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Sample records for multiple protein-protein interactions

  1. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

    DOE PAGES

    Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

    2015-01-01

    Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.« less

  2. PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

    PubMed Central

    Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

    2015-01-01

    Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species. PMID:26539460

  3. N-way FRET microscopy of multiple protein-protein interactions in live cells.

    PubMed

    Hoppe, Adam D; Scott, Brandon L; Welliver, Timothy P; Straight, Samuel W; Swanson, Joel A

    2013-01-01

    Fluorescence Resonance Energy Transfer (FRET) microscopy has emerged as a powerful tool to visualize nanoscale protein-protein interactions while capturing their microscale organization and millisecond dynamics. Recently, FRET microscopy was extended to imaging of multiple donor-acceptor pairs, thereby enabling visualization of multiple biochemical events within a single living cell. These methods require numerous equations that must be defined on a case-by-case basis. Here, we present a universal multispectral microscopy method (N-Way FRET) to enable quantitative imaging for any number of interacting and non-interacting FRET pairs. This approach redefines linear unmixing to incorporate the excitation and emission couplings created by FRET, which cannot be accounted for in conventional linear unmixing. Experiments on a three-fluorophore system using blue, yellow and red fluorescent proteins validate the method in living cells. In addition, we propose a simple linear algebra scheme for error propagation from input data to estimate the uncertainty in the computed FRET images. We demonstrate the strength of this approach by monitoring the oligomerization of three FP-tagged HIV Gag proteins whose tight association in the viral capsid is readily observed. Replacement of one FP-Gag molecule with a lipid raft-targeted FP allowed direct observation of Gag oligomerization with no association between FP-Gag and raft-targeted FP. The N-Way FRET method provides a new toolbox for capturing multiple molecular processes with high spatial and temporal resolution in living cells.

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

    PubMed Central

    2015-01-01

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

  5. Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources

    PubMed Central

    Xu, Bo; Lin, Hongfei; Chen, Yang; Yang, Zhihao; Liu, Hongfang

    2013-01-01

    Background Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification. Results We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks. Conclusions We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification. PMID:24386289

  6. Transient Protein-Protein Interaction of the SH3-Peptide Complex via Closely Located Multiple Binding Sites

    PubMed Central

    Hahn, Seungsoo; Kim, Dongsup

    2012-01-01

    Protein-protein interactions play an essential role in cellular processes. Certain proteins form stable complexes with their partner proteins, whereas others function by forming transient complexes. The conventional protein-protein interaction model describes an interaction between two proteins under the assumption that a protein binds to its partner protein through a single binding site. In this study, we improved the conventional interaction model by developing a Multiple-Site (MS) model in which a protein binds to its partner protein through closely located multiple binding sites on a surface of the partner protein by transiently docking at each binding site with individual binding free energies. To test this model, we used the protein-protein interaction mediated by Src homology 3 (SH3) domains. SH3 domains recognize their partners via a weak, transient interaction and are therefore promiscuous in nature. Because the MS model requires large amounts of data compared with the conventional interaction model, we used experimental data from the positionally addressable syntheses of peptides on cellulose membranes (SPOT-synthesis) technique. From the analysis of the experimental data, individual binding free energies for each binding site of peptides were extracted. A comparison of the individual binding free energies from the analysis with those from atomistic force fields gave a correlation coefficient of 0.66. Furthermore, application of the MS model to 10 SH3 domains lowers the prediction error by up to 9% compared with the conventional interaction model. This improvement in prediction originates from a more realistic description of complex formation than the conventional interaction model. The results suggested that, in many cases, SH3 domains increased the protein complex population through multiple binding sites of their partner proteins. Our study indicates that the consideration of general complex formation is important for the accurate description of

  7. Molecular modelling of protein-protein/protein-solvent interactions

    NASA Astrophysics Data System (ADS)

    Luchko, Tyler

    The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule

  8. Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

    PubMed Central

    Du, Pufeng; Wang, Lusheng

    2014-01-01

    One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. PMID:24466278

  9. Interaction entropy for protein-protein binding

    NASA Astrophysics Data System (ADS)

    Sun, Zhaoxi; Yan, Yu N.; Yang, Maoyou; Zhang, John Z. H.

    2017-03-01

    Protein-protein interactions are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein-protein binding is quantitatively characterized by the binding free energy whose accurate calculation from the first principle is a grand challenge in computational biology. In this paper, we show how the interaction entropy approach, which was recently proposed for protein-ligand binding free energy calculation, can be applied to computing the entropic contribution to the protein-protein binding free energy. Explicit theoretical derivation of the interaction entropy approach for protein-protein interaction system is given in detail from the basic definition. Extensive computational studies for a dozen realistic protein-protein interaction systems are carried out using the present approach and comparisons of the results for these protein-protein systems with those from the standard normal mode method are presented. Analysis of the present method for application in protein-protein binding as well as the limitation of the method in numerical computation is discussed. Our study and analysis of the results provided useful information for extracting correct entropic contribution in protein-protein binding from molecular dynamics simulations.

  10. Identification of a novel contactin-associated transmembrane receptor with multiple domains implicated in protein-protein interactions.

    PubMed Central

    Peles, E; Nativ, M; Lustig, M; Grumet, M; Schilling, J; Martinez, R; Plowman, G D; Schlessinger, J

    1997-01-01

    Receptor protein tyrosine phosphatase beta (RPTPbeta) expressed on the surface of glial cells binds to the glycosylphosphatidylinositol (GPI)-anchored recognition molecule contactin on neuronal cells leading to neurite outgrowth. We describe the cloning of a novel contactin-associated transmembrane receptor (p190/Caspr) containing a mosaic of domains implicated in protein-protein interactions. The extracellular domain of Caspr contains a neurophilin/coagulation factor homology domain, a region related to fibrinogen beta/gamma, epidermal growth factor-like repeats, neurexin motifs as well as unique PGY repeats found in a molluscan adhesive protein. The cytoplasmic domain of Caspr contains a proline-rich sequence capable of binding to a subclass of SH3 domains of signaling molecules. Caspr and contactin exist as a complex in rat brain and are bound to each other by means of lateral (cis) interactions in the plasma membrane. We propose that Caspr may function as a signaling component of contactin, enabling recruitment and activation of intracellular signaling pathways in neurons. The binding of RPTPbeta to the contactin-Caspr complex could provide a mechanism for cell-cell communication between glial cells and neurons during development. PMID:9118959

  11. Protopia: a protein-protein interaction tool

    PubMed Central

    Real-Chicharro, Alejandro; Ruiz-Mostazo, Iván; Navas-Delgado, Ismael; Kerzazi, Amine; Chniber, Othmane; Sánchez-Jiménez, Francisca; Medina, Miguel Ángel; Aldana-Montes, José F

    2009-01-01

    Background Protein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data . Results The tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein. Conclusion The tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks. PMID:19828077

  12. Targeting Multiple Conformations Leads to Small Molecule Inhibitors of the uPAR·uPA Protein-Protein Interaction that Block Cancer Cell Invasion

    PubMed Central

    Khanna, May; Wang, Fang; Jo, Inha; Knabe, W. Eric; Wilson, Sarah M.; Li, Liwei; Bum-Erdene, Khuchtumur; Li, Jing; Sledge, George; Khanna, Rajesh; Meroueh, Samy O.

    2011-01-01

    Interaction of the urokinase receptor (uPAR) with its binding partners including the urokinase-type plasminogen activator (uPA) at the cell surface triggers a series of proteolytic and signaling events that promote invasion and metastasis. Here, we report the discovery of a small molecule (IPR-456) and its derivatives that inhibit the tight uPAR·uPA protein-protein interaction. IPR-456 was discovered by virtual screening against multiple conformations of uPAR sampled from explicit-solvent molecular dynamics simulations. Biochemical characterization reveal that the compound binds to uPAR with sub-micromolar affinity (Kd = 310 nM) and inhibits the tight protein-protein interaction with an IC50 of 10 μM. Free energy calculations based on explicit-solvent molecular dynamics simulations suggested the importance of a carboxylate moiety on IPR-456, which was confirmed by the activity of several derivatives including IPR-803. Immunofluorescence imaging showed that IPR-456 inhibited uPA binding to uPAR of breast MDA-MB-231 tumor cells with an IC50 of 8 μM. The compounds blocked MDA-MB-231 cell invasion, but IPR-456 showed little effect on MDA-MB-231 migration, and no effect on adhesion, suggesting that uPAR mediates these processes through its other binding partners. PMID:21875078

  13. PREFACE: Protein protein interactions: principles and predictions

    NASA Astrophysics Data System (ADS)

    Nussinov, Ruth; Tsai, Chung-Jung

    2005-06-01

    Proteins are the `workhorses' of the cell. Their roles span functions as diverse as being molecular machines and signalling. They carry out catalytic reactions, transport, form viral capsids, traverse membranes and form regulated channels, transmit information from DNA to RNA, making possible the synthesis of new proteins, and they are responsible for the degradation of unnecessary proteins and nucleic acids. They are the vehicles of the immune response and are responsible for viral entry into the cell. Given their importance, considerable effort has been centered on the prediction of protein function. A prime way to do this is through identification of binding partners. If the function of at least one of the components with which the protein interacts is known, that should let us assign its function(s) and the pathway(s) in which it plays a role. This holds since the vast majority of their chores in the living cell involve protein-protein interactions. Hence, through the intricate network of these interactions we can map cellular pathways, their interconnectivities and their dynamic regulation. Their identification is at the heart of functional genomics; their prediction is crucial for drug discovery. Knowledge of the pathway, its topology, length, and dynamics may provide useful information for forecasting side effects. The goal of predicting protein-protein interactions is daunting. Some associations are obligatory, others are continuously forming and dissociating. In principle, from the physical standpoint, any two proteins can interact, but under what conditions and at which strength? The principles of protein-protein interactions are general: the non-covalent interactions of two proteins are largely the outcome of the hydrophobic effect, which drives the interactions. In addition, hydrogen bonds and electrostatic interactions play important roles. Thus, many of the interactions observed in vitro are the outcome of experimental overexpression. Protein disorder

  14. Length, protein protein interactions, and complexity

    NASA Astrophysics Data System (ADS)

    Tan, Taison; Frenkel, Daan; Gupta, Vishal; Deem, Michael W.

    2005-05-01

    The evolutionary reason for the increase in gene length from archaea to prokaryotes to eukaryotes observed in large-scale genome sequencing efforts has been unclear. We propose here that the increasing complexity of protein-protein interactions has driven the selection of longer proteins, as they are more able to distinguish among a larger number of distinct interactions due to their greater average surface area. Annotated protein sequences available from the SWISS-PROT database were analyzed for 13 eukaryotes, eight bacteria, and two archaea species. The number of subcellular locations to which each protein is associated is used as a measure of the number of interactions to which a protein participates. Two databases of yeast protein-protein interactions were used as another measure of the number of interactions to which each S. cerevisiae protein participates. Protein length is shown to correlate with both number of subcellular locations to which a protein is associated and number of interactions as measured by yeast two-hybrid experiments. Protein length is also shown to correlate with the probability that the protein is encoded by an essential gene. Interestingly, average protein length and number of subcellular locations are not significantly different between all human proteins and protein targets of known, marketed drugs. Increased protein length appears to be a significant mechanism by which the increasing complexity of protein-protein interaction networks is accommodated within the natural evolution of species. Consideration of protein length may be a valuable tool in drug design, one that predicts different strategies for inhibiting interactions in aberrant and normal pathways.

  15. Protein-protein interactions as drug targets.

    PubMed

    Skwarczynska, Malgorzata; Ottmann, Christian

    2015-01-01

    Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach. Here, we summarize the current state of small-molecule inhibition and stabilization of PPIs and review the active molecules from a structural and medicinal chemistry angle, especially focusing on the key examples of iNOS, LFA-1 and 14-3-3.

  16. Protein-protein interactions in multienzyme megasynthetases.

    PubMed

    Weissman, Kira J; Müller, Rolf

    2008-04-14

    The multienzyme polyketide synthases (PKSs), nonribosomal polypeptide synthetases (NRPSs), and their hybrids are responsible for the construction in bacteria of numerous natural products of clinical value. These systems generate high structural complexity by using a simple biosynthetic logic--that of the assembly line. Each of the individual steps in building the metabolites is designated to an independently folded domain within gigantic polypeptides. The domains are clustered into functional modules, and the modules are strung out along the proteins in the order in which they act. Every metabolite results, therefore, from the successive action of up to 100 individual catalysts. Despite the conceptual simplicity of this division-of-labor organization, we are only beginning to decipher the molecular details of the numerous protein-protein interactions that support assembly-line biosynthesis, and which are critical to attempts to re-engineer these systems as a tool in drug discovery. This review aims to summarize the state of knowledge about several aspects of protein-protein interactions, including current architectural models for PKS and NRPS systems, the central role of carrier proteins, and the structural basis for intersubunit recognition.

