomitant of this deficiency is robust and aberrant trafficking through the plasma membrane of LAMP-3, an integral lysosomal membrane protein normally carried directly to the lysosome. The clinical concomitant is a severe, G-CSF-responsive neutropenia in addition to oculocutaneous albinism and platelet storage pool deficiency. Our findings expand the molecular, cellular, and clinical spectrum of HPS-2 and call for an increased index of suspicion for this diagnosis among patients with features of albinism, bleeding, and neutropenia.

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    Ma'ayan Avi

    2007-10-01

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

  19. ATM-Dependent Phosphorylation of All Three Members of the MRN Complex: From Sensor to Adaptor.

    Science.gov (United States)

    Lavin, Martin F; Kozlov, Sergei; Gatei, Magtouf; Kijas, Amanda W

    2015-10-23

    The recognition, signalling and repair of DNA double strand breaks (DSB) involves the participation of a multitude of proteins and post-translational events that ensure maintenance of genome integrity. Amongst the proteins involved are several which when mutated give rise to genetic disorders characterised by chromosomal abnormalities, cancer predisposition, neurodegeneration and other pathologies. ATM (mutated in ataxia-telangiectasia (A-T) and members of the Mre11/Rad50/Nbs1 (MRN complex) play key roles in this process. The MRN complex rapidly recognises and locates to DNA DSB where it acts to recruit and assist in ATM activation. ATM, in the company of several other DNA damage response proteins, in turn phosphorylates all three members of the MRN complex to initiate downstream signalling. While ATM has hundreds of substrates, members of the MRN complex play a pivotal role in mediating the downstream signalling events that give rise to cell cycle control, DNA repair and ultimately cell survival or apoptosis. Here we focus on the interplay between ATM and the MRN complex in initiating signaling of breaks and more specifically on the adaptor role of the MRN complex in mediating ATM signalling to downstream substrates to control different cellular processes.

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

    Science.gov (United States)

    Wuchty, Stefan

    2006-05-23

    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. 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. 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 show a simple way to predict potential protein interactions

  1. 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

  2. A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

    Science.gov (United States)

    Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

    2017-09-01

    Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

    Science.gov (United States)

    Wang, Leilei; Cheng, Jinyong

    2018-03-01

    Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

  4. Protein-lipid interactions: from membrane domains to cellular networks

    National Research Council Canada - National Science Library

    Tamm, Lukas K

    2005-01-01

    ... membranes is the lipid bilayer. Embedded in the fluid lipid bilayer are proteins of various shapes and traits. This volume illuminates from physical, chemical and biological angles the numerous - mostly quite weak - interactions between lipids, proteins, and proteins and lipids that define the delicate, highly dynamic and yet so stable fabri...

  5. RAIN: RNA-protein Association and Interaction Networks

    DEFF Research Database (Denmark)

    Junge, Alexander; Refsgaard, Jan Christian; Garde, Christian

    2017-01-01

    is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data...

  6. msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

    Directory of Open Access Journals (Sweden)

    Yuan Zhang

    2014-01-01

    Full Text Available Dynamics of protein-protein interactions (PPIs reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

  7. Pitch adaptors of the ATLAS-SCT Endcap detector modules

    International Nuclear Information System (INIS)

    Ullan, M; Lozano, M; Campabadal, F; Fleta, C; Pellegrini, G; Garcia, C; Gonzalez, F

    2007-01-01

    Interconnection between detectors and electronics in modern High Energy Physics has become an issue of difficult solution due to the need to integrate both parts in the same module and the need for a low mass, simple connection. The Endcap section of the Semiconductor Tracker (SCT) of the ATLAS experiment at CERN has adopted the solution of using interface devices called pitch adaptors or fan-ins that, mounted on the modules, and using automatic wire bonding, connect the detector's multiple channels to the front-end electronics, adapting their different designs (pad pitch, dimensions, position). This paper describes the characteristics of these devices, the qualification tests that they have been submitted to, and the final results of their fabrication including quality assurance procedures

  8. NASCENT: an automatic protein interaction network generation tool for non-model organisms.

    Science.gov (United States)

    Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

    2009-04-24

    Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

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

    DEFF Research Database (Denmark)

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

    1998-01-01

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

  10. 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.

  11. Protein-Protein Interaction Article Classification Using a Convolutional Recurrent Neural Network with Pre-trained Word Embeddings.

    Science.gov (United States)

    Matos, Sérgio; Antunes, Rui

    2017-12-13

    Curation of protein interactions from scientific articles is an important task, since interaction networks are essential for the understanding of biological processes associated with disease or pharmacological action for example. However, the increase in the number of publications that potentially contain relevant information turns this into a very challenging and expensive task. In this work we used a convolutional recurrent neural network for identifying relevant articles for extracting information regarding protein interactions. Using the BioCreative III Article Classification Task dataset, we achieved an area under the precision-recall curve of 0.715 and a Matthew's correlation coefficient of 0.600, which represents an improvement over previous works.

  12. 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...

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

    International Nuclear Information System (INIS)

    Ba, Qian; Li, Junyang; Huang, Chao; Li, Jingquan; Chu, Ruiai; Wu, Yongning; Wang, Hui

    2015-01-01

    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

  14. 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.

  15. Analysis of hepatocellular carcinoma and metastatic hepatic carcinoma via functional modules in a protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Jun Pan

    2014-01-01

    Full Text Available Introduction: This study aims to identify protein clusters with potential functional relevance in the pathogenesis of hepatocellular carcinoma (HCC and metastatic hepatic carcinoma using network analysis. Materials and Methods: We used human protein interaction data to build a protein-protein interaction network with Cytoscape and then derived functional clusters using MCODE. Combining the gene expression profiles, we calculated the functional scores for the clusters and selected statistically significant clusters. Meanwhile, Gene Ontology was used to assess the functionality of these clusters. Finally, a support vector machine was trained on the gold standard data sets. Results: The differentially expressed genes of HCC were mainly involved in metabolic and signaling processes. We acquired 13 significant modules from the gene expression profiles. The area under the curve value based on the differentially expressed modules were 98.31%, which outweighed the classification with DEGs. Conclusions: Differentially expressed modules are valuable to screen biomarkers combined with functional modules.

  16. Efficient identification of critical residues based only on protein structure by network analysis.

    Directory of Open Access Journals (Sweden)

    Michael P Cusack

    2007-05-01

    Full Text Available Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.

  17. Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

    OpenAIRE

    Huang, Hao; He, Yuehan; Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming

    2016-01-01

    Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biologi...

  18. Protein Inference from the Integration of Tandem MS Data and Interactome Networks.

    Science.gov (United States)

    Zhong, Jiancheng; Wang, Jianxing; Ding, Xiaojun; Zhang, Zhen; Li, Min; Wu, Fang-Xiang; Pan, Yi

    2017-01-01

    Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

  19. PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

    OpenAIRE

    Pasquier, Claude; Promponas, Vasilis; Hamodrakas, Stavros

    2009-01-01

    International audience; A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the av...

  20. 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.

  1. Study on the isothermal forging process of MB26 magnesium alloy adaptor

    Directory of Open Access Journals (Sweden)

    Xu Wenchen

    2015-01-01

    Full Text Available The isothermal forging process is an effective method to manufacture complex-shaped components of hard-to-work materials, such as magnesium alloys. This study investigates the isothermal forging process of an MB26 magnesium alloy adaptor with three branches. The results show that two-step forging process is appropriate to form the adaptor forging, which not only improves the filling quality but also reduces the forging load compared with one-step forging process. Moreover, the flow line is distributed along the contour of the complex-shaped adaptor forging.

  2. 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

  3. NECAPs are negative regulators of the AP2 clathrin adaptor complex.

    Science.gov (United States)

    Beacham, Gwendolyn M; Partlow, Edward A; Lange, Jeffrey J; Hollopeter, Gunther

    2018-01-18

    Eukaryotic cells internalize transmembrane receptors via clathrin-mediated endocytosis, but it remains unclear how the machinery underpinning this process is regulated. We recently discovered that membrane-associated muniscin proteins such as FCHo and SGIP initiate endocytosis by converting the AP2 clathrin adaptor complex to an open, active conformation that is then phosphorylated (Hollopeter et al., 2014). Here we report that loss of ncap-1 , the sole C. elegans gene encoding an adaptiN Ear-binding Coat-Associated Protein (NECAP), bypasses the requirement for FCHO-1. Biochemical analyses reveal AP2 accumulates in an open, phosphorylated state in ncap-1 mutant worms, suggesting NECAPs promote the closed, inactive conformation of AP2. Consistent with this model, NECAPs preferentially bind open and phosphorylated forms of AP2 in vitro and localize with constitutively open AP2 mutants in vivo. NECAPs do not associate with phosphorylation-defective AP2 mutants, implying that phosphorylation precedes NECAP recruitment. We propose NECAPs function late in endocytosis to inactivate AP2. © 2018, Beacham et al.