  17. INTERACT: an object oriented protein-protein interaction database.

    PubMed

    Eilbeck, K; Brass, A; Paton, N; Hodgman, C

    1999-01-01

    Protein-protein interactions provide vital information concerning the function of proteins, complexes and networks. Currently there is no widely accepted repository of this interaction information. Our aim is to provide a single database with the necessary architecture to fully store, query and analyse interaction data. An object oriented database has been created which provides scientists with a resource for examining existing protein-protein interactions and inferring possible interactions from the data stored. It also provides a basis for examining networks of interacting proteins, via analysis of the data stored. The database contains over a thousand interactions. k.eilbeck@stud.man.ac.uk

  18. Cation-pi interactions in protein-protein interfaces.

    PubMed

    Crowley, Peter B; Golovin, Adel

    2005-05-01

    Arginine is an abundant residue in protein-protein interfaces. The importance of this residue relates to the versatility of its side chain in intermolecular interactions. Different classes of protein-protein interfaces were surveyed for cation-pi interactions. Approximately half of the protein complexes and one-third of the homodimers analyzed were found to contain at least one intermolecular cation-pi pair. Interactions between arginine and tyrosine were found to be the most abundant. The electrostatic interaction energy was calculated to be approximately 3 kcal/mol, on average. A distance-based search of guanidinium:aromatic interactions was also performed using the Macromolecular Structure Database (MSD). This search revealed that half of the guanidinium:aromatic pairs pack in a coplanar manner. Furthermore, it was found that the cationic group of the cation-pi pair is frequently involved in intermolecular hydrogen bonds. In this manner the arginine side chain can participate in multiple interactions, providing a mechanism for inter-protein specificity. Thus, the cation-pi interaction is established as an important contributor to protein-protein interfaces.

  19. Protein-protein interactions: methods for detection and analysis.

    PubMed Central

    Phizicky, E M; Fields, S

    1995-01-01

    The function and activity of a protein are often modulated by other proteins with which it interacts. This review is intended as a practical guide to the analysis of such protein-protein interactions. We discuss biochemical methods such as protein affinity chromatography, affinity blotting, coimmunoprecipitation, and cross-linking; molecular biological methods such as protein probing, the two-hybrid system, and phage display: and genetic methods such as the isolation of extragenic suppressors, synthetic mutants, and unlinked noncomplementing mutants. We next describe how binding affinities can be evaluated by techniques including protein affinity chromatography, sedimentation, gel filtration, fluorescence methods, solid-phase sampling of equilibrium solutions, and surface plasmon resonance. Finally, three examples of well-characterized domains involved in multiple protein-protein interactions are examined. The emphasis of the discussion is on variations in the approaches, concerns in evaluating the results, and advantages and disadvantages of the techniques. PMID:7708014

  20. New Compound Classes: Protein-Protein Interactions.

    PubMed

    Ottmann, C

    2016-01-01

    "Protein-protein interactions (PPIs) are one of the most promising new targets in drug discovery. With estimates between 300,000 and 650,000 in human physiology, targeted modulation of PPIs would tremendously extend the "druggable" genome. In fact, in every disease a wealth of potentially addressable PPIs can be found making pharmacological intervention based on PPI modulators in principle a generally applicable technology. An impressing number of success stories in small-molecule PPI inhibition and natural-product PPI stabilization increasingly encourage academia and industry to invest in PPI modulation. In this chapter examples of both inhibition as well as stabilization of PPIs are reviewed including some of the technologies which has been used for their identification."

  1. Protein-protein interactions: scoring schemes and binding affinity.

    PubMed

    Gromiha, M Michael; Yugandhar, K; Jemimah, Sherlyn

    2017-06-01

    Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Linkers in the structural biology of protein-protein interactions.

    PubMed

    Reddy Chichili, Vishnu Priyanka; Kumar, Veerendra; Sivaraman, J

    2013-02-01

    Linkers or spacers are short amino acid sequences created in nature to separate multiple domains in a single protein. Most of them are rigid and function to prohibit unwanted interactions between the discrete domains. However, Gly-rich linkers are flexible, connecting various domains in a single protein without interfering with the function of each domain. The advent of recombinant DNA technology made it possible to fuse two interacting partners with the introduction of artificial linkers. Often, independent proteins may not exist as stable or structured proteins until they interact with their binding partner, following which they gain stability and the essential structural elements. Gly-rich linkers have been proven useful for these types of unstable interactions, particularly where the interaction is weak and transient, by creating a covalent link between the proteins to form a stable protein-protein complex. Gly-rich linkers are also employed to form stable covalently linked dimers, and to connect two independent domains that create a ligand-binding site or recognition sequence. The lengths of linkers vary from 2 to 31 amino acids, optimized for each condition so that the linker does not impose any constraints on the conformation or interactions of the linked partners. Various structures of covalently linked protein complexes have been described using X-ray crystallography, nuclear magnetic resonance and cryo-electron microscopy techniques. In this review, we evaluate several structural studies where linkers have been used to improve protein quality, to produce stable protein-protein complexes, and to obtain protein dimers.

  3. Evolutionary reprograming of protein-protein interaction specificity.

    PubMed

    Akiva, Eyal; Babbitt, Patricia C

    2015-10-22

    Using mutation libraries and deep sequencing, Aakre et al. study the evolution of protein-protein interactions using a toxin-antitoxin model. The results indicate probable trajectories via "intermediate" proteins that are promiscuous, thus avoiding transitions via non-interactions. These results extend observations about other biological interactions and enzyme evolution, suggesting broadly general principles.

  4. Discovering interacting domains and motifs in protein-protein interactions.

    PubMed

    Hugo, Willy; Sung, Wing-Kin; Ng, See-Kiong

    2013-01-01

    Many important biological processes, such as the signaling pathways, require protein-protein interactions (PPIs) that are designed for fast response to stimuli. These interactions are usually transient, easily formed, and disrupted, yet specific. Many of these transient interactions involve the binding of a protein domain to a short stretch (3-10) of amino acid residues, which can be characterized by a sequence pattern, i.e., a short linear motif (SLiM). We call these interacting domains and motifs domain-SLiM interactions. Existing methods have focused on discovering SLiMs in the interacting proteins' sequence data. With the recent increase in protein structures, we have a new opportunity to detect SLiMs directly from the proteins' 3D structures instead of their linear sequences. In this chapter, we describe a computational method called SLiMDIet to directly detect SLiMs on domain interfaces extracted from 3D structures of PPIs. SLiMDIet comprises two steps: (1) interaction interfaces belonging to the same domain are extracted and grouped together using structural clustering and (2) the extracted interaction interfaces in each cluster are structurally aligned to extract the corresponding SLiM. Using SLiMDIet, de novo SLiMs interacting with protein domains can be computationally detected from structurally clustered domain-SLiM interactions for PFAM domains which have available 3D structures in the PDB database.

  5. Protein-Protein Interaction (PPI) Network: Recent Advances in Drug Discovery.

    PubMed

    Athanasios, Alexiou; Charalampos, Vairaktarakis; Vasileios, Tsiamis; Ashraf, Ghulam Md

    2017-01-01

    The investigation of the cellular components, their interactions and related functions constitute the major conditions in order to understand the cell as an integrated system. More specifically, the Protein-Protein Interactions and the obtained networks are very important in the majority of biological functions and processes, while most of the proteins appear to activate their functionalities through their interaction. Our in depth review analysis, include Sixty-five peer-reviewed research and review studies from several bibliographic databases. The most significant components were fully described, filtered, combined and analyzed in order to provide documented proofs on the Protein-Protein Interaction Network' applications in biomedicine. The Protein-Protein Interaction Network' alignment and mapping give the opportunity of further knowledge extraction concerning the evolutionary relationships between the species through conserved pathways and protein complexes. Additionally, Protein-Protein Interaction Network information has been demonstrated to be able to predict functionally orthologous proteins within sequence homology clusters. Our review analysis concluded that, while Protein- Protein Interaction was used to be characterized just by their large and plain interacting surfaces, they were considered inapplicable for drug discovery studies for a long time. The present review explores multiple technologies implicated in Protein-Protein Interaction Networks, implicating their potential role in drug discovery mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. Dissecting protein-protein interactions using directed evolution.

    PubMed

    Bonsor, Daniel A; Sundberg, Eric J

    2011-04-05

    Protein-protein interactions are essential for life. They are responsible for most cellular functions and when they go awry often lead to disease. Proteins are inherently complex. They are flexible macromolecules whose constituent amino acid components act in combinatorial and networked ways when they engage one another in binding interactions. It is just this complexity that allows them to conduct such a broad array of biological functions. Despite decades of intense study of the molecular basis of protein-protein interactions, key gaps in our understanding remain, hindering our ability to accurately predict the specificities and affinities of their interactions. Until recently, most protein-protein investigations have been probed experimentally at the single-amino acid level, making them, by definition, incapable of capturing the combinatorial nature of, and networked communications between, the numerous residues within and outside of the protein-protein interface. This aspect of protein-protein interactions, however, is emerging as a major driving force for protein affinity and specificity. Understanding a combinatorial process necessarily requires a combinatorial experimental tool. Much like the organisms in which they reside, proteins naturally evolve over time, through a combinatorial process of mutagenesis and selection, to functionally associate. Elucidating the process by which proteins have evolved may be one of the keys to deciphering the molecular rules that govern their interactions with one another. Directed evolution is a technique performed in the laboratory that mimics natural evolution on a tractable time scale that has been utilized widely to engineer proteins with novel capabilities, including altered binding properties. In this review, we discuss directed evolution as an emerging tool for dissecting protein-protein interactions.

  7. Direct Probing of Protein-Protein Interactions

    SciTech Connect

    Noy, A; Sulchek, T A; Friddle, R W

    2005-03-10

    This project aimed to establish feasibility of using experimental techniques based on direct measurements of interaction forces on the single molecule scale to characterize equilibrium interaction potentials between individual biological molecules. Such capability will impact several research areas, ranging from rapid interaction screening capabilities to providing verifiable inputs for computational models. It should be one of the enabling technologies for modern proteomics research. This study used a combination of Monte-Carlo simulations, theoretical considerations, and direct experimental measurements to investigate two model systems that represented typical experimental situations: force-induced melting of DNA rigidly attached to the tip, and force-induced unbinding of a protein-antibody pair connected to flexible tethers. Our results establish that for both systems researchers can use force spectroscopy measurements to extract reliable information about equilibrium interaction potentials. However, the approaches necessary to extract these potentials in each case--Jarzynski reconstruction and Dynamic Force Spectroscopy--are very different. We also show how the thermodynamics and kinetics of unbinding process dictates the choice between in each case.

  8. Protein-Protein Interactions in Virus-Host Systems.

    PubMed

    Brito, Anderson F; Pinney, John W

    2017-01-01

    To study virus-host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions-viral-viral or host-host interactions-are constantly targeted and inhibited by exogenous interfaces mediating viral-host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these "hub proteins" evolve. "Party hubs" have multiple interfaces; they establish simultaneous/stable (domain-domain) interactions, and tend to evolve slowly. On the other hand, "date hubs" have few interfaces; they establish transient/weak (domain-motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein-protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface

  9. On the role of electrostatics on protein-protein interactions

    PubMed Central

    Zhang, Zhe; Witham, Shawn; Alexov, Emil

    2011-01-01

    The role of electrostatics on protein-protein interactions and binding is reviewed in this article. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and basic electrostatic effects occurring upon the formation of the complex are discussed. The role of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated and indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartment. At the end, the similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity. PMID:21572182

  10. Modulators of 14-3-3 Protein-Protein Interactions.

    PubMed

    Stevers, Loes M; Sijbesma, Eline; Botta, Maurizio; MacKintosh, Carol; Obsil, Tomas; Landrieu, Isabelle; Cau, Ylenia; Wilson, Andrew J; Karawajczyk, Anna; Eickhoff, Jan; Davis, Jeremy; Hann, Michael M; O'Mahony, Gavin; Doveston, Richard G; Brunsveld, Luc; Ottmann, Christian

    2017-10-02

    Direct interactions between proteins are essential for the regulation of their functions in biological pathways. Targeting the complex network of protein-protein interactions (PPIs) has now been widely recognized as an attractive means to therapeutically intervene in disease states. Even though this is a challenging endeavor and PPIs have long been regarded as 'undruggable' targets, the last two decades have seen an increasing number of successful examples of PPI modulators resulting in a growing interest in this field. PPI modulation requires novel approaches and the integrated efforts of multiple disciplines to be a fruitful strategy. This Perspective focuses on the hub protein 14-3-3, which has several hundred identified protein interaction partners and is therefore involved in a wide range of cellular processes and diseases. Here, we aim to provide an integrated overview of the approaches explored for the modulation of 14-3-3 PPIs and review the examples resulting from these efforts in both inhibiting and stabilizing specific 14-3-3 protein complexes by small molecules, peptide-mimetics and natural products.

  11. How Many Protein-Protein Interactions Types Exist in Nature?

    PubMed Central

    Mitra, Pralay; Zhang, Yang

    2012-01-01

    “Protein quaternary structure universe” refers to the ensemble of all protein-protein complexes across all organisms in nature. The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions. PMID:22719985

  12. Protein-Protein Interaction Reagents | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

  13. Protein-Protein Interaction Reagents | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below. Emory_CTD^2_PPI_Reagents.xlsx Contact: Haian Fu

  14. Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

  15. Protein-Protein Interactions (PPI) reagents: | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University has a library of genes used to study protein-protein interactions in mammalian cells. These genes are cloned in different mammalian expression vectors. A list of available cancer-associated genes can be accessed below.

  16. Small molecules that target phosphorylation dependent protein-protein interaction.

    PubMed

    Watanabe, Nobumoto; Osada, Hiroyuki

    2016-08-01

    Protein-protein interaction is one of the key events in the signal transduction pathway. The interaction changes the conformations, activities, localization and stabilities of the proteins, and transduces the signal to the next step. Frequently, this interaction occurs upon the protein phosphorylation. When upstream signals are stimulated, protein kinase(s) is/are activated and phosphorylate(s) their substrates, and induce the phosphorylation dependent protein-protein interaction. For this interaction, several domains in proteins are known to specifically recognize the phosphorylated residues of target proteins. These specific domains for interaction are important in the progression of the diseases caused by disordered signal transduction such as cancer. Thus small molecules that modulate this interaction are attractive lead compounds for the treatment of such diseases. In this review, we focused on three examples of phosphorylation dependent protein-protein interaction modules (14-3-3, polo box domain of Plk1 and F-box proteins in SCF ubiquitin ligases) and summarize small molecules that modulate their interaction. We also introduce our original screening system to identify such small molecules. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. PPIM: A Protein-Protein Interaction Database for Maize1

    PubMed Central

    Wu, Aibo; Xu, Xin-Jian; Lu, Le; Liu, Jingdong; Cao, Yongwei; Chen, Luonan; Wu, Jun; Zhao, Xing-Ming

    2016-01-01

    Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop. PMID:26620522

  18. Computational Prediction of Protein-Protein Interactions of Human Tyrosinase

    PubMed Central

    Wang, Su-Fang; Oh, Sangho; Si, Yue-Xiu; Wang, Zhi-Jiang; Han, Hong-Yan; Lee, Jinhyuk; Qian, Guo-Ying

    2012-01-01

    The various studies on tyrosinase have recently gained the attention of researchers due to their potential application values and the biological functions. In this study, we predicted the 3D structure of human tyrosinase and simulated the protein-protein interactions between tyrosinase and three binding partners, four and half LIM domains 2 (FHL2), cytochrome b-245 alpha polypeptide (CYBA), and RNA-binding motif protein 9 (RBM9). Our interaction simulations showed significant binding energy scores of −595.3 kcal/mol for FHL2, −859.1 kcal/mol for CYBA, and −821.3 kcal/mol for RBM9. We also investigated the residues of each protein facing toward the predicted site of interaction with tyrosinase. Our computational predictions will be useful for elucidating the protein-protein interactions of tyrosinase and studying its binding mechanisms. PMID:22577521

  19. Noninvasive imaging of protein-protein interactions in living animals

    NASA Astrophysics Data System (ADS)

    Luker, Gary D.; Sharma, Vijay; Pica, Christina M.; Dahlheimer, Julie L.; Li, Wei; Ochesky, Joseph; Ryan, Christine E.; Piwnica-Worms, Helen; Piwnica-Worms, David

    2002-05-01

    Protein-protein interactions control transcription, cell division, and cell proliferation as well as mediate signal transduction, oncogenic transformation, and regulation of cell death. Although a variety of methods have been used to investigate protein interactions in vitro and in cultured cells, none can analyze these interactions in intact, living animals. To enable noninvasive molecular imaging of protein-protein interactions in vivo by positron-emission tomography and fluorescence imaging, we engineered a fusion reporter gene comprising a mutant herpes simplex virus 1 thymidine kinase and green fluorescent protein for readout of a tetracycline-inducible, two-hybrid system in vivo. By using micro-positron-emission tomography, interactions between p53 tumor suppressor and the large T antigen of simian virus 40 were visualized in tumor xenografts of HeLa cells stably transfected with the imaging constructs. Imaging protein-binding partners in vivo will enable functional proteomics in whole animals and provide a tool for screening compounds targeted to specific protein-protein interactions in living animals.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

  1. Characterization of protein-protein interactions by isothermal titration calorimetry.

    PubMed

    Velazquez-Campoy, Adrian; Leavitt, Stephanie A; Freire, Ernesto

    2015-01-01

    The analysis of protein-protein interactions has attracted the attention of many researchers from both a fundamental point of view and a practical point of view. From a fundamental point of view, the development of an understanding of the signaling events triggered by the interaction of two or more proteins provides key information to elucidate the functioning of many cell processes. From a practical point of view, understanding protein-protein interactions at a quantitative level provides the foundation for the development of antagonists or agonists of those interactions. Isothermal Titration Calorimetry (ITC) is the only technique with the capability of measuring not only binding affinity but the enthalpic and entropic components that define affinity. Over the years, isothermal titration calorimeters have evolved in sensitivity and accuracy. Today, TA Instruments and MicroCal market instruments with the performance required to evaluate protein-protein interactions. In this methods paper, we describe general procedures to analyze heterodimeric (porcine pancreatic trypsin binding to soybean trypsin inhibitor) and homodimeric (bovine pancreatic α-chymotrypsin) protein associations by ITC.

  2. Evolution of protein-protein interaction networks in yeast.

    PubMed

    Schoenrock, Andrew; Burnside, Daniel; Moteshareie, Houman; Pitre, Sylvain; Hooshyar, Mohsen; Green, James R; Golshani, Ashkan; Dehne, Frank; Wong, Alex

    2017-01-01

    Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

  3. Evolution of protein-protein interaction networks in yeast

    PubMed Central

    Schoenrock, Andrew; Burnside, Daniel; Moteshareie, Houman; Pitre, Sylvain; Hooshyar, Mohsen; Green, James R.; Golshani, Ashkan; Dehne, Frank; Wong, Alex

    2017-01-01

    Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms. PMID:28248977

  4. Hash subgraph pairwise kernel for protein-protein interaction extraction.

    PubMed

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

    2012-01-01

    Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining (BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel approach on all five corpora and achieves state-of-the-art performance.

  5. [Chemical libraries dedicated to protein-protein interactions].

    PubMed

    Sperandio, Olivier; Villoutreix, Bruno O; Morelli, Xavier; Roche, Philippe

    2015-03-01

    The identification of complete networks of protein-protein interactions (PPI) within a cell has contributed to major breakthroughs in understanding biological pathways, host-pathogen interactions and cancer development. As a consequence, PPI have emerged as a new class of promising therapeutic targets. However, they are still considered as a challenging class of targets for drug discovery programs. Recent successes have allowed the characterization of structural and physicochemical properties of protein-protein interfaces leading to a better understanding of how they can be disrupted with small molecule compounds. In addition, characterization of the profiles of PPI inhibitors has allowed the development of PPI-focused libraries. In this review, we present the current efforts at developing chemical libraries dedicated to these innovative targets. © 2015 médecine/sciences – Inserm.

  6. Solid State NMR and Protein-Protein Interactions in Membranes

    PubMed Central

    Miao, Yimin; Cross, Timothy A.

    2013-01-01

    Solid state NMR spectroscopy has evolved rapidly in recent years into an excellent tool for the characterization of membrane proteins and their complexes. In the past few years it has also become clear that the structure of membrane proteins, especially helical membrane proteins is determined, in part, by the membrane environment. Therefore, the modeling of this environment by a liquid crystalline lipid bilayer for solid state NMR has generated a unique tool for the characterization of native conformational states, local and global dynamics, and high resolution structure for these proteins. Protein-protein interactions can also benefit from this solid state NMR capability to characterize membrane proteins in a native-like environment. These complexes take the form of oligomeric structures and hetero-protein interactions both with water soluble proteins and other membrane proteins. PMID:24034903

  7. Structural biology and drug discovery for protein-protein interactions.

    PubMed

    Jubb, Harry; Higueruelo, Alicia P; Winter, Anja; Blundell, Tom L

    2012-05-01

    Although targeting protein-protein interfaces of regulatory multiprotein complexes has become a significant focus in drug discovery, it continues to pose major challenges. Most interfaces would be classed as 'undruggable' by conventional analyses, as they tend to be large, flat and featureless. Over the past decade, encouragement has come from the discovery of hotspots that contribute much of the free energy of interaction, and this has led to the development of tethering methods that target small molecules to these sites, often inducing adaptive changes. Equally important has been the recognition that many protein-protein interactions involve a continuous epitope of one partner and a well-defined groove or series of specific small pockets. These observations have stimulated the development of stapled α-helical peptides and other proteomimetic approaches. They have also led to the realisation that fragments might gain low-affinity 'footholds' on some protein-protein interfaces, and that these fragments might be elaborated to useful modulators of the interactions.

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

    PubMed

    Kuchaiev, Oleksii; Rasajski, Marija; Higham, Desmond J; Przulj, Natasa

    2009-08-01

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

  9. Essential multimeric enzymes in kinetoplastid parasites: A host of potentially druggable protein-protein interactions.

    PubMed

    Wachsmuth, Leah M; Johnson, Meredith G; Gavenonis, Jason

    2017-06-01

    Parasitic diseases caused by kinetoplastid parasites of the genera Trypanosoma and Leishmania are an urgent public health crisis in the developing world. These closely related species possess a number of multimeric enzymes in highly conserved pathways involved in vital functions, such as redox homeostasis and nucleotide synthesis. Computational alanine scanning of these protein-protein interfaces has revealed a host of potentially ligandable sites on several established and emerging anti-parasitic drug targets. Analysis of interfaces with multiple clustered hotspots has suggested several potentially inhibitable protein-protein interactions that may have been overlooked by previous large-scale analyses focusing solely on secondary structure. These protein-protein interactions provide a promising lead for the development of new peptide and macrocycle inhibitors of these enzymes.