  4. The adaptor molecule SAP plays essential roles during invariant NKT cell cytotoxicity and lytic synapse formation.

    Science.gov (United States)

    Das, Rupali; Bassiri, Hamid; Guan, Peng; Wiener, Susan; Banerjee, Pinaki P; Zhong, Ming-Chao; Veillette, André; Orange, Jordan S; Nichols, Kim E

    2013-04-25

    The adaptor molecule signaling lymphocytic activation molecule-associated protein (SAP) plays critical roles during invariant natural killer T (iNKT) cell ontogeny. As a result, SAP-deficient humans and mice lack iNKT cells. The strict developmental requirement for SAP has made it difficult to discern its possible involvement in mature iNKT cell functions. By using temporal Cre recombinase-mediated gene deletion to ablate SAP expression after completion of iNKT cell development, we demonstrate that SAP is essential for T-cell receptor (TCR)-induced iNKT cell cytotoxicity against T-cell and B-cell leukemia targets in vitro and iNKT-cell-mediated control of T-cell leukemia growth in vivo. These findings are not restricted to the murine system: silencing RNA-mediated suppression of SAP expression in human iNKT cells also significantly impairs TCR-induced cytolysis. Mechanistic studies reveal that iNKT cell killing requires the tyrosine kinase Fyn, a known SAP-binding protein. Furthermore, SAP expression is required within iNKT cells to facilitate their interaction with T-cell targets and induce reorientation of the microtubule-organizing center to the immunologic synapse (IS). Collectively, these studies highlight a novel and essential role for SAP during iNKT cell cytotoxicity and formation of a functional IS.

  5. The human-bacterial pathogen protein interaction networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis.

    Directory of Open Access Journals (Sweden)

    Matthew D Dyer

    2010-08-01

    Full Text Available Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.

  6. Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

    Directory of Open Access Journals (Sweden)

    Xianjun Shen

    Full Text Available How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment. It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

  7. 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.

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

    DEFF Research Database (Denmark)

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

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  9. 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...

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

    Directory of Open Access Journals (Sweden)

    Anna Delprato

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

  11. Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

    Science.gov (United States)

    Cui, Jian; Liu, Jinghua; Li, Yuhua; Shi, Tieliu

    2011-01-01

    Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome. PMID:21297957

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

    International Nuclear Information System (INIS)

    Hu Longhua; Papoian, Garegin A

    2011-01-01

    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.

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

    DEFF Research Database (Denmark)

    Di Roberto, Raphaël B; Chang, Belinda; Trusina, Ala

    2016-01-01

    All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae......, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While...... demonstrate that a new receptor-ligand pair can evolve through network-altering mutations independently of receptor-ligand binding, and suggest a potential role for such mutations in disease....

  14. Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

    International Nuclear Information System (INIS)

    Pei, Guangsheng; Chen, Lei; Wang, Jiangxin; Qiao, Jianjun; Zhang, Weiwen

    2014-01-01

    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 need 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 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 weighted gene co-expression 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.

  15. Protein Network Signatures Associated with Exogenous Biofuels Treatments in Cyanobacterium Synechocystis sp. PCC 6803

    Energy Technology Data Exchange (ETDEWEB)

    Pei, Guangsheng; Chen, Lei; Wang, Jiangxin; Qiao, Jianjun, E-mail: jianjunq@tju.edu.cn; Zhang, Weiwen, E-mail: jianjunq@tju.edu.cn [Laboratory of Synthetic Microbiology, School of Chemical Engineering and Technology, Tianjin University, Tianjin (China); Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin (China); SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering, Tianjin (China)

    2014-11-03

    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 need 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 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 weighted gene co-expression 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.

  16. Protein and signaling networks in vertebrate photoreceptor cells

    Directory of Open Access Journals (Sweden)

    Karl-Wilhelm eKoch

    2015-11-01

    Full Text Available Vertebrate photoreceptor cells are exquisite light detectors operating under very dim and bright illumination. The photoexcitation and adaptation machinery in photoreceptor cells consists of protein complexes that can form highly ordered supramolecular structures and control the homeostasis and mutual dependence of the secondary messengers cGMP and Ca2+. The visual pigment in rod photoreceptors, the G protein-coupled receptor rhodopsin is organized in tracks of dimers thereby providing a signaling platform for the dynamic scaffolding of the G protein transducin. Illuminated rhodopsin is turned off by phosphorylation catalyzed by rhodopsin kinase GRK1 under control of Ca2+-recoverin. The GRK1 protein complex partly assembles in lipid raft structures, where shutting off rhodopsin seems to be more effective. Re-synthesis of cGMP is another crucial step in the recovery of the photoresponse after illumination. It is catalyzed by membrane bound sensory guanylate cyclases and is regulated by specific neuronal Ca2+-sensor proteins called GCAPs. At least one guanylate cyclase (ROS-GC1 was shown to be part of a multiprotein complex having strong interactions with the cytoskeleton and being controlled in a multimodal Ca2+-dependent fashion. The final target of the cGMP signaling cascade is a cyclic nucleotide-gated channel that is a hetero-oligomeric protein located in the plasma membrane and interacting with accessory proteins in highly organized microdomains. We summarize results and interpretations of findings related to the inhomogeneous organization of signaling units in photoreceptor outer segments.

  17. Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.

    Directory of Open Access Journals (Sweden)

    Xiaoqing Peng

    Full Text Available Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN. However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs. To do this, a method based on Localization Specificity for Essential protein Detection (LSED, was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster. Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.

  18. Caenorhabditis elegans fibroblast growth factor receptor signaling can occur independently of the multi-substrate adaptor FRS2.

    Science.gov (United States)

    Lo, Te-Wen; Bennett, Daniel C; Goodman, S Jay; Stern, Michael J

    2010-06-01

    The components of receptor tyrosine kinase signaling complexes help to define the specificity of the effects of their activation. The Caenorhabditis elegans fibroblast growth factor receptor (FGFR), EGL-15, regulates a number of processes, including sex myoblast (SM) migration guidance and fluid homeostasis, both of which require a Grb2/Sos/Ras cassette of signaling components. Here we show that SEM-5/Grb2 can bind directly to EGL-15 to mediate SM chemoattraction. A yeast two-hybrid screen identified SEM-5 as able to interact with the carboxy-terminal domain (CTD) of EGL-15, a domain that is specifically required for SM chemoattraction. This interaction requires the SEM-5 SH2-binding motifs present in the CTD (Y(1009) and Y(1087)), and these sites are required for the CTD role of EGL-15 in SM chemoattraction. SEM-5, but not the SEM-5 binding sites located in the CTD, is required for the fluid homeostasis function of EGL-15, indicating that SEM-5 can link to EGL-15 through an alternative mechanism. The multi-substrate adaptor protein FRS2 serves to link vertebrate FGFRs to Grb2. In C. elegans, an FRS2-like gene, rog-1, functions upstream of a Ras/MAPK pathway for oocyte maturation but is not required for EGL-15 function. Thus, unlike the vertebrate FGFRs, which require the multi-substrate adaptor FRS2 to recruit Grb2, EGL-15 can recruit SEM-5/Grb2 directly.

  19. Listeriolysin O Regulates the Expression of Optineurin, an Autophagy Adaptor That Inhibits the Growth of Listeria monocytogenes.

    Science.gov (United States)

    Puri, Madhu; La Pietra, Luigi; Mraheil, Mobarak Abu; Lucas, Rudolf; Chakraborty, Trinad; Pillich, Helena

    2017-09-05

    Autophagy, a well-established defense mechanism, enables the elimination of intracellular pathogens including Listeria monocytogenes . Host cell recognition results in ubiquitination of L . monocytogenes and interaction with autophagy adaptors p62/SQSTM1 and NDP52, which target bacteria to autophagosomes by binding to microtubule-associated protein 1 light chain 3 (LC3). Although studies have indicated that L . monocytogenes induces autophagy, the significance of this process in the infectious cycle and the mechanisms involved remain poorly understood. Here, we examined the role of the autophagy adaptor optineurin (OPTN), the phosphorylation of which by the TANK binding kinase 1 (TBK1) enhances its affinity for LC3 and promotes autophagosomal degradation, during L . monocytogenes infection. In LC3- and OPTN-depleted host cells, intracellular replicating L . monocytogenes increased, an effect not seen with a mutant lacking the pore-forming toxin listeriolysin O (LLO). LLO induced the production of OPTN. In host cells expressing an inactive TBK1, bacterial replication was also inhibited. Our studies have uncovered an OPTN-dependent pathway in which L . monocytogenes uses LLO to restrict bacterial growth. Hence, manipulation of autophagy by L . monocytogenes , either through induction or evasion, represents a key event in its intracellular life style and could lead to either cytosolic growth or persistence in intracellular vacuolar structures.