  10. Quantitative study of protein-protein interactions by quartz nanopipettes

    NASA Astrophysics Data System (ADS)

    Tiwari, Purushottam Babu; Astudillo, Luisana; Miksovska, Jaroslava; Wang, Xuewen; Li, Wenzhi; Darici, Yesim; He, Jin

    2014-08-01

    In this report, protein-modified quartz nanopipettes were used to quantitatively study protein-protein interactions in attoliter sensing volumes. As shown by numerical simulations, the ionic current through the conical-shaped nanopipette is very sensitive to the surface charge variation near the pore mouth. With the appropriate modification of negatively charged human neuroglobin (hNgb) onto the inner surface of a nanopipette, we were able to detect concentration-dependent current change when the hNgb-modified nanopipette tip was exposed to positively charged cytochrome c (Cyt c) with a series of concentrations in the bath solution. Such current change is due to the adsorption of Cyt c to the inner surface of the nanopipette through specific interactions with hNgb. In contrast, a smaller current change with weak concentration dependence was observed when Cyt c was replaced with lysozyme, which does not specifically bind to hNgb. The equilibrium dissociation constant (KD) for the Cyt c-hNgb complex formation was derived and the value matched very well with the result from surface plasmon resonance measurement. This is the first quantitative study of protein-protein interactions by a conical-shaped nanopore based on charge sensing. Our results demonstrate that nanopipettes can potentially be used as a label-free analytical tool to quantitatively characterize protein-protein interactions.In this report, protein-modified quartz nanopipettes were used to quantitatively study protein-protein interactions in attoliter sensing volumes. As shown by numerical simulations, the ionic current through the conical-shaped nanopipette is very sensitive to the surface charge variation near the pore mouth. With the appropriate modification of negatively charged human neuroglobin (hNgb) onto the inner surface of a nanopipette, we were able to detect concentration-dependent current change when the hNgb-modified nanopipette tip was exposed to positively charged cytochrome c (Cyt c) with

  11. Detecting protein-protein interactions using Renilla luciferase fusion proteins.

    PubMed

    Burbelo, Peter D; Kisailus, Adam E; Peck, Jeremy W

    2002-11-01

    We have developed a novel system designated the luciferase assay for protein detection (LAPD) to study protein-protein interactions. This method involves two protein fusions, a soluble reporter fusion and a fusion for immobilizing the target protein. The soluble reporter is an N-terminal Renilla luciferase fusion protein that exhibits high Renilla luciferase activity. Crude cleared lysates from transfected Cos1 cells that express the Renilla luciferase fusion protein can be used in binding assays with immobilized target proteins. Following incubation and washing, target-bound Renilla luciferase fusion proteins produce light from the coelenterazine substrate, indicating an interaction between the two proteins of interest. As proof of the principle, we reproduced known, transient protein-protein interactions between the Cdc42 GTPase and its effector proteins. GTPase Renilla fusion proteins produced in Cos1 cells were tested with immobilized recombinant GST-N-WASP and CEP5 effector proteins. Using this assay, we could detect specific interactions of Cdc42 with these effector proteins in approximately 50 min. The specificity of these interactions was demonstrated by showing that they were GTPase-specific and GTP-dependent and not seen with other unrelated target proteins. These results suggest that the LAPD method, which is both rapid and sensitive, may have research and practical applications.

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

    SciTech Connect

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

    2009-09-01

    We present an integrated platform that is used for the reconstruction and analysis of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey experiment data. At the heart of this pipeline is the Software Environment for Biological Network Inference (SEBINI), an interactive environment for the deployment and testing of network inference algorithms that use high-throughput data. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. For integration, comparison and analysis of the inferred protein-protein interactions with interaction evidence obtained from multiple public sources, the pipeline connects to the Collective Analysis of Biological Interaction Networks (CABIN) software. Incorporating BEPro3 into SEBINI and automatically feeding the resulting inferred network into CABIN, we have created a structured workflow for protein-protein network inference and supplemental analysis from sets of MS bait-prey experiments.

  13. Potential disruption of protein-protein interactions by graphene oxide

    NASA Astrophysics Data System (ADS)

    Feng, Mei; Kang, Hongsuk; Yang, Zaixing; Luan, Binquan; Zhou, Ruhong

    2016-06-01

    Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

  14. Protein-protein interactions as druggable targets: recent technological advances.

    PubMed

    Higueruelo, Alicia P; Jubb, Harry; Blundell, Tom L

    2013-10-01

    Classical target-based drug discovery, where large chemical libraries are screened using inhibitory assays for a single target, has struggled to find ligands that inhibit protein-protein interactions (PPI). Nevertheless, in the past decade there have been successes that have demonstrated that PPI can be useful drug targets, and the field is now evolving fast. This review focuses on the new approaches and concepts that are being developed to tackle these challenging targets: the use of fragment based methods to explore the chemical space, stapled peptides to regulate intracellular PPI, alternatives to competitive inhibition and the use of antibodies to enable small molecule discovery for these targets.

  15. Inferring High-Confidence Human Protein-Protein Interactions

    DTIC Science & Technology

    2012-01-01

    iPfam: visualization of protein-protein interactions in PDB at domain and amino acid resolutions. Bioinformatics 2005, 21(3):410–412. 43. Kanehisa M, Goto...comprised proteins that had the same specific func- tion or were subunits of the same protein complex, such as branched chain keto acid E1 alpha (BCKDHA...and branched chain keto acid E1 beta (BCKDHB) [3,29], and dynein cytoplasmic 2 intermediate chain 1 (D2LIC) and dynein cytoplasmic 2 heavy chain 1

  16. Protein-protein interaction predictions using text mining methods.

    PubMed

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

    2015-03-01

    It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Finding protein-protein interaction patterns by contact map matching.

    PubMed

    Melo, R C; Ribeiro, C; Murray, C S; Veloso, C J M; da Silveira, C H; Neshich, G; Meira, W; Carceroni, R L; Santoro, M M

    2007-10-05

    We propose a novel method for defining patterns of contacts present in protein-protein complexes. A new use of the traditional contact maps (more frequently used for representation of the intra-chain contacts) is presented for analysis of inter-chain contacts. Using an algorithm based on image processing techniques, we can compare protein-protein interaction maps and also obtain a dissimilarity score between them. The same algorithm used to compare the maps can align the contacts of all the complexes and be helpful in the determination of a pattern of conserved interactions at the interfaces. We present an example for the application of this method by analyzing the pattern of interaction of bovine pancreatic trypsin inhibitors and trypsins, chymotrypsins, a thrombin, a matriptase, and a kallikrein - all classified as serine proteases. We found 20 contacts conserved in trypsins and chymotrypsins and 3 specific ones are present in all the serine protease complexes studied. The method was able to identify important contacts for the protein family studied and the results are in agreement with the literature.

  18. Novel computational methods to design protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Zhou, Alice Qinhua; O'Hern, Corey; Regan, Lynne

    2014-03-01

    Despite the abundance of structural data, we still cannot accurately predict the structural and energetic changes resulting from mutations at protein interfaces. The inadequacy of current computational approaches to the analysis and design of protein-protein interactions has hampered the development of novel therapeutic and diagnostic agents. In this work, we apply a simple physical model that includes only a minimal set of geometrical constraints, excluded volume, and attractive van der Waals interactions to 1) rank the binding affinity of mutants of tetratricopeptide repeat proteins with their cognate peptides, 2) rank the energetics of binding of small designed proteins to the hydrophobic stem region of the influenza hemagglutinin protein, and 3) predict the stability of T4 lysozyme and staphylococcal nuclease mutants. This work will not only lead to a fundamental understanding of protein-protein interactions, but also to the development of efficient computational methods to rationally design protein interfaces with tunable specificity and affinity, and numerous applications in biomedicine. NSF DMR-1006537, PHY-1019147, Raymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences, and Howard Hughes Medical Institute.

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

    SciTech Connect

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

    2009-12-01

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

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  2. Evolvability of yeast protein-protein interaction interfaces.

    PubMed

    Talavera, David; Williams, Simon G; Norris, Matthew G S; Robertson, David L; Lovell, Simon C

    2012-06-22

    The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.

  3. Single-Molecule Study of Protein-Protein Interaction Dynamics in a Cell Signaling System

    SciTech Connect

    Tan, Xin; Nalbant, Perihan; Toutchkine, Alexei; Hu, Dehong; Vorpagel, Erich R.; Hahn, Klaus M.; Lu, H PETER.

    2004-01-15

    We report a combined single-molecule fluorescence and molecular dynamics (MD) simulation study of protein-protein interactions in a GTP-binding intracellular signaling protein Cdc42 in complex with a downstream effector protein WASP. A 13- kDa WASP fragment which binds only the activated GTP-loaded Cdc42 was labeled with a novel solvatochromic dye and used to probe hydrophobic interactions significant to Cdc42/WASP recognition. Our single-molecule fluorescence measurements have shown conformational fluctuations of the protein complex and suggested multiple conformational states at a wide range of time scales might be involved in protein interaction dynamics. Single-molecule experiments have revealed the dynamic disorder or protein-protein interactions within the Cdc42/WASP complex, which may be important for regulating downstream signaling events.

  4. Protein-protein interactions in the synaptonemal complex.

    PubMed Central

    Tarsounas, M; Pearlman, R E; Gasser, P J; Park, M S; Moens, P B

    1997-01-01

    In mammalian systems, an approximately M(r) 30,000 Cor1 protein has been identified as a major component of the meiotic prophase chromosome cores, and a M(r) 125,000 Syn1 protein is present between homologue cores where they are synapsed and form the synaptonemal complex (SC). Immunolocalization of these proteins during meiosis suggests possible homo- and heterotypic interactions between the two as well as possible interactions with yet unrecognized proteins. We used the two-hybrid system in the yeast Saccharomyces cerevisiae to detect possible protein-protein associations. Segments of hamsters Cor1 and Syn1 proteins were tested in various combinations for homo- and heterotypic interactions. In the cause of Cor1, homotypic interactions involve regions capable of coiled-coil formation, observation confirmed by in vitro affinity coprecipitation experiments. The two-hybrid assay detects no interaction of Cor1 protein with central and C-terminal fragments of Syn1 protein and no homotypic interactions involving these fragments of Syn1. Hamster Cor1 and Syn1 proteins both associate with the human ubiquitin-conjugation enzyme Hsubc9 as well as with the hamster Ubc9 homologue. The interactions between SC proteins and the Ubc9 protein may be significant for SC disassembly, which coincides with the repulsion of homologs by late prophase I, and also for the termination of sister centromere cohesiveness at anaphase II. Images PMID:9285814

  5. The binary protein-protein interaction landscape of Escherichia coli

    PubMed Central

    Rajagopala, Seesandra V.; Vlasblom, James; Arnold, Roland; Franca-Koh, Jonathan; Pakala, Suman B.; Phanse, Sadhna; Ceol, Arnaud; Häuser, Roman; Siszler, Gabriella; Wuchty, Stefan; Emili, Andrew; Babu, Mohan; Aloy, Patrick; Pieper, Rembert; Uetz, Peter

    2014-01-01

    Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (~70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, approximately doubling the number of known binary PPIs in E. coli. Integration of binary PPIs and genetic interactions revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that could be mapped within multi-protein complexes were informative regarding internal topology and indicated that interactions within complexes are significantly more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily significant model microbe. PMID:24561554

  6. Stabilization of protein-protein interactions by small molecules.

    PubMed

    Giordanetto, Fabrizio; Schäfer, Anja; Ottmann, Christian

    2014-11-01

    Protein-protein interactions (PPIs) are implicated in every disease and mastering the ability to influence PPIs with small molecules would considerably enlarge the druggable genome. Whereas inhibition of PPIs has repeatedly been shown to work successfully, targeted stabilization of PPIs is underrepresented in the literature. This is all the more surprising because natural products like FK506, rapamycin, brefeldin, forskolin and fusicoccin confer their physiological activity by stabilizing specific PPIs. However, recently a number of very interesting synthetic molecules have been reported from drug discovery projects that indeed achieve their desired activities by stabilizing either homo- or hetero-oligomeric complexes of their target proteins. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. A method for predicting protein-protein interaction types.

    PubMed

    Silberberg, Yael; Kupiec, Martin; Sharan, Roded

    2014-01-01

    Protein-protein interactions (PPIs) govern basic cellular processes through signal transduction and complex formation. The diversity of those processes gives rise to a remarkable diversity of interactions types, ranging from transient phosphorylation interactions to stable covalent bonding. Despite our increasing knowledge on PPIs in humans and other species, their types remain relatively unexplored and few annotations of types exist in public databases. Here, we propose the first method for systematic prediction of PPI type based solely on the techniques by which the interaction was detected. We show that different detection methods are better suited for detecting specific types. We apply our method to ten interaction types on a large scale human PPI dataset. We evaluate the performance of the method using both internal cross validation and external data sources. In cross validation, we obtain an area under receiver operating characteristic (ROC) curve ranging from 0.65 to 0.97 with an average of 0.84 across the predicted types. Comparing the predicted interaction types to external data sources, we obtained significant agreements for phosphorylation and ubiquitination interactions, with hypergeometric p-value = 2.3e(-54) and 5.6e(-28) respectively. We examine the biological relevance of our predictions using known signaling pathways and chart the abundance of interaction types in cell processes. Finally, we investigate the cross-relations between different interaction types within the network and characterize the discovered patterns, or motifs. We expect the resulting annotated network to facilitate the reconstruction of process-specific subnetworks and assist in predicting protein function or interaction.