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

    Directory of Open Access Journals (Sweden)

    Jalal Rezaeenour

    2016-10-01

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

  1. Dynamical analysis of yeast protein interaction network during the sake brewing process.

    Science.gov (United States)

    Mirzarezaee, Mitra; Sadeghi, Mehdi; Araabi, Babak N

    2011-12-01

    Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.

  2. The ability to store energy in pea protein gels is set by network dimensions smaller than 50 nm

    NARCIS (Netherlands)

    Munialo, C.D.; Linden, van der E.; Jongh, de H.H.J.

    2014-01-01

    The objective of this study was to identify which length scales set the ability to elastically store energy in pea protein network structures. Various network structures were obtained frompea proteins by varying the pH and salt conditions during gel formation. The coarseness of the network structure

  3. Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

    Science.gov (United States)

    Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

    2017-10-01

    The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

  4. 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.

  5. Exploring overlapping functional units with various structure in protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Xiao-Fei Zhang

    Full Text Available Revealing functional units in protein-protein interaction (PPI networks are important for understanding cellular functional organization. Current algorithms for identifying functional units mainly focus on cohesive protein complexes which have more internal interactions than external interactions. Most of these approaches do not handle overlaps among complexes since they usually allow a protein to belong to only one complex. Moreover, recent studies have shown that other non-cohesive structural functional units beyond complexes also exist in PPI networks. Thus previous algorithms that just focus on non-overlapping cohesive complexes are not able to present the biological reality fully. Here, we develop a new regularized sparse random graph model (RSRGM to explore overlapping and various structural functional units in PPI networks. RSRGM is principally dominated by two model parameters. One is used to define the functional units as groups of proteins that have similar patterns of connections to others, which allows RSRGM to detect non-cohesive structural functional units. The other one is used to represent the degree of proteins belonging to the units, which supports a protein belonging to more than one revealed unit. We also propose a regularizer to control the smoothness between the estimators of these two parameters. Experimental results on four S. cerevisiae PPI networks show that the performance of RSRGM on detecting cohesive complexes and overlapping complexes is superior to that of previous competing algorithms. Moreover, RSRGM has the ability to discover biological significant functional units besides complexes.

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

    Directory of Open Access Journals (Sweden)

    Gregorio eAlanis-Lobato

    2015-09-01

    Full Text Available 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 aetiology. 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.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Swapnil R Chhabra

    Full Text Available 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 Escherichia coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio vulgaris Hildenborough, a model obligate anaerobe and sulfate reducer and the subject of this study. Here we carried out affinity purification followed by mass spectrometry to reconstruct an interaction network among 12 chromosomally encoded bait and 90 prey proteins based on 134 bait-prey interactions identified to be of high confidence. Protein-protein interaction data are often plagued by the lack of adequate controls and replication analyses necessary to assess confidence in the results, including identification of potential false positives. We addressed these issues through the use of biological replication, exponentially modified protein abundance indices, results from an experimental negative control, and a statistical test to assign confidence to each putative interacting pair applicable to small interaction data studies. We discuss the biological significance of metabolic features of D. vulgaris revealed by these protein-protein interaction data and the observed protein modifications. 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. A Web server for predicting proteins involved in pluripotent network

    Indian Academy of Sciences (India)

    2016-11-04

    Nov 4, 2016 ... which are important in pluripotency from the existing knowledge about pluripotent ... proteins, we took 117 genes with gene ontology term developmental ... space to find a hyperplane which maximizes the margin between two ...

  10. 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 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.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.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.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  11. 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...

  12. Neuronal adaptor FE65 stimulates Rac1-mediated neurite outgrowth by recruiting and activating ELMO1.

    Science.gov (United States)

    Li, Wen; Tam, Ka Ming Vincent; Chan, Wai Wan Ray; Koon, Alex Chun; Ngo, Jacky Chi Ki; Chan, Ho Yin Edwin; Lau, Kwok-Fai

    2018-04-03

    Neurite outgrowth is a crucial process in developing neurons for neural network formation. Understanding the regulatory mechanisms of neurite outgrowth is essential for developing strategies to stimulate neurite regeneration after nerve injury and in neurodegenerative disorders. FE65 is a brain-enriched adaptor that stimulates Rac1-mediated neurite elongation. However, the precise mechanism by which FE65 promotes the process remains elusive. Here, we show that ELMO1, a subunit of ELMO1-DOCK180 bipartite Rac1 GEF, interacts with the FE65 N-terminal region. Overexpression of FE65 and/or ELMO1 enhances whereas knockdown of FE65 or ELMO1 inhibits neurite outgrowth and Rac1 activation. The effect of FE65 alone or together with ELMO1 is attenuated by an FE65 double mutation that disrupts FE65-ELMO1 interaction. Notably, FE65 is found to activate ELMO1 by diminishing ELMO1 intramolecular autoinhibitory interaction and to promote the targeting of ELMO1 to the plasma membrane where Rac1 is activated. We also show that FE65, ELMO1 and DOCK180 form a tripartite complex. Knockdown of DOCK180 reduces the stimulatory effect of FE65-ELMO1 on Rac1 activation and neurite outgrowth. Thus, we identify a novel mechanism that FE65 stimulates Rac1-mediated neurite outgrowth by recruiting and activating of ELMO1. Published under license by The American Society for Biochemistry and Molecular Biology, Inc.

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

    Science.gov (United States)

    Lin, Wen-Hsien; Liu, Wei-Chung; Hwang, Ming-Jing

    2009-03-11

    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 interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. Compared to a random selection of proteins, house-keeping gene-encoded proteins tended to have a greater number of directly interacting neighbors and occupy network positions in several shortest paths of interaction between protein pairs, whereas tissue-specific gene-encoded proteins did not. In addition, house-keeping gene-encoded proteins tended to connect with other house-keeping gene-encoded proteins in all tissue types, whereas tissue-specific gene-encoded proteins also tended to connect with other tissue-specific gene-encoded proteins, but only in approximately half of the tissue types examined. Our analysis showed that house-keeping gene-encoded proteins tend to occupy important network positions, while those encoded by tissue-specific genes do not. The biological implications of our findings were discussed and we proposed a hypothesis regarding how cells organize their protein tools in protein-protein interaction networks. Our results led us to speculate that house-keeping gene-encoded proteins might form a core in human protein-protein interaction networks, while clusters of tissue-specific gene

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

    Directory of Open Access Journals (Sweden)

    Mingzhu Zhao

    2013-01-01

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

  15. 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.

  16. Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

    Science.gov (United States)

    Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

    2008-10-01

    Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

  17. 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.

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Ma

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

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

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

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

  1. RRW: repeated random walks on genome-scale protein networks for local cluster discovery

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2009-09-01

    Full Text Available Abstract Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL, and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.

  2. 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...... 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...

  3. Regulation of lifespan, metabolism, and stress responses by the Drosophila SH2B protein, Lnk.

    Directory of Open Access Journals (Sweden)

    Cathy Slack

    2010-03-01

    Full Text Available Drosophila Lnk is the single ancestral orthologue of a highly conserved family of structurally-related intracellular adaptor proteins, the SH2B proteins. As adaptors, they lack catalytic activity but contain several protein-protein interaction domains, thus playing a critical role in signal transduction from receptor tyrosine kinases to form protein networks. Physiological studies of SH2B function in mammals have produced conflicting data. However, a recent study in Drosophila has shown that Lnk is an important regulator of the insulin/insulin-like growth factor (IGF-1 signaling (IIS pathway during growth, functioning in parallel to the insulin receptor substrate, Chico. As this pathway also has an evolutionary conserved role in the determination of organism lifespan, we investigated whether Lnk is required for normal lifespan in Drosophila. Phenotypic analysis of mutants for Lnk revealed that loss of Lnk function results in increased lifespan and improved survival under conditions of oxidative stress and starvation. Starvation resistance was found to be associated with increased metabolic stores of carbohydrates and lipids indicative of impaired metabolism. Biochemical and genetic data suggest that Lnk functions in both the IIS and Ras/Mitogen activated protein Kinase (MapK signaling pathways. Microarray studies support this model, showing transcriptional feedback onto genes in both pathways as well as indicating global changes in both lipid and carbohydrate metabolism. Finally, our data also suggest that Lnk itself may be a direct target of the IIS responsive transcription factor, dFoxo, and that dFoxo may repress Lnk expression. We therefore describe novel functions for a member of the SH2B protein family and provide the first evidence for potential mechanisms of SH2B regulation. Our findings suggest that IIS signaling in Drosophila may require the activity of a second intracellular adaptor, thereby yielding fundamental new insights into the

  4. Characterization of the CLASP2 Protein Interaction Network Identifies SOGA1 as a Microtubule-Associated Protein

    DEFF Research Database (Denmark)

    Sørensen, Rikke Kruse; Krantz, James; Barker, Natalie

    2017-01-01

    . The GTPase-activating proteins AGAP1 and AGAP3 were also enriched in the CLASP2 interactome, although subsequent AGAP3 and CLIP2 interactome analysis suggests a preference of AGAP3 for CLIP2. Follow-up MARK2 interactome analysis confirmed reciprocal co-IP of CLASP2 and also revealed MARK2 can co-IP SOGA1......, glycogen synthase, and glycogenin. Investigating the SOGA1 interactome confirmed SOGA1 can reciprocal co-IP both CLASP2 and MARK2 as well as glycogen synthase and glycogenin. SOGA1 was confirmed to colocalize with CLASP2 and also with tubulin, which identifies SOGA1 as a new microtubule-associated protein....... These results introduce the metabolic function of these proposed novel protein networks and their relationship with microtubules as new fields of cytoskeleton-associated protein biology....

  5. Control of Cellular Structural Networks Through Unstructured Protein Domains

    Science.gov (United States)

    2016-07-01

    0195-z Albert J. Keung, Meimei Dong, David V. Schaffer, Sanjay Kumar. Pan-neuronal maturation but not neuronal subtype differentiation of adult neural...thin film of silicon dioxide deposited on a reflective silicon wafer. The intensity of the fluorescence excitation light is axially modulated by...star rating in Faculty of 1000. C. ENGINEERING NEURONAL BEHAVIOR VIA CYTOSKELETAL NETWORKS We have sought to understand how adult neural stem cells

  6. Thick Filament Protein Network, Functions, and Disease Association.

    Science.gov (United States)

    Wang, Li; Geist, Janelle; Grogan, Alyssa; Hu, Li-Yen R; Kontrogianni-Konstantopoulos, Aikaterini

    2018-03-13

    Sarcomeres consist of highly ordered arrays of thick myosin and thin actin filaments along with accessory proteins. Thick filaments occupy the center of sarcomeres where they partially overlap with thin filaments. The sliding of thick filaments past thin filaments is a highly regulated process that occurs in an ATP-dependent manner driving muscle contraction. In addition to myosin that makes up the backbone of the thick filament, four other proteins which are intimately bound to the thick filament, myosin binding protein-C, titin, myomesin, and obscurin play important structural and regulatory roles. Consistent with this, mutations in the respective genes have been associated with idiopathic and congenital forms of skeletal and cardiac myopathies. In this review, we aim to summarize our current knowledge on the molecular structure, subcellular localization, interacting partners, function, modulation via posttranslational modifications, and disease involvement of these five major proteins that comprise the thick filament of striated muscle cells. © 2018 American Physiological Society. Compr Physiol 8:631-709, 2018. Copyright © 2018 American Physiological Society. All rights reserved.

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

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    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.

  8. When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks.

    Science.gov (United States)

    Iván, Gábor; Grolmusz, Vince

    2011-02-01

    Enormous and constantly increasing quantity of biological information is represented in metabolic and in protein interaction network databases. Most of these data are freely accessible through large public depositories. The robust analysis of these resources needs novel technologies, being developed today. Here we demonstrate a technique, originating from the PageRank computation for the World Wide Web, for analyzing large interaction networks. The method is fast, scalable and robust, and its capabilities are demonstrated on metabolic network data of the tuberculosis bacterium and the proteomics analysis of the blood of melanoma patients. The Perl script for computing the personalized PageRank in protein networks is available for non-profit research applications (together with sample input files) at the address: http://uratim.com/pp.zip.

  9. Protein-protein networks construction and their relevance measurement based on multi-epitope-ligand-kartographie and gene ontology data of T-cell surface proteins for polymyositis.

    Science.gov (United States)

    Li, Fang-Zhen; Gao, Feng

    2012-08-01

    Polymyositis is an inflammatory myopathy characterized by muscle invasion of T-cells penetrating the basal lamina and displacing the plasma membrane of normal muscle fibers. In order to understand the different adhesive mechanisms at the T-cell surface, Schubert randomly selected 19 proteins expressed at the T-cell surface and studied them using MELK technique [4], among which 15 proteins are picked up for further study by us. Two types of functional similarity networks are constructed for these proteins. The first type is MELK similarity network, which is constructed based on their MELK data by using the McNemar's test [24]. The second type is GO similarity network, which is constructed based on their GO annotation data by using the RSS method to measuring functional similarity. Then the subset surprisology theory is employed to measure the degree of similarity between two networks. Our computing results show that these two types of networks are high related. This conclusion added new values on MELK technique and expanded its applications greatly.

  10. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

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    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

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

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    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.

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

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    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.

  13. Usher syndrome protein network functions in the retina and their relation to other retinal ciliopathies.

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    Sorusch, Nasrin; Wunderlich, Kirsten; Bauss, Katharina; Nagel-Wolfrum, Kerstin; Wolfrum, Uwe

    2014-01-01

    The human Usher syndrome (USH) is the most frequent cause of combined hereditary deaf-blindness. USH is genetically and clinically heterogeneous: 15 chromosomal loci assigned to 3 clinical types, USH1-3. All USH1 and 2 proteins are organized into protein networks by the scaffold proteins harmonin (USH1C), whirlin (USH2D) and SANS (USH1G). This has contributed essentially to our current understanding of the USH protein function in the eye and the ear and explains why defects in proteins of different families cause very similar phenotypes. Ongoing in depth analyses of USH protein networks in the eye indicated cytoskeletal functions as well as roles in molecular transport processes and ciliary cargo delivery in photoreceptor cells. The analysis of USH protein networks revealed molecular links of USH to other ciliopathies, including non-syndromic inner ear defects and isolated retinal dystrophies but also to kidney diseases and syndromes like the Bardet-Biedl syndrome. These findings provide emerging evidence that USH is a ciliopathy molecularly related to other ciliopathies, which opens an avenue for common therapy strategies to treat these diseases.

  14. P³DB 3.0: From plant phosphorylation sites to protein networks.

    Science.gov (United States)

    Yao, Qiuming; Ge, Huangyi; Wu, Shangquan; Zhang, Ning; Chen, Wei; Xu, Chunhui; Gao, Jianjiong; Thelen, Jay J; Xu, Dong

    2014-01-01

    In the past few years, the Plant Protein Phosphorylation Database (P(3)DB, http://p3db.org) has become one of the most significant in vivo data resources for studying plant phosphoproteomics. We have substantially updated P(3)DB with respect to format, new datasets and analytic tools. In the P(3)DB 3.0, there are altogether 47 923 phosphosites in 16 477 phosphoproteins curated across nine plant organisms from 32 studies, which have met our multiple quality standards for acquisition of in vivo phosphorylation site data. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data--all of which provides context for the phosphorylation event. In addition, P(3)DB 3.0 incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, the new P(3)DB reflects a community-based design through which users can share datasets and automate data depository processes for publication purposes. Each of these new features supports the goal of making P(3)DB a comprehensive, systematic and interactive platform for phosphoproteomics research.

  15. 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...

  16. Effect of dataset selection on the topological interpretation of protein interaction networks

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    Robertson David L

    2005-09-01

    Full Text Available Abstract Background Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected

  17. 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.

  18. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

    Science.gov (United States)

    Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

    2015-04-01

    The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. DeepQA: improving the estimation of single protein model quality with deep belief networks.

    Science.gov (United States)

    Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

    2016-12-05

    Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

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

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    Yong Chern Han

    2012-12-01

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

  1. Classification of Beta-lactamases and penicillin binding proteins using ligand-centric network models.

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    Hakime Öztürk

    Full Text Available β-lactamase mediated antibiotic resistance is an important health issue and the discovery of new β-lactam type antibiotics or β-lactamase inhibitors is an area of intense research. Today, there are about a thousand β-lactamases due to the evolutionary pressure exerted by these ligands. While β-lactamases hydrolyse the β-lactam ring of antibiotics, rendering them ineffective, Penicillin-Binding Proteins (PBPs, which share high structural similarity with β-lactamases, also confer antibiotic resistance to their host organism by acquiring mutations that allow them to continue their participation in cell wall biosynthesis. In this paper, we propose a novel approach to include ligand sharing information for classifying and clustering β-lactamases and PBPs in an effort to elucidate the ligand induced evolution of these β-lactam binding proteins. We first present a detailed summary of the β-lactamase and PBP families in the Protein Data Bank, as well as the compounds they bind to. Then, we build two different types of networks in which the proteins are represented as nodes, and two proteins are connected by an edge with a weight that depends on the number of shared identical or similar ligands. These models are analyzed under three different edge weight settings, namely unweighted, weighted, and normalized weighted. A detailed comparison of these six networks showed that the use of ligand sharing information to cluster proteins resulted in modules comprising proteins with not only sequence similarity but also functional similarity. Consideration of ligand similarity highlighted some interactions that were not detected in the identical ligand network. Analysing the β-lactamases and PBPs using ligand-centric network models enabled the identification of novel relationships, suggesting that these models can be used to examine other protein families to obtain information on their ligand induced evolutionary paths.