  8. Comprehensive peptidomimetic libraries targeting protein-protein interactions.

    PubMed

    Whitby, Landon R; Boger, Dale L

    2012-10-16

    Transient protein-protein interactions (PPIs) are essential components in cellular signaling pathways as well as in important processes such as viral infection, replication, and immune suppression. The unknown or uncharacterized PPIs involved in such interaction networks often represent compelling therapeutic targets for drug discovery. To date, however, the main strategies for discovery of small molecule modulators of PPIs are typically limited to structurally characterized targets. Recent developments in molecular scaffolds that mimic the side chain display of peptide secondary structures have yielded effective designs, but few screening libraries of such mimetics are available to interrogate PPI targets. We initiated a program to prepare a comprehensive small molecule library designed to mimic the three major recognition motifs that mediate PPIs (α-helix, β-turn, and β-strand). Three libraries would be built around templates designed to mimic each such secondary structure and substituted with all triplet combinations of groups representing the 20 natural amino acid side chains. When combined, the three libraries would contain a member capable of mimicking the key interaction and recognition residues of most targetable PPIs. In this Account, we summarize the results of the design, synthesis, and validation of an 8000 member α-helix mimetic library and a 4200 member β-turn mimetic library. We expect that the screening of these libraries will not only provide lead structures against α-helix- or β-turn-mediated protein-protein or peptide-receptor interactions, even if the nature of the interaction is unknown, but also yield key insights into the recognition motif (α-helix or β-turn) and identify the key residues mediating the interaction. Consistent with this expectation, the screening of the libraries against p53/MDM2 and HIV-1 gp41 (α-helix mimetic library) or the opioid receptors (β-turn mimetic library) led to the discovery of library members expected

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

    NASA Astrophysics Data System (ADS)

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

    2004-04-01

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

  10. Interface-Resolved Network of Protein-Protein Interactions

    PubMed Central

    Johnson, Margaret E.; Hummer, Gerhard

    2013-01-01

    We define an interface-interaction network (IIN) to capture the specificity and competition between protein-protein interactions (PPI). This new type of network represents interactions between individual interfaces used in functional protein binding and thereby contains the detail necessary to describe the competition and cooperation between any pair of binding partners. Here we establish a general framework for the construction of IINs that merges computational structure-based interface assignment with careful curation of available literature. To complement limited structural data, the inclusion of biochemical data is critical for achieving the accuracy and completeness necessary to analyze the specificity and competition between the protein interactions. Firstly, this procedure provides a means to clarify the information content of existing data on purported protein interactions and to remove indirect and spurious interactions. Secondly, the IIN we have constructed here for proteins involved in clathrin-mediated endocytosis (CME) exhibits distinctive topological properties. In contrast to PPI networks with their global and relatively dense connectivity, the fragmentation of the IIN into distinctive network modules suggests that different functional pressures act on the evolution of its topology. Large modules in the IIN are formed by interfaces sharing specificity for certain domain types, such as SH3 domains distributed across different proteins. The shared and distinct specificity of an interface is necessary for effective negative and positive design of highly selective binding targets. Lastly, the organization of detailed structural data in a network format allows one to identify pathways of specific binding interactions and thereby predict effects of mutations at specific surfaces on a protein and of specific binding inhibitors, as we explore in several examples. Overall, the endocytosis IIN is remarkably complex and rich in features masked in the coarser

  11. Highly specific protein-protein interactions, evolution and negative design.

    PubMed

    Sear, Richard P

    2004-12-01

    We consider highly specific protein-protein interactions in proteomes of simple model proteins. We are inspired by the work of Zarrinpar et al (2003 Nature 426 676). They took a binding domain in a signalling pathway in yeast and replaced it with domains of the same class but from different organisms. They found that the probability of a protein binding to a protein from the proteome of a different organism is rather high, around one half. We calculate the probability of a model protein from one proteome binding to the protein of a different proteome. These proteomes are obtained by sampling the space of functional proteomes uniformly. In agreement with Zarrinpar et al we find that the probability of a protein binding a protein from another proteome is rather high, of order one tenth. Our results, together with those of Zarrinpar et al, suggest that designing, say, a peptide to block or reconstitute a single signalling pathway, without affecting any other pathways, requires knowledge of all the partners of the class of binding domains the peptide is designed to mimic. This knowledge is required to use negative design to explicitly design out interactions of the peptide with proteins other than its target. We also found that patches that are required to bind with high specificity evolve more slowly than those that are required only to not bind to any other patch. This is consistent with some analysis of sequence data for proteins engaged in highly specific interactions.

  12. Structural Analysis of Protein-Protein Interactions in Type I Polyketide Synthases

    PubMed Central

    Xu, Wei; Qiao, Kangjian; Tang, Yi

    2013-01-01

    Polyketide synthases (PKSs) are responsible for synthesizing a myriad of natural products with agricultural, medicinal relevance. The PKSs consist of multiple functional domains of which each can catalyze a specified chemical reaction leading to the synthesis of polyketides. Biochemical studies showed that protein-substrate and protein-protein interactions play crucial roles in these complex regio-/stereo- selective biochemical processes. Recent developments on X-ray crystallography and protein NMR techniques have allowed us to understand the biosynthetic mechanism of these enzymes from their structures. These structural studies have facilitated the elucidation of sequence-function relationship of PKSs and will ultimately contribute to the prediction of product structure. This review will focus on the current knowledge of type I PKS structures and the protein-protein interactions in this system. PMID:23249187

  13. Protein-Protein Interaction Network and Gene Ontology

    NASA Astrophysics Data System (ADS)

    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.

  14. Prediction and integration of regulatory and protein-protein interactions

    SciTech Connect

    Wichadakul, Duangdao; McDermott, Jason E.; Samudrala, Ram

    2009-04-20

    Knowledge of transcriptional regulatory interactions (TRIs) is essential for exploring functional genomics and systems biology in any organism. While several results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast [1] and worm [2]. Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a method for the integration of various data sets to predict TRIs for 54 organisms in the Bioverse [3]. We describe how to compile and handle various formats and identifiers of data sets from different sources, and how to predict the TRIs using a homology-based approach, utilizing the compiled data sets. Integrated data sets include experimentally verified TRIs, binding sites of transcription factors, promoter sequences, protein sub-cellular localization, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein-protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological dynamics of an integrated network with gene expression under different conditions, essential for exploring functional genomics and systems biology.

  15. Protein-protein interaction network of celiac disease.

    PubMed

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.

  16. Parallel Force Assay for Protein-Protein Interactions

    PubMed Central

    Aschenbrenner, Daniela; Pippig, Diana A.; Klamecka, Kamila; Limmer, Katja; Leonhardt, Heinrich; Gaub, Hermann E.

    2014-01-01

    Quantitative proteome research is greatly promoted by high-resolution parallel format assays. A characterization of protein complexes based on binding forces offers an unparalleled dynamic range and allows for the effective discrimination of non-specific interactions. Here we present a DNA-based Molecular Force Assay to quantify protein-protein interactions, namely the bond between different variants of GFP and GFP-binding nanobodies. We present different strategies to adjust the maximum sensitivity window of the assay by influencing the binding strength of the DNA reference duplexes. The binding of the nanobody Enhancer to the different GFP constructs is compared at high sensitivity of the assay. Whereas the binding strength to wild type and enhanced GFP are equal within experimental error, stronger binding to superfolder GFP is observed. This difference in binding strength is attributed to alterations in the amino acids that form contacts according to the crystal structure of the initial wild type GFP-Enhancer complex. Moreover, we outline the potential for large-scale parallelization of the assay. PMID:25546146

  17. Insights from protein-protein interaction studies on bacterial pathogenesis.

    PubMed

    Gagarinova, Alla; Phanse, Sadhna; Cygler, Miroslaw; Babu, Mohan

    2017-09-01

    The threat bacterial pathogens pose to human health is increasing with the number and distribution of antibiotic-resistant bacteria, while the rate of discovery of new antimicrobials dwindles. Proteomics is playing key roles in understanding the molecular mechanisms of bacterial pathogenesis, and in identifying disease outcome determinants. The physical associations identified by proteomics can provide the means to develop pathogen-specific treatment methods that reduce the spread of antibiotic resistance and alleviate the negative effects of broad-spectrum antibiotics on beneficial bacteria. Areas covered: This review discusses recent trends in proteomics and introduces new and developing approaches that can be applied to the study of protein-protein interactions (PPIs) underlying bacterial pathogenesis. The approaches examined encompass options for mapping proteomes as well as stable and transient interactions in vivo and in vitro. We also explored the coverage of bacterial and human-bacterial PPIs, knowledge gaps in this area, and how they can be filled. Expert commentary: Identifying potential antimicrobial candidates is confounded by the complex molecular biology of bacterial pathogenesis and the lack of knowledge about PPIs underlying this process. Proteomics approaches can offer new perspectives for mechanistic insights and identify essential targets for guiding the discovery of next generation antimicrobials.

  18. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  19. Free energy decomposition of protein-protein interactions.

    PubMed Central

    Noskov, S Y; Lim, C

    2001-01-01

    A free energy decomposition scheme has been developed and tested on antibody-antigen and protease-inhibitor binding for which accurate experimental structures were available for both free and bound proteins. Using the x-ray coordinates of the free and bound proteins, the absolute binding free energy was computed assuming additivity of three well-defined, physical processes: desolvation of the x-ray structures, isomerization of the x-ray conformation to a nearby local minimum in the gas-phase, and subsequent noncovalent complex formation in the gas phase. This free energy scheme, together with the Generalized Born model for computing the electrostatic solvation free energy, yielded binding free energies in remarkable agreement with experimental data. Two assumptions commonly used in theoretical treatments; viz., the rigid-binding approximation (which assumes no conformational change upon complexation) and the neglect of vdW interactions, were found to yield large errors in the binding free energy. Protein-protein vdW and electrostatic interactions between complementary surfaces over a relatively large area (1400--1700 A(2)) were found to drive antibody-antigen and protease-inhibitor binding. PMID:11463622

  20. Targeting protein-protein interactions as an anticancer strategy.

    PubMed

    Ivanov, Andrei A; Khuri, Fadlo R; Fu, Haian

    2013-07-01

    The emergence and convergence of cancer genomics, targeted therapies, and network oncology have significantly expanded the landscape of protein-protein interaction (PPI) networks in cancer for therapeutic discovery. Extensive biological and clinical investigations have led to the identification of protein interaction hubs and nodes that are critical for the acquisition and maintenance of characteristics of cancer essential for cell transformation. Such cancer-enabling PPIs have become promising therapeutic targets. With technological advances in PPI modulator discovery and validation of PPI-targeting agents in clinical settings, targeting of PPI interfaces as an anticancer strategy has become a reality. Future research directed at genomics-based PPI target discovery, PPI interface characterization, PPI-focused chemical library design, and patient-genomic subpopulation-driven clinical studies is expected to accelerate the development of the next generation of PPI-based anticancer agents for personalized precision medicine. Here we briefly review prominent PPIs that mediate cancer-acquired properties, highlight recognized challenges and promising clinical results in targeting PPIs, and outline emerging opportunities. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Protein-Protein Interactions of Viroporins in Coronaviruses and Paramyxoviruses: New Targets for Antivirals?

    PubMed Central

    Torres, Jaume; Surya, Wahyu; Li, Yan; Liu, Ding Xiang

    2015-01-01

    Viroporins are members of a rapidly growing family of channel-forming small polypeptides found in viruses. The present review will be focused on recent structural and protein-protein interaction information involving two viroporins found in enveloped viruses that target the respiratory tract; (i) the envelope protein in coronaviruses and (ii) the small hydrophobic protein in paramyxoviruses. Deletion of these two viroporins leads to viral attenuation in vivo, whereas data from cell culture shows involvement in the regulation of stress and inflammation. The channel activity and structure of some representative members of these viroporins have been recently characterized in some detail. In addition, searches for protein-protein interactions using yeast-two hybrid techniques have shed light on possible functional roles for their exposed cytoplasmic domains. A deeper analysis of these interactions should not only provide a more complete overview of the multiple functions of these viroporins, but also suggest novel strategies that target protein-protein interactions as much needed antivirals. These should complement current efforts to block viroporin channel activity. PMID:26053927

  2. Using support vector machine for improving protein-protein interaction prediction utilizing domain interactions

    SciTech Connect

    Singhal, Mudita; Shah, Anuj R.; Brown, Roslyn N.; Adkins, Joshua N.

    2010-10-02

    Understanding protein interactions is essential to gain insights into the biological processes at the whole cell level. The high-throughput experimental techniques for determining protein-protein interactions (PPI) are error prone and expensive with low overlap amongst them. Although several computational methods have been proposed for predicting protein interactions there is definite room for improvement. Here we present DomainSVM, a predictive method for PPI that uses computationally inferred domain-domain interaction values in a Support Vector Machine framework to predict protein interactions. DomainSVM method utilizes evidence of multiple interacting domains to predict a protein interaction. It outperforms existing methods of PPI prediction by achieving very high explanation ratios, precision, specificity, sensitivity and F-measure values in a 10 fold cross-validation study conducted on the positive and negative PPIs in yeast. A Functional comparison study using GO annotations on the positive and the negative test sets is presented in addition to discussing novel PPI predictions in Salmonella Typhimurium.

  3. Comparative analysis of five protein-protein interaction corpora.

    PubMed

    Pyysalo, Sampo; Airola, Antti; Heimonen, Juho; Björne, Jari; Ginter, Filip; Salakoski, Tapio

    2008-04-11

    Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate. We present the first comparative evaluation of the diverse PPI corpora, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties. For the evaluation, we unify the corpus PPI annotations to a shared level of information, consisting of undirected, untyped binary interactions of non-static types with no identification of the words specifying the interaction, no negations, and no interaction certainty. We find that the F-score performance of a state-of-the-art PPI extraction method varies on average 19 percentage units and in some cases over 30 percentage units between the different evaluated corpora. The differences stemming from the choice of corpus can thus be substantially larger than differences between the performance of PPI extraction methods, which suggests definite limits on the ability to compare methods evaluated on different resources. We analyse a number of potential sources for these differences and identify factors explaining approximately half of the variance. We further suggest ways in which the difficulty of the PPI extraction tasks codified by different corpora can be determined to advance comparability. Our analysis also identifies points of agreement and disagreement in PPI corpus annotation that are rarely explicitly stated by the authors of the corpora. Our comparative analysis uncovers key similarities and differences between the diverse PPI corpora, thus taking an important step towards standardization. In the course of this study we have created a major practical contribution in

  4. Stabilizer-Guided Inhibition of Protein-Protein Interactions.

    PubMed

    Milroy, Lech-Gustav; Bartel, Maria; Henen, Morkos A; Leysen, Seppe; Adriaans, Joris M C; Brunsveld, Luc; Landrieu, Isabelle; Ottmann, Christian

    2015-12-21

    The discovery of novel protein-protein interaction (PPI) modulators represents one of the great molecular challenges of the modern era. PPIs can be modulated by either inhibitor or stabilizer compounds, which target different though proximal regions of the protein interface. In principle, protein-stabilizer complexes can guide the design of PPI inhibitors (and vice versa). In the present work, we combine X-ray crystallographic data from both stabilizer and inhibitor co-crystal complexes of the adapter protein 14-3-3 to characterize, down to the atomic scale, inhibitors of the 14-3-3/Tau PPI, a potential drug target to treat Alzheimer's disease. The most potent compound notably inhibited the binding of phosphorylated full-length Tau to 14-3-3 according to NMR spectroscopy studies. Our work sets a precedent for the rational design of PPI inhibitors guided by PPI stabilizer-protein complexes while potentially enabling access to new synthetically tractable stabilizers of 14-3-3 and other PPIs. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. From Topology to Phenotype in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Pržulj, Nataša

    We have recently witnessed an explosion in biological network data along with the development of computational approaches for their analyses. This new interdisciplinary research area is an integral part of systems biology, promising to provide new insights into organizational principles of life, as well as into evolution and disease. However, there is a danger that the area might become hindered by several emerging issues. In particular, there is typically a weak link between biological and computational scientists, resulting in the use of simple computational techniques of limited potential to explain these complex biological data. Hence, there is a danger that the community might view the topological features of network data as mere statistics, ignoring the value of the information contained in these data. This might result in the imposition of scientific doctrines, such as scale-free-centric (on the modelling side) and genome-centric (on the biological side) opinions onto this nascent research area. In this chapter, we take a network science perspective and present a brief, high-level overview of the area, commenting on possible challenges ahead. We focus on protein-protein interaction networks (PINs) in which nodes correspond to proteins in a cell and edges to physical bindings between the proteins.

  6. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective.

    PubMed

    Feng, Yanghe; Wang, Qi; Wang, Tengjiao

    2017-01-01

    The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper.

  7. Targeting Protein-Protein Interactions for Parasite Control

    PubMed Central

    Taylor, Christina M.; Fischer, Kerstin; Abubucker, Sahar; Wang, Zhengyuan; Martin, John; Jiang, Daojun; Magliano, Marc; Rosso, Marie-Noëlle; Li, Ben-Wen; Fischer, Peter U.; Mitreva, Makedonka

    2011-01-01

    Finding new drug targets for pathogenic infections would be of great utility for humanity, as there is a large need to develop new drugs to fight infections due to the developing resistance and side effects of current treatments. Current drug targets for pathogen infections involve only a single protein. However, proteins rarely act in isolation, and the majority of biological processes occur via interactions with other proteins, so protein-protein interactions (PPIs) offer a realm of unexplored potential drug targets and are thought to be the next-generation of drug targets. Parasitic worms were chosen for this study because they have deleterious effects on human health, livestock, and plants, costing society billions of dollars annually and many sequenced genomes are available. In this study, we present a computational approach that utilizes whole genomes of 6 parasitic and 1 free-living worm species and 2 hosts. The species were placed in orthologous groups, then binned in species-specific ortholgous groups. Proteins that are essential and conserved among species that span a phyla are of greatest value, as they provide foundations for developing broad-control strategies. Two PPI databases were used to find PPIs within the species specific bins. PPIs with unique helminth proteins and helminth proteins with unique features relative to the host, such as indels, were prioritized as drug targets. The PPIs were scored based on RNAi phenotype and homology to the PDB (Protein DataBank). EST data for the various life stages, GO annotation, and druggability were also taken into consideration. Several PPIs emerged from this study as potential drug targets. A few interactions were supported by co-localization of expression in M. incognita (plant parasite) and B. malayi (H. sapiens parasite), which have extremely different modes of parasitism. As more genomes of pathogens are sequenced and PPI databases expanded, this methodology will become increasingly applicable. PMID

  8. Optical methods in the study of protein-protein interactions.

    PubMed

    Masi, Alessio; Cicchi, Riccardo; Carloni, Adolfo; Pavone, Francesco Saverio; Arcangeli, Annarosa

    2010-01-01

    Förster (or Fluorescence) resonance energy transfer (FRET) is a physical process in which energy is transferred nonradiatively from an excited fluorophore, serving as a donor, to another chromophore (acceptor). Among the techniques related to fluorescence microscopy, FRET is unique in providing signals sensitive to intra- and intermolecular distances in the 1-10 nm range. Because of its potency, FRET is increasingly used to visualize and quantify the dynamics of protein-protein interaction in living cells, with high spatio-temporal resolution. Here we describe the physical bases of FRET, detailing the principal methods applied: (1) measurement of signal intensity and (2) analysis of fluorescence lifetime (FLIM). Although several technical complications must be carefully considered, both methods can be applied fruitfully to specific fields. For example, FRET based on intensity detection is more suitable to follow biological phenomena at a finely tuned spatial and temporal scale. Furthermore, a specific fluorescence signal occurring close to the plasma membrane (< or = 100 nm) can be obtained using a total internal reflection fluorescence (TIRF) microscopy system. When performing FRET experiments, care must be also taken to the method chosen for labeling interacting proteins. Two principal tools can be applied: (1) fluorophore tagged antibodies; (2) recombinant fluorescent fusion proteins. The latter method essentially takes advantage of the discovery and use of spontaneously fluorescent proteins, like the green fluorescent protein (GFP). Until now, FRET has been widely used to analyze the structural characteristics of several proteins, including integrins and ion channels. More recently, this method has been applied to clarify the interaction dynamics of these classes of membrane proteins with cytosolic signaling proteins. We report two examples in which the interaction dynamics between integrins and ion channels have been studied with FRET methods. Using

  9. Protein-protein interaction network of celiac disease

    PubMed Central

    Zamanian Azodi, Mona; Peyvandi, Hassan; Rostami-Nejad, Mohammad; Safaei, Akram; Rostami, Kamran; Vafaee, Reza; Heidari, Mohammadhossein; Hosseini, Mostafa; Zali, Mohammad Reza

    2016-01-01

    Aim: The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Background: Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. Results: According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Conclusion: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease. PMID:27895852

  10. Quantifying the Molecular Origins of Opposite Solvent Effects on Protein-Protein Interactions

    PubMed Central

    Vagenende, Vincent; Han, Alvin X.; Pek, Han B.; Loo, Bernard L. W.

    2013-01-01

    Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments. PMID:23696727

  11. A Laboratory-Intensive Course on the Experimental Study of Protein-Protein Interactions

    ERIC Educational Resources Information Center

    Witherow, D. Scott; Carson, Sue

    2011-01-01

    The study of protein-protein interactions is important to scientists in a wide range of disciplines. We present here the assessment of a lab-intensive course that teaches students techniques used to identify and further study protein-protein interactions. One of the unique elements of the course is that students perform a yeast two-hybrid screen…

  12. A Laboratory-Intensive Course on the Experimental Study of Protein-Protein Interactions

    ERIC Educational Resources Information Center

    Witherow, D. Scott; Carson, Sue

    2011-01-01

    The study of protein-protein interactions is important to scientists in a wide range of disciplines. We present here the assessment of a lab-intensive course that teaches students techniques used to identify and further study protein-protein interactions. One of the unique elements of the course is that students perform a yeast two-hybrid screen…

  13. A reliability measure of protein-protein interactions and a reliability measure-based search engine.

    PubMed

    Park, Byungkyu; Han, Kyungsook

    2010-02-01

    Many methods developed for estimating the reliability of protein-protein interactions are based on the topology of protein-protein interaction networks. This paper describes a new reliability measure for protein-protein interactions, which does not rely on the topology of protein interaction networks, but expresses biological information on functional roles, sub-cellular localisations and protein classes as a scoring schema. The new measure is useful for filtering many spurious interactions, as well as for estimating the reliability of protein interaction data. In particular, the reliability measure can be used to search protein-protein interactions with the desired reliability in databases. The reliability-based search engine is available at http://yeast.hpid.org. We believe this is the first search engine for interacting proteins, which is made available to public. The search engine and the reliability measure of protein interactions should provide useful information for determining proteins to focus on.

  14. STRING v10: protein-protein interaction networks, integrated over the tree of life.

    PubMed

    Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan; Forslund, Kristoffer; Heller, Davide; Huerta-Cepas, Jaime; Simonovic, Milan; Roth, Alexander; Santos, Alberto; Tsafou, Kalliopi P; Kuhn, Michael; Bork, Peer; Jensen, Lars J; von Mering, Christian

    2015-01-01

    The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. An effective system for detecting protein-protein interaction based on in vivo cleavage by PPV NIa protease.

    PubMed

    Zheng, Nuoyan; Huang, Xiahe; Yin, Bojiao; Wang, Dan; Xie, Qi

    2012-12-01

    Detection of protein-protein interaction can provide valuable information for investigating the biological function of proteins. The current methods that applied in protein-protein interaction, such as co-immunoprecipitation and pull down etc., often cause plenty of working time due to the burdensome cloning and purification procedures. Here we established a system that characterization of protein-protein interaction was accomplished by co-expression and simply purification of target proteins from one expression cassette within E. coli system. We modified pET vector into co-expression vector pInvivo which encoded PPV NIa protease, two cleavage site F and two multiple cloning sites that flanking cleavage sites. The target proteins (for example: protein A and protein B) were inserted at multiple cloning sites and translated into polyprotein in the order of MBP tag-protein A-site F-PPV NIa protease-site F-protein B-His(6) tag. PPV NIa protease carried out intracellular cleavage along expression, then led to the separation of polyprotein components, therefore, the interaction between protein A-protein B can be detected through one-step purification and analysis. Negative control for protein B was brought into this system for monitoring interaction specificity. We successfully employed this system to prove two cases of reported protien-protein interaction: RHA2a/ANAC and FTA/FTB. In conclusion, a convenient and efficient system has been successfully developed for detecting protein-protein interaction.

  16. Single-Molecule Study of Protein-Protein and Protein-DNA Interaction Dynamics

    SciTech Connect

    Lu, H. PETER

    2005-03-01

    Protein-protein and protein-DNA interactions play critical roles in biological functions of living cells, such as cell signaling, receptor-ligand activation, cellular metabolism, DNA damage recognition and repair, gene expression, replication, etc. These protein interactions often involve complex mechanisms and inhomogeneous dynamics with significant conformational changes. Protein-protein, protein-ligand, and protein-DNA interactions are often intrinsically single-molecule processes at an induction stage associated with the initiation of crucial early eents in living cells. For example, cell-signaling processes are often initiated through a few copies of protein-interaction complexes, being amplified along the signaling pathway.