  2. Salt-bridge networks within globular and disordered proteins: characterizing trends for designable interactions.

    Science.gov (United States)

    Basu, Sankar; Mukharjee, Debasish

    2017-07-01

    There has been considerable debate about the contribution of salt bridges to the stabilization of protein folds, in spite of their participation in crucial protein functions. Salt bridges appear to contribute to the activity-stability trade-off within proteins by bringing high-entropy charged amino acids into close contacts during the course of their functions. The current study analyzes the modes of association of salt bridges (in terms of networks) within globular proteins and at protein-protein interfaces. While the most common and trivial type of salt bridge is the isolated salt bridge, bifurcated salt bridge appears to be a distinct salt-bridge motif having a special topology and geometry. Bifurcated salt bridges are found ubiquitously in proteins and interprotein complexes. Interesting and attractive examples presenting different modes of interaction are highlighted. Bifurcated salt bridges appear to function as molecular clips that are used to stitch together large surface contours at interacting protein interfaces. The present work also emphasizes the key role of salt-bridge-mediated interactions in the partial folding of proteins containing long stretches of disordered regions. Salt-bridge-mediated interactions seem to be pivotal to the promotion of "disorder-to-order" transitions in small disordered protein fragments and their stabilization upon binding. The results obtained in this work should help to guide efforts to elucidate the modus operandi of these partially disordered proteins, and to conceptualize how these proteins manage to maintain the required amount of disorder even in their bound forms. This work could also potentially facilitate explorations of geometrically specific designable salt bridges through the characterization of composite salt-bridge networks. Graphical abstract ᅟ.

  3. MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

    Science.gov (United States)

    Fang, Chao; Shang, Yi; Xu, Dong

    2018-05-01

    Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

  4. Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

    Science.gov (United States)

    He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

    2012-06-25

    Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the

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

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    Jiang Xiaoyu

    2008-08-01

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

  6. Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network

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    Pooja Sharma

    2018-06-01

    Full Text Available Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors. Keywords: Protein complex, Connectivity, Semantic similarity, Contribution

  7. Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

    Science.gov (United States)

    Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

    2016-10-24

    Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks.

    Science.gov (United States)

    Li, Fei; Li, Peng; Xu, Wenjian; Peng, Yuxing; Bo, Xiaochen; Wang, Shengqi

    2010-01-15

    The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. Supplementary data are available at Bioinformatics online.

  10. The effect of oil type on network formation by protein aggregates into oleogels

    NARCIS (Netherlands)

    Vries, de Auke; Lopez Gomez, Yuly; Linden, van der Erik; Scholten, Elke

    2017-01-01

    The aim of this study was to assess the effect of oil type on the network formation of heat-set protein aggregates in liquid oil. The gelling properties of such aggregates to structure oil into so-called ‘oleogels’ are related to both the particle-particle and particle-solvent interactions. To

  11. An Interaction between KSHV ORF57 and UIF Provides mRNA-Adaptor Redundancy in Herpesvirus Intronless mRNA Export

    Science.gov (United States)

    Jackson, Brian R.; Boyne, James R.; Noerenberg, Marko; Taylor, Adam; Hautbergue, Guillaume M.; Walsh, Matthew J.; Wheat, Rachel; Blackbourn, David J.; Wilson, Stuart A.; Whitehouse, Adrian

    2011-01-01

    The hTREX complex mediates cellular bulk mRNA nuclear export by recruiting the nuclear export factor, TAP, via a direct interaction with the export adaptor, Aly. Intriguingly however, depletion of Aly only leads to a modest reduction in cellular mRNA nuclear export, suggesting the existence of additional mRNA nuclear export adaptor proteins. In order to efficiently export Kaposi's sarcoma-associated herpesvirus (KSHV) intronless mRNAs from the nucleus, the KSHV ORF57 protein recruits hTREX onto viral intronless mRNAs allowing access to the TAP-mediated export pathway. Similarly however, depletion of Aly only leads to a modest reduction in the nuclear export of KSHV intronless mRNAs. Herein, we identify a novel interaction between ORF57 and the cellular protein, UIF. We provide the first evidence that the ORF57-UIF interaction enables the recruitment of hTREX and TAP to KSHV intronless mRNAs in Aly-depleted cells. Strikingly, depletion of both Aly and UIF inhibits the formation of an ORF57-mediated nuclear export competent ribonucleoprotein particle and consequently prevents ORF57-mediated mRNA nuclear export and KSHV protein production. Importantly, these findings highlight that redundancy exists in the eukaryotic system for certain hTREX components involved in the mRNA nuclear export of intronless KSHV mRNAs. PMID:21814512

  12. A global network of RNA and protein interactions in Fronto Temporal Dementia

    Directory of Open Access Journals (Sweden)

    Francesca eFontana

    2015-03-01

    Full Text Available Fronto Temporal Dementia (FTD is a neurodegenerative disorder characterized by degeneration of the fronto temporal lobes and abnormal protein inclusions. It exhibits a broad clinicopathological spectrum and has been linked to mutations in seven different genes. We will provide a picture, which connects the products of these genes, albeit diverse in nature and function, in a network. Despite the paucity of information available for some of these genes, we believe that RNA processing and post-transcriptional regulation of gene expression might constitute a common theme in the network. Recent studies have unraveled the role of mutations affecting the functions of RNA binding proteins and regulation of microRNAs. This review will combine all the recent findings on genes involved in the pathogenesis of FTD, highlighting the importance of a common network of interactions in order to study and decipher the heterogeneous clinical manifestations associated with FTD. This approach could be helpful for the research of potential therapeutic strategies.

  13. Isotachophoresis of proteins in a networked microfluidic chip: experiment and 2-D simulation.

    Science.gov (United States)

    Cui, Huanchun; Dutta, Prashanta; Ivory, Cornelius F

    2007-04-01

    This paper reports both the experimental application and 2-D simulation of ITP of proteins in a networked microfluidic chip. Experiments demonstrate that a mixture of three fluorescent proteins can be concentrated and stacked into adjacent zones of pure protein under a constant voltage of 100 V over a 2 cm long microchannel. Measurements of the isotachophoretic velocity of the moving zones demonstrates that, during ITP under a constant voltage, the zone velocity decreases as more of the channel is occupied by the terminating electrolyte. A 2-D ITP model based on the Nernst-Planck equations illustrates the stacking and separation features of ITP using simulations of three virtual proteins. The self-sharpening behavior of ITP zones dispersed by a T-junction is clearly demonstrated both by experiment and by simulation. Comparison of 2-D simulations of ITP and zone electrophoresis (ZE) confirms that ZE lacks the ability to resharpen protein zones after they pass through a T-junction.

  14. Generating functional analysis of complex formation and dissociation in large protein interaction networks

    International Nuclear Information System (INIS)

    Coolen, A C C; Rabello, S

    2009-01-01

    We analyze large systems of interacting proteins, using techniques from the non-equilibrium statistical mechanics of disordered many-particle systems. Apart from protein production and removal, the most relevant microscopic processes in the proteome are complex formation and dissociation, and the microscopic degrees of freedom are the evolving concentrations of unbound proteins (in multiple post-translational states) and of protein complexes. Here we only include dimer-complexes, for mathematical simplicity, and we draw the network that describes which proteins are reaction partners from an ensemble of random graphs with an arbitrary degree distribution. We show how generating functional analysis methods can be used successfully to derive closed equations for dynamical order parameters, representing an exact macroscopic description of the complex formation and dissociation dynamics in the infinite system limit. We end this paper with a discussion of the possible routes towards solving the nontrivial order parameter equations, either exactly (in specific limits) or approximately.