  17. Studying protein-protein interactions via blot overlay/far western blot.

    PubMed

    Hall, Randy A

    2015-01-01

    Blot overlay is a useful method for studying protein-protein interactions. This technique involves fractionating proteins on SDS-PAGE, blotting to nitrocellulose or PVDF membrane, and then incubating with a probe of interest. The probe is typically a protein that is radiolabeled, biotinylated, or simply visualized with a specific antibody. When the probe is visualized via antibody detection, this technique is often referred to as "Far Western blot." Many different kinds of protein-protein interactions can be studied via blot overlay, and the method is applicable to screens for unknown protein-protein interactions as well as to the detailed characterization of known interactions.

  18. Mining the characteristic interaction patterns on protein-protein binding interfaces.

    PubMed

    Li, Yan; Liu, Zhihai; Han, Li; Li, Chengke; Wang, Renxiao

    2013-09-23

    Protein-protein interactions are observed in various biological processes. They are important for understanding the underlying molecular mechanisms and can be potential targets for developing small-molecule regulators of such processes. Previous studies suggest that certain residues on protein-protein binding interfaces are "hot spots". As an extension to this concept, we have developed a residue-based method to identify the characteristic interaction patterns (CIPs) on protein-protein binding interfaces, in which each pattern is a cluster of four contacting residues. Systematic analysis was conducted on a nonredundant set of 1,222 protein-protein binding interfaces selected out of the entire Protein Data Bank. Favored interaction patterns across different protein-protein binding interfaces were retrieved by considering both geometrical and chemical conservations. As demonstrated on two test tests, our method was able to predict hot spot residues on protein-protein binding interfaces with good recall scores and acceptable precision scores. By analyzing the function annotations and the evolutionary tree of the protein-protein complexes in our data set, we also observed that protein-protein interfaces sharing common characteristic interaction patterns are normally associated with identical or similar biological functions.

  19. Inferring Domain-Domain Interactions from Protein-Protein Interactions with Formal Concept Analysis

    PubMed Central

    Khor, Susan

    2014-01-01

    Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. PMID:24586450

  20. Development of small molecules designed to modulate protein-protein interactions.

    PubMed

    Che, Ye; Brooks, Bernard R; Marshall, Garland R

    2006-02-01

    Protein-protein interactions are ubiquitous, essential to almost all known biological processes, and offer attractive opportunities for therapeutic intervention. Developing small molecules that modulate protein-protein interactions is challenging, owing to the large size of protein-complex interface, the lack of well-defined binding pockets, etc. We describe a general approach based on the "privileged-structure hypothesis" [Che, Ph.D. Thesis, Washington University, 2003] - that any organic templates capable of mimicking surfaces of protein-recognition motifs are potential privileged scaffolds as protein-complex antagonists--to address the challenges inherent in the discovery of small-molecule inhibitors of protein-protein interactions.

  1. Protein-Protein Interactions as New Targets for Ion Channel Drug Discovery

    PubMed Central

    Stoilova-McPhie, Svetla; Ali, Syed; Laezza, Fernanda

    2014-01-01

    Protein-protein interactions (PPI) are key molecular elements that provide the basis of signaling in virtually all cellular processes. The precision and specificity of these molecular interactions have ignited a strong interest in pursuing PPI surfaces as new targets for drug discovery, especially against ion channels in the central nervous system (CNS) where selectivity and specificity are vital for developing drugs with limited side effects. Ion channels are large transmembrane domain proteins assembled with multiple regulatory proteins binding to the intracellular portion of channels. These macromolecular complexes are difficult to isolate, purify and reconstitute, posing a significant barrier in targeting these PPI for drug discovery purposes. Here, we will provide a short overview of salient features of PPI and discuss successful studies focusing on protein-channel interactions that could inspire new drug discovery campaigns targeting ion channel complexes. PMID:25485305

  2. Protein-Protein Interactions of the Baculovirus Per Os Infectivity Factors in the PIF Complex.

    PubMed

    Zheng, Qin; Shen, Yunwang; Kon, Xiangshuo; Zhang, Jianjia; Feng, Min; Wu, Xiaofeng

    2017-01-28

    After ingestion of occlusion bodies, the occlusion-derived viruses (ODVs) of baculoviruses establish the first round of infection within the larval host midgut cells. Several ODV envelope proteins, called per os infectivity factors (PIFs), have been shown to be essential for oral infection. Eight PIFs have been identified to date, including P74, PIFs1-6, and Ac110. At least six PIFs: P74, PIFs1-4, PIF6, together with three other ODV-specific proteins: Ac5, P95 (Ac83), and Ac108, have been reported to form a complex on the ODV surface. In this study, in order to understand the interactions of these PIFs, the direct protein-protein interactions of the nine components of the Autographa californica multiple nucleopolyhedrovirus (AcMNPV) PIF complex were investigated using yeast two-hybrid (Y2H) combined with bimolecular fluorescence complementation (BiFC) assay. Six direct interactions comprising PIF1-PIF2, PIF1-PIF3, PIF1-PIF4, PIF1-P95, PIF2-PIF3, and PIF3-PIF4, were identified in Y2H analysis, and these results were further verified by BiFC. For P74, PIF6, Ac5 and Ac108, no direct interaction was identified. P95 (Ac83) was identified to interact with PIF1 and further Y2H analysis of the truncations and deletion mutants showed that the predicted P95 chitin-binding domain and PIF1 100-200aa were responsible for P95 interaction with PIF1. Furthermore, a summary of the protein-protein interactions of PIFs reported so far, comprising 10 reciprocal interactions and 2 self-interactions, is presented, which will facilitate our understanding of the characteristic of PIF complex.

  3. INDEX: Incremental depth extension approach for protein-protein interaction networks alignment.

    PubMed

    Mir, Abolfazl; Naghibzadeh, Mahmoud; Saadati, Nayyereh

    2017-08-30

    High-throughput methods have provided us with a large amount of data pertaining to protein-protein interaction networks. The alignment of these networks enables us to better understand biological systems. Given the fact that the alignment of networks is computationally intractable, it is important to introduce a more efficient and accurate algorithm which finds as large as possible similar areas among networks. This paper proposes a new algorithm named INDEX for the global alignment of protein-protein interaction networks. INDEX has multiple phases. First, it computes topological and biological scores of proteins and creates the initial alignment based on the proposed matching score strategy. Using networks topologies and aligned proteins, it then selects a set of high scoring proteins in each phase and extends new alignments around them until final alignment is obtained. Proposing a new alignment strategy, detailed consideration of matching scores, and growth of the alignment core has led INDEX to obtain a larger common connected subgraph with a much greater number of edges compared with previous methods. Regarding other measures such as edge correctness, symmetric substructure score, and runtime, the proposed algorithm performed considerably better than existing popular methods. Our results show that INDEX can be a promising method for identifying functionally conserved interactions. The INDEX executable file is available at https://github.com/a-mir/index/. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Protein-protein interactions: principles, techniques, and their potential role in new drug development.

    PubMed

    Khan, Shagufta H; Ahmad, Faizan; Ahmad, Nihal; Flynn, Daniel C; Kumar, Raj

    2011-06-01

    A vast network of genes is inter-linked through protein-protein interactions and is critical component of almost every biological process under physiological conditions. Any disruption of the biologically essential network leads to pathological conditions resulting into related diseases. Therefore, proper understanding of biological functions warrants a comprehensive knowledge of protein-protein interactions and the molecular mechanisms that govern such processes. The importance of protein-protein interaction process is highlighted by the fact that a number of powerful techniques/methods have been developed to understand how such interactions take place under various physiological and pathological conditions. Many of the key protein-protein interactions are known to participate in disease-associated signaling pathways, and represent novel targets for therapeutic intervention. Thus, controlling protein-protein interactions offers a rich dividend for the discovery of new drug targets. Availability of various tools to study and the knowledge of human genome have put us in a unique position to understand highly complex biological network, and the mechanisms involved therein. In this review article, we have summarized protein-protein interaction networks, techniques/methods of their binding/kinetic parameters, and the role of these interactions in the development of potential tools for drug designing.

  5. Conditional random field approach to prediction of protein-protein interactions using domain information.

    PubMed

    Hayashida, Morihiro; Kamada, Mayumi; Song, Jiangning; Akutsu, Tatsuya

    2011-06-20

    For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins. We propose novel methods using conditional random fields for predicting protein-protein interactions. We focus on the mutual information between residues, and combine it with conditional random fields. In the methods, protein-protein interactions are modeled using domain-domain interactions. We perform computational experiments using protein-protein interaction datasets for several organisms, and calculate AUC (Area Under ROC Curve) score. The results suggest that our proposed methods with and without mutual information outperform EM (Expectation Maximization) method proposed by Deng et al., which is one of the best predictors based on domain-domain interactions. We propose novel methods using conditional random fields with and without mutual information between domains. Our methods based on domain-domain interactions are useful for predicting protein-protein interactions.

  6. Protein-Protein Interactions in Transcription: A Fertile Ground for Helix Mimetics

    PubMed Central

    Guarracino, Danielle A.; Bullock, Brooke N.; Arora, Paramjit S.

    2010-01-01

    Designed ligands that inhibit protein-protein interactions involved in gene expression are valuable as reagents for genomics research and as leads for drug discovery efforts. Selective modulation of protein-protein interactions has proven to be a daunting task for synthetic ligands; however, the last decade has seen significant advances in inhibitor design, especially for helical protein interfaces. This review discusses examples of transcriptional complexes targeted by designer helices. PMID:20882600

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

    PubMed

    Klein, Mark A

    2014-08-14

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

  8. Mini review: protein-protein interactions in transcription: a fertile ground for helix mimetics.

    PubMed

    Guarracino, Danielle A; Bullock, Brooke N; Arora, Paramjit S

    2011-01-01

    Designed ligands that inhibit protein-protein interactions involved in gene expression are valuable as reagents for genomics research and as leads for drug discovery efforts. Selective modulation of protein-protein interactions has proven to be a daunting task for synthetic ligands; however, the last decade has seen significant advances in inhibitor design, especially for helical protein interfaces. This review discusses examples of transcriptional complexes targeted by designer helices. 2010 Wiley Periodicals, Inc.

  9. Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

    PubMed

    Du, Tianchuan; Liao, Li; Wu, Cathy H

    2016-12-01

    Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

  10. Using data fusion for scoring reliability of protein-protein interactions.

    PubMed

    Vazifedoost, Alireza; Rahgozar, Maseud; Moshiri, Behzad; Sadeghi, Mehdi; Chua, Hon Nian; Ng, See Kiong; Wong, Limsoon

    2014-08-01

    Protein-protein interactions (PPIs) are important for understanding the cellular mechanisms of biological functions, but the reliability of PPIs extracted by high-throughput assays is known to be low. To address this, many current methods use multiple evidence from different sources of information to compute reliability scores for such PPIs. However, they often combine the evidence without taking into account the uncertainty of the evidence values, potential dependencies between the information sources used and missing values from some information sources. We propose to formulate the task of scoring PPIs using multiple information sources as a multi-criteria decision making problem that can be solved using data fusion to model potential interactions between the multiple information sources. Using data fusion, the amount of contribution from each information source can be proportioned accordingly to systematically score the reliability of PPIs. Our experimental results showed that the reliability scores assigned by our data fusion method can effectively classify highly reliable PPIs from multiple information sources, with substantial improvement in scoring over conventional approach such as the Adjust CD-Distance approach. In addition, the underlying interactions between the information sources used, as well as their relative importance, can also be determined with our data fusion approach. We also showed that such knowledge can be used to effectively handle missing values from information sources.