  15. Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

    Science.gov (United States)

    Melak, Tilahun; Gakkhar, Sunita

    2015-12-01

    In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes. The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. Potential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to

  16. Using sequence similarity networks for visualization of relationships across diverse protein superfamilies.

    Directory of Open Access Journals (Sweden)

    Holly J Atkinson

    Full Text Available The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

  17. Using sequence similarity networks for visualization of relationships across diverse protein superfamilies.

    Science.gov (United States)

    Atkinson, Holly J; Morris, John H; Ferrin, Thomas E; Babbitt, Patricia C

    2009-01-01

    The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

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

    Directory of Open Access Journals (Sweden)

    Julien F Ollivier

    2010-11-01

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

  19. Neuroplasticity pathways and protein-interaction networks are modulated by vortioxetine in rodents

    DEFF Research Database (Denmark)

    Waller, Jessica A.; Nygaard, Sara Holm; Li, Yan

    2017-01-01

    species and sexes, different brain regions, and in response to distinct routes of administration and regimens. Conclusions: A recurring theme, based on the present study as well as previous findings, is that networks related to synaptic plasticity, synaptic transmission, signal transduction...... and rat in response to distinct treatment regimens and in different brain regions. Furthermore, analysis of complexes of physically-interacting proteins reveal that biomarkers involved in transcriptional regulation, neurodevelopment, neuroplasticity, and endocytosis are modulated by vortioxetine....... A subsequent qPCR study examining the expression of targets in the protein-protein interactome space in response to chronic vortioxetine treatment over a range of doses provides further biological validation that vortioxetine engages neuroplasticity networks. Thus, the same biology is regulated in different...

  20. Aftereffects of Spectrally Similar and Dissimilar Spectral Motion Adaptors in the Tritone Paradox

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    Stephanie Malek

    2018-05-01

    Full Text Available Shepard tones consist of octave-spaced components, whose amplitudes are generated under a fixed bell-shaped spectral envelope. They are well defined in pitch chroma, but generate octave confusions that in turn can produce ambiguities in the perceived relative pitch heights when their chromas are exactly a tritone apart (the tritone paradox. This study examined the effects of tonal context on relative pitch height judgments using adaptor sequences followed by target sequences (pairs of Shepard tones of different chromas separated by a tritone. Listeners judged whether the second target Shepard tone was higher or lower than the first. Adaptor sequences consisted of rising or falling scales (43 s at the beginning of each block, 4 s before each target sequence. Two sets of Shepard tones were used for adaptors and targets that were generated under spectral envelopes centered at either A3 (220 Hz and C6 (1,046 Hz. Pitch direction judgments (rising vs. falling to spectrally consistent (A3–A3, C6–C6 and inconsistent (A3–C6, C6–A3 adaptor-target combinations were studied. Large significant contrastive aftereffects (0.08–0.21 change in fraction of pitch direction responses were only found for the Shepard tones that were judged as higher in the control condition (judgments about the target sequences without adaptor sequences for the consistent adaptor-target conditions (A3–A3, C6–C6. The experiments rule out explanations based on non-sensory decision making processes. Possible explanations in terms of perceptual aftereffects caused by adaptation in central auditory frequency-motion detectors are discussed.

  1. Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

    Directory of Open Access Journals (Sweden)

    Bader Al-Anzi

    2015-05-01

    Full Text Available An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae. A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

  2. Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.

    Science.gov (United States)

    Al-Anzi, Bader; Arpp, Patrick; Gerges, Sherif; Ormerod, Christopher; Olsman, Noah; Zinn, Kai

    2015-05-01

    An approach combining genetic, proteomic, computational, and physiological analysis was used to define a protein network that regulates fat storage in budding yeast (Saccharomyces cerevisiae). A computational analysis of this network shows that it is not scale-free, and is best approximated by the Watts-Strogatz model, which generates "small-world" networks with high clustering and short path lengths. The network is also modular, containing energy level sensing proteins that connect to four output processes: autophagy, fatty acid synthesis, mRNA processing, and MAP kinase signaling. The importance of each protein to network function is dependent on its Katz centrality score, which is related both to the protein's position within a module and to the module's relationship to the network as a whole. The network is also divisible into subnetworks that span modular boundaries and regulate different aspects of fat metabolism. We used a combination of genetics and pharmacology to simultaneously block output from multiple network nodes. The phenotypic results of this blockage define patterns of communication among distant network nodes, and these patterns are consistent with the Watts-Strogatz model.

  3. The Prediction of Key Cytoskeleton Components Involved in Glomerular Diseases Based on a Protein-Protein Interaction Network.

    Science.gov (United States)

    Ding, Fangrui; Tan, Aidi; Ju, Wenjun; Li, Xuejuan; Li, Shao; Ding, Jie

    2016-01-01

    Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet

  4. 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

  5. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.

    Science.gov (United States)

    Vella, Danila; Zoppis, Italo; Mauri, Giancarlo; Mauri, Pierluigi; Di Silvestre, Dario

    2017-12-01

    The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

  6. The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean

    Directory of Open Access Journals (Sweden)

    Shuxian Li

    2018-04-01

    Full Text Available Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla is the primary cause of Phomopsis seed decay (PSD in soybean, Glycine max (L. Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein–protein interactions (PPI and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI database. Additionally, 149 plant cell wall degrading enzymes (PCWDE were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms.

  7. Stepping motor adaptor actuator for a commercial uhv linear motion feedthrough

    International Nuclear Information System (INIS)

    Iarocci, M.; Oversluizen, T.

    1989-01-01

    An adaptor coupling has been developed that will allow the attachment of a standard stepping motor to a precision commercial (Varian) uhv linear motion feedthrough. The assembly, consisting of the motor, motor adaptor, limit switches, etc. is clamped to the feedthrough body which can be done under vacuum conditions if necessary. With a 500 step/rev. stepping motor the resolution is 1.27 μm per step. We presently use this assembly in a remote location for the precise positioning of a beam sensing monitor. 2 refs., 3 figs

  8. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  9. Genetic Deletion of the Clathrin Adaptor GGA3 Reduces Anxiety and Alters GABAergic Transmission.

    Directory of Open Access Journals (Sweden)

    Kendall R Walker

    Full Text Available Golgi-localized γ-ear-containing ARF binding protein 3 (GGA3 is a monomeric clathrin adaptor that has been shown to regulate the trafficking of the Beta-site APP-cleaving enzyme (BACE1, which is required for production of the Alzheimer's disease (AD-associated amyloid βpeptide. Our previous studies have shown that BACE1 is degraded via the lysosomal pathway and that depletion of GGA3 results in increased BACE1 levels and activity owing to impaired lysosomal trafficking and degradation. We further demonstrated the role of GGA3 in the regulation of BACE1 in vivo by showing that BACE1 levels are increased in the brain of GGA3 null mice. We report here that GGA3 deletion results in novelty-induced hyperactivity and decreased anxiety-like behaviors. Given the pivotal role of GABAergic transmission in the regulation of anxiety-like behaviors, we performed electrophysiological recordings in hippocampal slices and found increased phasic and decreased tonic inhibition in the dentate gyrus granule cells (DGGC. Moreover, we found that the number of inhibitory synapses is increased in the dentate gyrus of GGA3 null mice in further support of the electrophysiological data. Thus, the increased GABAergic transmission is a leading candidate mechanism underlying the reduced anxiety-like behaviors observed in GGA3 null mice. All together these findings suggest that GGA3 plays a key role in GABAergic transmission. Since BACE1 levels are elevated in the brain of GGA3 null mice, it is possible that at least some of these phenotypes are a consequence of increased processing of BACE1 substrates.

  10. Genetic Deletion of the Clathrin Adaptor GGA3 Reduces Anxiety and Alters GABAergic Transmission.

    Science.gov (United States)

    Walker, Kendall R; Modgil, Amit; Albrecht, David; Lomoio, Selene; Haydon, Philip G; Moss, Stephen J; Tesco, Giuseppina

    2016-01-01

    Golgi-localized γ-ear-containing ARF binding protein 3 (GGA3) is a monomeric clathrin adaptor that has been shown to regulate the trafficking of the Beta-site APP-cleaving enzyme (BACE1), which is required for production of the Alzheimer's disease (AD)-associated amyloid βpeptide. Our previous studies have shown that BACE1 is degraded via the lysosomal pathway and that depletion of GGA3 results in increased BACE1 levels and activity owing to impaired lysosomal trafficking and degradation. We further demonstrated the role of GGA3 in the regulation of BACE1 in vivo by showing that BACE1 levels are increased in the brain of GGA3 null mice. We report here that GGA3 deletion results in novelty-induced hyperactivity and decreased anxiety-like behaviors. Given the pivotal role of GABAergic transmission in the regulation of anxiety-like behaviors, we performed electrophysiological recordings in hippocampal slices and found increased phasic and decreased tonic inhibition in the dentate gyrus granule cells (DGGC). Moreover, we found that the number of inhibitory synapses is increased in the dentate gyrus of GGA3 null mice in further support of the electrophysiological data. Thus, the increased GABAergic transmission is a leading candidate mechanism underlying the reduced anxiety-like behaviors observed in GGA3 null mice. All together these findings suggest that GGA3 plays a key role in GABAergic transmission. Since BACE1 levels are elevated in the brain of GGA3 null mice, it is possible that at least some of these phenotypes are a consequence of increased processing of BACE1 substrates.

  11. The adaptor Lnk (SH2B3): an emerging regulator in vascular cells and a link between immune and inflammatory signaling.

    Science.gov (United States)

    Devallière, Julie; Charreau, Béatrice

    2011-11-15

    A better knowledge of the process by which inflammatory extracellular signals are relayed from the plasma membrane to specific intracellular sites is a key step to understand how inflammation develops and how it is regulated. This review focuses on Lnk (SH2B3) a member, with SH2B1 and SH2B2, of the SH2B family of adaptor proteins that influences a variety of signaling pathways mediated by Janus kinase and receptor tyrosine kinases. SH2B adaptor proteins contain conserved dimerization, pleckstrin homology, and SH2 domains. Initially described as a regulator of hematopoiesis and lymphocyte differentiation, Lnk now emerges as a key regulator in hematopoeitic and non hematopoeitic cells such as endothelial cells (EC) moderating growth factor and cytokine receptor-mediated signaling. In EC, Lnk is a negative regulator of TNF signaling that reduce proinflammatory phenotype and prevent EC from apoptosis. Lnk is a modulator in integrin signaling and actin cytoskeleton organization in both platelets and EC with an impact on cell adhesion, migration and thrombosis. In this review, we discuss some recent insights proposing Lnk as a key regulator of bone marrow-endothelial progenitor cell kinetics, including the ability to cell growth, endothelial commitment, mobilization, and recruitment for vascular regeneration. Finally, novel findings also provided evidences that mutations in Lnk gene are strongly linked to myeloproliferative disorders but also autoimmune and inflammatory syndromes where both immune and vascular cells display a role. Overall, these studies emphasize the importance of the Lnk adaptor molecule not only as prognostic marker but also as potential therapeutic target. Copyright © 2011 Elsevier Inc. All rights reserved.

  12. The mitotic spindle protein SPAG5/Astrin connects to the Usher protein network postmitotically

    Directory of Open Access Journals (Sweden)

    Kersten Ferry FJ

    2012-04-01

    Full Text Available Abstract Background Mutations in the gene for Usher syndrome 2A (USH2A are causative for non-syndromic retinitis pigmentosa and Usher syndrome, a condition that is the most common cause of combined deaf-blindness. To gain insight into the molecular pathology underlying USH2A-associated retinal degeneration, we aimed to identify interacting proteins of USH2A isoform B (USH2AisoB in the retina. Results We identified the centrosomal and microtubule-associated protein sperm-associated antigen (SPAG5 in the retina. SPAG5 was also found to interact with another previously described USH2AisoB interaction partner: the centrosomal ninein-like protein NINLisoB. Using In situ hybridization, we found that Spag5 was widely expressed during murine embryonic development, with prominent signals in the eye, cochlea, brain, kidney and liver. SPAG5 expression in adult human tissues was detected by quantitative PCR, which identified expression in the retina, brain, intestine, kidney and testis. In the retina, Spag5, Ush2aisoB and NinlisoB were present at several subcellular structures of photoreceptor cells, and colocalized at the basal bodies. Conclusions Based on these results and on the suggested roles for USH proteins in vesicle transport and providing structural support to both the inner ear and the retina, we hypothesize that SPAG5, USH2AisoB and NINLisoB may function together in microtubule-based cytoplasmic trafficking of proteins that are essential for cilium formation, maintenance and/or function.

  13. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Science.gov (United States)

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  14. In-silico studies of neutral drift for functional protein interaction networks

    Science.gov (United States)

    Ali, Md Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

    We have developed a minimal physically-motivated model of protein-protein interaction networks. Our system consists of two classes of enzymes, activators (e.g. kinases) and deactivators (e.g. phosphatases), and the enzyme-mediated activation/deactivation rates are determined by sequence-dependent binding strengths between enzymes and their targets. The network is evolved by introducing random point mutations in the binding sequences where we assume that each new mutation is either fixed or entirely lost. We apply this model to studies of neutral drift in networks that yield oscillatory dynamics, where we start, for example, with a relatively simple network and allow it to evolve by adding nodes and connections while requiring that dynamics be conserved. Our studies demonstrate both the importance of employing a sequence-based evolutionary scheme and the relative rapidity (in evolutionary time) for the redistribution of function over new nodes via neutral drift. Surprisingly, in addition to this redistribution time we discovered another much slower timescale for network evolution, reflecting hidden order in sequence space that we interpret in terms of sparsely connected domains.

  15. 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.

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

    Science.gov (United States)

    Nacher, J. C.; Ochiai, T.; Hayashida, M.; Akutsu, T.

    2009-12-01

    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.

  17. 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.

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

    International Nuclear Information System (INIS)

    Nacher, J C; Ochiai, T; Hayashida, M; Akutsu, T

    2009-01-01

    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.

  19. 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.

  20. Identification of key residues for protein conformational transition using elastic network model.

    Science.gov (United States)

    Su, Ji Guo; Xu, Xian Jin; Li, Chun Hua; Chen, Wei Zu; Wang, Cun Xin

    2011-11-07

    Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.

  1. Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

    Science.gov (United States)

    Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

    2014-08-01

    Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

  2. Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics.

    Science.gov (United States)

    Sardiu, Mihaela E; Gilmore, Joshua M; Carrozza, Michael J; Li, Bing; Workman, Jerry L; Florens, Laurence; Washburn, Michael P

    2009-10-06

    Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.

  3. Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics.

    Directory of Open Access Journals (Sweden)

    Mihaela E Sardiu

    2009-10-01

    Full Text Available Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.

  4. The Protein Interaction Network of Bacteriophage Lambda with Its Host, Escherichia coli

    Science.gov (United States)

    Blasche, Sonja; Wuchty, Stefan; Rajagopala, Seesandra V.

    2013-01-01

    Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens. PMID:24049175

  5. Functional equivalency inferred from "authoritative sources" in networks of homologous proteins.

    Science.gov (United States)

    Natarajan, Shreedhar; Jakobsson, Eric

    2009-06-12

    A one-on-one mapping of protein functionality across different species is a critical component of comparative analysis. This paper presents a heuristic algorithm for discovering the Most Likely Functional Counterparts (MoLFunCs) of a protein, based on simple concepts from network theory. A key feature of our algorithm is utilization of the user's knowledge to assign high confidence to selected functional identification. We show use of the algorithm to retrieve functional equivalents for 7 membrane proteins, from an exploration of almost 40 genomes form multiple online resources. We verify the functional equivalency of our dataset through a series of tests that include sequence, structure and function comparisons. Comparison is made to the OMA methodology, which also identifies one-on-one mapping between proteins from different species. Based on that comparison, we believe that incorporation of user's knowledge as a key aspect of the technique adds value to purely statistical formal methods.

  6. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  7. The membrane stress response buffers lethal effects of lipid disequilibrium by reprogramming the protein homeostasis network.

    Science.gov (United States)

    Thibault, Guillaume; Shui, Guanghou; Kim, Woong; McAlister, Graeme C; Ismail, Nurzian; Gygi, Steven P; Wenk, Markus R; Ng, Davis T W

    2012-10-12

    Lipid composition can differ widely among organelles and even between leaflets of a membrane. Lipid homeostasis is critical because disequilibrium can have disease outcomes. Despite their importance, mechanisms maintaining lipid homeostasis remain poorly understood. Here, we establish a model system to study the global effects of lipid imbalance. Quantitative lipid profiling was integral to monitor changes to lipid composition and for system validation. Applying global transcriptional and proteomic analyses, a dramatically altered biochemical landscape was revealed from adaptive cells. The resulting composite regulation we term the "membrane stress response" (MSR) confers compensation, not through restoration of lipid composition, but by remodeling the protein homeostasis network. To validate its physiological significance, we analyzed the unfolded protein response (UPR), one facet of the MSR and a key regulator of protein homeostasis. We demonstrate that the UPR maintains protein biogenesis, quality control, and membrane integrity-functions otherwise lethally compromised in lipid dysregulated cells. Copyright © 2012 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Baoman Wang

    2015-01-01

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

  9. Quantitative analysis of the network structure that underlines the transitioning in mechanical responses of pea protein gels

    NARCIS (Netherlands)

    Munialo, C.D.; Linden, van der E.; Ako, K.; Jongh, de H.H.J.

    2015-01-01

    The objective of this study was to analyze quantitatively the network structure that underlines the transitioning in the mechanical responses of heat-induced pea protein gels. To achieve this, gels were prepared from pea proteins at varying pHs from 3.0 to 4.2 at a fixed 100 mg/mL protein

  10. 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

  11. Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

    Science.gov (United States)

    Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

    2013-01-01

    DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

  12. 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.

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

    DEFF Research Database (Denmark)

    Kuhn, Michael; Szklarczyk, Damian; Franceschini, Andrea

    2010-01-01

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

  14. 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.

  15. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  16. Prediction of the location and type of beta-turns in proteins using neural networks.