  11. Computational biology for target discovery and characterization: a feasibility study in protein-protein interaction detection

    SciTech Connect

    Zhou, C; Zemla, A

    2009-02-25

    In this work we developed new code for detecting putative multi-domain protein-protein interactions for a small network of bacterial pathogen proteins, and determined how structure-driven domain-fusion (DF) methods should be scaled up for whole-proteome analysis. Protein-protein interactions are of great interest in structural biology and are important for understanding the biology of pathogens. The ability to predict protein-protein interactions provides a means for development of anti-microbials that may interfer with key processes in pathogenicity. The function of a protein-protein complex can be elucidated through knowledge of its structure. The overall goal of this project was to determine the feasibility of extending current LLNL capabilities to produce a high-throughput systems bio-informatics capability for identification and characterization of putative interacting protein partners within known or suspected small protein networks. We extended an existing LLNL methodology for identification of putative protein-protein interacting partners (Chakicherla et al (in review)) by writing a new code to identify multi-domain-fusion linkages (3 or more per complex). We applied these codes to the proteins in the Yersinia pestis quorum sensing network, known as the lsr operon, which comprises a virulence mechanism in this pathogen. We determined that efficient application of our computational algorithms in high-throughput for detection of putative protein-protein complexes genome wide would require pre-computation of PDB domains and construction of a domain-domain association database.

  12. Examining post-translational modification-mediated protein-protein interactions using a chemical proteomics approach.

    PubMed

    Li, Xiang; Foley, Emily A; Kawashima, Shigehiro A; Molloy, Kelly R; Li, Yinyin; Chait, Brian T; Kapoor, Tarun M

    2013-03-01

    Post-translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM-dependent protein-protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein-protein interactions. We have recently developed CLASPI (cross-linking-assisted and stable isotope labeling in cell culture-based protein identification), a chemical proteomics approach to examine protein-protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation-dependent protein-protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine-9 (H3K9Me₃)-dependent protein-protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation-dependent protein-protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine-3 (H3T3-Phos), a mitotic histone "mark" appearing exclusively during cell division. Our approach identified survivin, the only known H3T3-Phos-binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation "mark". Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3-Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me₃). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein-protein interactions mediated by PTMs. Copyright © 2013 The Protein Society.

  13. The role of electrostatics in protein-protein interactions of a monoclonal antibody.

    PubMed

    Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

    2014-07-07

    Understanding how protein-protein interactions depend on the choice of buffer, salt, ionic strength, and pH is needed to have better control over protein solution behavior. Here, we have characterized the pH and ionic strength dependence of protein-protein interactions in terms of an interaction parameter kD obtained from dynamic light scattering and the osmotic second virial coefficient B22 measured by static light scattering. A simplified protein-protein interaction model based on a Baxter adhesive potential and an electric double layer force is used to separate out the contributions of longer-ranged electrostatic interactions from short-ranged attractive forces. The ionic strength dependence of protein-protein interactions for solutions at pH 6.5 and below can be accurately captured using a Deryaguin-Landau-Verwey-Overbeek (DLVO) potential to describe the double layer forces. In solutions at pH 9, attractive electrostatics occur over the ionic strength range of 5-275 mM. At intermediate pH values (7.25 to 8.5), there is a crossover effect characterized by a nonmonotonic ionic strength dependence of protein-protein interactions, which can be rationalized by the competing effects of long-ranged repulsive double layer forces at low ionic strength and a shorter ranged electrostatic attraction, which dominates above a critical ionic strength. The change of interactions from repulsive to attractive indicates a concomitant change in the angular dependence of protein-protein interaction from isotropic to anisotropic. In the second part of the paper, we show how the Baxter adhesive potential can be used to predict values of kD from fitting to B22 measurements, thus providing a molecular basis for the linear correlation between the two protein-protein interaction parameters.

  14. [Methods for analysis of protein-protein and protein-ligand interactions].

    PubMed

    Durech, M; Trčka, F; Vojtěšek, B; Müller, P

    2014-01-01

    In order to maintain cellular homeostasis, cellular proteins coexist in complex and variable molecular assemblies. Therefore, understanding of major physiological processes at molecular level is based on analysis of protein-protein interaction networks. Firstly, composition of the molecular assembly has to be qualitatively analyzed. In the next step, quantitative bio-chemical properties of the identified protein-protein interactions are determined. Detailed information about the protein-protein interaction interface can be obtained by crystallographic methods. Accordingly, the insight into the molecular architecture of these protein-protein complexes allows us to rationally design new synthetic compounds that specifically influence various physiological or pathological processes by targeted modulation of protein interactions. This review is focused on description of the most used methods applied in both qualitative and quantitative analysis of protein-protein interactions. Co- immunoprecipitation and affinity co- precipitation are basic methods designed for qualitative analysis of protein binding partners. Further bio-chemical analysis of the interaction requires definition of kinetic and thermodynamic parameters. Surface plasmon resonance (SPR) is used for description of affinity and kinetic profile of the interaction, fluorescence polarization (FP) method for fast determination of inhibition potential of inhibitors and isothermal titration calorimetry (ITC) for definition of thermodynamic parameters of the interaction (G, H and S). Besides the importance of uncovering the molecular basis of protein interactions for basic research, the same methodological approaches open new possibilities in rational design of novel therapeutic agents.

  15. Protein-protein interactions in intracellular Ca2+-release channel function.

    PubMed Central

    MacKrill, J J

    1999-01-01

    Release of Ca2+ ions from intracellular stores can occur via two classes of Ca2+-release channel (CRC) protein, the inositol 1,4, 5-trisphosphate receptors (InsP3Rs) and the ryanodine receptors (RyRs). Multiple isoforms and subtypes of each CRC class display distinct but overlapping distributions within mammalian tissues. InsP3Rs and RyRs interact with a plethora of accessory proteins which modulate the activity of their intrinsic channels. Although many aspects of CRC structure and function have been reviewed in recent years, the properties of proteins with which they interact has not been comprehensively surveyed, despite extensive current research on the roles of these modulators. The aim of this article is to review the regulation of CRC activity by accessory proteins and, wherever possible, to outline the structural details of such interactions. The CRCs are large transmembrane proteins, with the bulk of their structure located cytoplasmically. Intra- and inter-complex protein-protein interactions between these cytoplasmic domains also regulate CRC function. Some accessory proteins modulate channel activity of all CRC subtypes characterized, whereas other have class- or even isoform-specific effects. Certain accessory proteins exert both direct and indirect forms of regulation on CRCs, occasionally with opposing effects. Others are themselves modulated by changes in Ca2+ concentration, thereby participating in feedback mechanisms acting on InsP3R and RyR activity. CRCs are therefore capable of integrating numerous signalling events within a cell by virtue of such protein-protein interactions. Consequently, the functional properties of InsP3Rs and RyRs within particular cells and subcellular domains are 'customized' by the accessory proteins present. PMID:9895277

  16. Exploring NMR ensembles of calcium binding proteins: Perspectives to design inhibitors of protein-protein interactions

    PubMed Central

    2011-01-01

    Background Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding. Results In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces. Conclusions NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions. PMID:21569443

  17. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    PubMed

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  18. Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks.

    PubMed

    Cao, Renzhi; Cheng, Jianlin

    2016-01-15

    Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein-protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and new information such as spatial gene-gene interaction networks generated from chromosomal conformation data together to improve protein function prediction. In this work, we developed three different probabilistic scores (MIS, SEQ, and NET score) to combine protein sequence, function associations, and protein-protein interaction and spatial gene-gene interaction networks for protein function prediction. The MIS score is mainly generated from homologous proteins found by PSI-BLAST search, and also association rules between Gene Ontology terms, which are learned by mining the Swiss-Prot database. The SEQ score is generated from protein sequences. The NET score is generated from protein-protein interaction and spatial gene-gene interaction networks. These three scores were combined in a new Statistical Multiple Integrative Scoring System (SMISS) to predict protein function. We tested SMISS on the data set of 2011 Critical Assessment of Function Annotation (CAFA). The method performed substantially better than three base-line methods and an advanced method based on protein profile-sequence comparison, profile-profile comparison, and domain co-occurrence networks according to the maximum F-measure. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Prediction of protein-protein interaction sites by means of ensemble learning and weighted feature descriptor.

    PubMed

    Du, Xiuquan; Sun, Shiwei; Hu, Changlin; Li, Xinrui; Xia, Junfeng

    2016-05-01

    Reliable prediction of protein-protein interaction sites is an important goal in the field of bioinformatics. Many computational methods have been explored for the large-scale prediction of protein-protein interaction sites based on various data types, including protein sequence, structural and genomic data. Although much progress has been achieved in recent years, the problem has not yet been satisfactorily solved. In this work, we presented an efficient approach that uses ensemble learning algorithm with weighted feature descriptor (EL-WFD) to predict protein-protein interaction sites. Moreover, weighted feature descriptor was designed to describe the distance influence of neighboring residues on interaction sites. The results on two dataset (Hetero and Homo), show that the proposed method yields a satisfactory accuracy with 83.8 % recall and 96.3 % precision on the Hetero dataset and 84.2 % recall and 96.3 % precision on the Homo dataset, respectively. In both datasets, our method tend to obtain high Mathews correlation coefficient compared with state-of-the-art technique random forest method. The experimental results show that the EL-WFD method is quite effective in predicting protein-protein interaction sites. The novel weighted feature descriptor was proved to be promising in discovering interaction sites. Overall, the proposed method can be considered as a new powerful tool for predicting protein-protein interaction sites with excellence performance.

  20. A credit-card library approach for disrupting protein-protein interactions.

    PubMed

    Xu, Yang; Shi, Jin; Yamamoto, Noboru; Moss, Jason A; Vogt, Peter K; Janda, Kim D

    2006-04-15

    Protein-protein interfaces are prominent in many therapeutically important targets. Using small organic molecules to disrupt protein-protein interactions is a current challenge in chemical biology. An important example of protein-protein interactions is provided by the Myc protein, which is frequently deregulated in human cancers. Myc belongs to the family of basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factors. It is biologically active only as heterodimer with the bHLH-ZIP protein Max. Herein, we report a new strategy for the disruption of protein-protein interactions that has been corroborated through the design and synthesis of a small parallel library composed of 'credit-card' compounds. These compounds are derived from a planar, aromatic scaffold and functionalized with four points of diversity. From a 285 membered library, several hits were obtained that disrupted the c-Myc-Max interaction and cellular functions of c-Myc. The IC50 values determined for this small focused library for the disruption of Myc-Max dimerization are quite potent, especially since small molecule antagonists of protein-protein interactions are notoriously difficult to find. Furthermore, several of the compounds were active at the cellular level as shown by their biological effects on Myc action in chicken embryo fibroblast assays. In light of our findings, this approach is considered a valuable addition to the armamentarium of new molecules being developed to interact with protein-protein interfaces. Finally, this strategy for disrupting protein-protein interactions should prove applicable to other families of proteins.

  1. Glutathione-S-transferase (GST)-fusion based assays for studying protein-protein interactions.

    PubMed

    Vikis, Haris G; Guan, Kun-Liang

    2015-01-01

    Glutathione-S-transferase (GST)-fusion proteins have become an effective reagent to use in the study of protein-protein interactions. GST-fusion proteins can be produced in bacterial and mammalian cells in large quantities and purified rapidly. GST can be coupled to a glutathione matrix, which permits its use as an effective affinity column to study interactions in vitro or to purify protein complexes in cells expressing the GST-fusion protein. Here, we provide a technical description of the utilization of GST-fusion proteins as both a tool to study protein-protein interactions and also as a means to purify interacting proteins.

  2. MCLIP Detection of Novel Protein-Protein Interactions at the Nuclear Envelope.

    PubMed

    Jafferali, Mohammed Hakim; Figueroa, Ricardo A; Hallberg, Einar

    2016-01-01

    The organization and function of the nuclear envelope (NE) involves hundreds of nuclear membrane proteins and myriad protein-protein interactions, most of which are still uncharacterized. Many NE proteins interact stably or dynamically with the nuclear lamina or chromosomes. This can make them difficult to extract under nondenaturing conditions, and greatly limits our ability to explore and identify functional protein interactions at the NE. This knowledge is needed to understand nuclear envelope structure and the mechanisms of human laminopathy diseases. This chapter provides detailed protocols for MCLIP (membrane cross-linking immunoprecipitation) identification of novel protein-protein interactions in mammalian cells.

  3. Discovering patterns to extract protein-protein interactions from full texts.

    PubMed

    Huang, Minlie; Zhu, Xiaoyan; Hao, Yu; Payan, Donald G; Qu, Kunbin; Li, Ming

    2004-12-12

    Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. The program is available on request from the authors.

  4. Experimental and bioinformatic approaches for interrogating protein-protein interactions to determine protein function.

    PubMed

    Droit, Arnaud; Poirier, Guy G; Hunter, Joanna M

    2005-04-01

    An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein-protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein-protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein-protein interactions.

  5. Role of protein-protein interactions in cytochrome P450-mediated drug metabolism and