    OpenAIRE

    Shepherd, A. J.; Gorse, D.; Thornton, J. M.

    1999-01-01

    A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews cor...

  17. Annotating gene sets by mining large literature collections with protein networks.

    Science.gov (United States)

    Wang, Sheng; Ma, Jianzhu; Yu, Michael Ku; Zheng, Fan; Huang, Edward W; Han, Jiawei; Peng, Jian; Ideker, Trey

    2018-01-01

    Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

  18. Transmembrane adaptor molecules: a new category of lymphoid-cell markers

    Czech Academy of Sciences Publication Activity Database

    Tedoldi, S.; Paterson, J.C.; Hansmann, M.-L.; Natkunam, Y.; Rüdiger, T.; Angelisová, Pavla; Du, M.Q.; Roberton, H.; Roncador, G.; Sanchez, L.; Pozzobon, M.; Masir, N.; Barry, R.; Pileri, S.; Mason, D.Y.; Marafioti, T.; Hořejší, Václav

    2006-01-01

    Roč. 107, č. 1 (2006), s. 213-221 ISSN 0006-4971 R&D Projects: GA MŠk(CZ) 1M0506 Institutional research plan: CEZ:AV0Z50520514 Keywords : transmembrane adaptors * PAG * LIME Subject RIV: EC - Immunology Impact factor: 10.370, year: 2006

  19. GH32 family activity: a topological approach through protein contact networks.

    Science.gov (United States)

    Cimini, Sara; Di Paola, Luisa; Giuliani, Alessandro; Ridolfi, Alessandra; De Gara, Laura

    2016-11-01

    The application of Protein Contact Networks methodology allowed to highlight a novel response of border region between the two domains to substrate binding. Glycoside hydrolases (GH) are enzymes that mainly hydrolyze the glycosidic bond between two carbohydrates or a carbohydrate and a non-carbohydrate moiety. These enzymes are involved in many fundamental and diverse biological processes in plants. We have focused on the GH32 family, including enzymes very similar in both sequence and structure, each having however clear specificities of substrate preferences and kinetic properties. Structural and topological differences among proteins of the GH32 family have been here identified by means of an emerging approach (Protein Contact network, PCN) based on the formalization of 3D structures as contact networks among amino-acid residues. The PCN approach proved successful in both reconstructing the already known functional domains and in identifying the structural counterpart of the properties of GH32 enzymes, which remain uncertain, like their allosteric character. The main outcome of the study was the discovery of the activation upon binding of the border (cleft) region between the two domains. This reveals the allosteric nature of the enzymatic activity for all the analyzed forms in the GH32 family, a character yet to be highlighted in biochemical studies. Furthermore, we have been able to recognize a topological signature (graph energy) of the different affinity of the enzymes towards small and large substrates.

  20. PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

    Science.gov (United States)

    Pasquier, C; Promponas, V J; Hamodrakas, S J

    2001-08-15

    A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

  1. Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

    Science.gov (United States)

    Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

    2010-12-01

    The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  2. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Evolutionary Conservation and Emerging Functional Diversity of the Cytosolic Hsp70:J Protein Chaperone Network of Arabidopsis thaliana.

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    Verma, Amit K; Diwan, Danish; Raut, Sandeep; Dobriyal, Neha; Brown, Rebecca E; Gowda, Vinita; Hines, Justin K; Sahi, Chandan

    2017-06-07

    Heat shock proteins of 70 kDa (Hsp70s) partner with structurally diverse Hsp40s (J proteins), generating distinct chaperone networks in various cellular compartments that perform myriad housekeeping and stress-associated functions in all organisms. Plants, being sessile, need to constantly maintain their cellular proteostasis in response to external environmental cues. In these situations, the Hsp70:J protein machines may play an important role in fine-tuning cellular protein quality control. Although ubiquitous, the functional specificity and complexity of the plant Hsp70:J protein network has not been studied. Here, we analyzed the J protein network in the cytosol of Arabidopsis thaliana and, using yeast genetics, show that the functional specificities of most plant J proteins in fundamental chaperone functions are conserved across long evolutionary timescales. Detailed phylogenetic and functional analysis revealed that increased number, regulatory differences, and neofunctionalization in J proteins together contribute to the emerging functional diversity and complexity in the Hsp70:J protein network in higher plants. Based on the data presented, we propose that higher plants have orchestrated their "chaperome," especially their J protein complement, according to their specialized cellular and physiological stipulations. Copyright © 2017 Verma et al.

  5. Simplified Swarm Optimization-Based Function Module Detection in Protein–Protein Interaction Networks

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    Xianghan Zheng

    2017-04-01

    Full Text Available Proteomics research has become one of the most important topics in the field of life science and natural science. At present, research on protein–protein interaction networks (PPIN mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate detection mechanisms of functional modules in PPIN, including open database, existing detection algorithms, and recent solutions. After that, we describe the proposed approach based on the simplified swarm optimization (SSO algorithm and the knowledge of Gene Ontology (GO. The proposed solution implements the SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species datasets for experiment: fruitfly, mouse, scere, and human. The testing and analysis result show that the proposed solution is feasible, efficient, and could achieve a higher accuracy of prediction than existing approaches.

  6. Integrating atomistic molecular dynamics simulations, experiments and network analysis to study protein dynamics: strength in unity

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    Elena ePapaleo

    2015-05-01

    Full Text Available In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

  7. A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

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    Deng, Lei; Fan, Chao; Zeng, Zhiwen

    2017-12-28

    Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

  8. Cross-over between discrete and continuous protein structure space: insights into automatic classification and networks of protein structures.

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    Alberto Pascual-García

    2009-03-01

    Full Text Available Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications

  9. An assessment of machine and statistical learning approaches to inferring networks of protein-protein interactions

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    Browne Fiona

    2006-12-01

    Full Text Available Protein-protein interactions (PPI play a key role in many biological systems. Over the past few years, an explosion in availability of functional biological data obtained from high-throughput technologies to infer PPI has been observed. However, results obtained from such experiments show high rates of false positives and false negatives predictions as well as systematic predictive bias. Recent research has revealed that several machine and statistical learning methods applied to integrate relatively weak, diverse sources of large-scale functional data may provide improved predictive accuracy and coverage of PPI. In this paper we describe the effects of applying different computational, integrative methods to predict PPI in Saccharomyces cerevisiae. We investigated the predictive ability of combining different sets of relatively strong and weak predictive datasets. We analysed several genomic datasets ranging from mRNA co-expression to marginal essentiality. Moreover, we expanded an existing multi-source dataset from S. cerevisiae by constructing a new set of putative interactions extracted from Gene Ontology (GO- driven annotations in the Saccharomyces Genome Database. Different classification techniques: Simple Naive Bayesian (SNB, Multilayer Perceptron (MLP and K-Nearest Neighbors (KNN were evaluated. Relatively simple classification methods (i.e. less computing intensive and mathematically complex, such as SNB, have been proven to be proficient at predicting PPI. SNB produced the “highest” predictive quality obtaining an area under Receiver Operating Characteristic (ROC curve (AUC value of 0.99. The lowest AUC value of 0.90 was obtained by the KNN classifier. This assessment also demonstrates the strong predictive power of GO-driven models, which offered predictive performance above 0.90 using the different machine learning and statistical techniques. As the predictive power of single-source datasets became weaker MLP and SNB performed

  10. Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks.

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    Grimes, Mark; Hall, Benjamin; Foltz, Lauren; Levy, Tyler; Rikova, Klarisa; Gaiser, Jeremiah; Cook, William; Smirnova, Ekaterina; Wheeler, Travis; Clark, Neil R; Lachmann, Alexander; Zhang, Bin; Hornbeck, Peter; Ma'ayan, Avi; Comb, Michael

    2018-05-22

    Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  11. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

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    Hanson, Jack; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

    2017-03-01

    Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction. The new method, named SPOT-Disorder, has steadily improved over a similar method using a traditional, window-based neural network (SPINE-D) in all datasets tested without separate training on short and long disordered regions. Independent tests on four other datasets including the datasets from critical assessment of structure prediction (CASP) techniques and >10 000 annotated proteins from MobiDB, confirmed SPOT-Disorder as one of the best methods in disorder prediction. Moreover, initial studies indicate that the method is more accurate in predicting functional sites in disordered regions. These results highlight the usefulness combining LSTM with deep bidirectional recurrent neural networks in capturing non-local, long-range interactions for bioinformatics applications. SPOT-disorder is available as a web server and as a standalone program at: http://sparks-lab.org/server/SPOT-disorder/index.php . j.hanson@griffith.edu.au or yuedong.yang@griffith.edu.au or yaoqi.zhou@griffith.edu.au. Supplementary data is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  12. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

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    Sara Garamszegi

    Full Text Available A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen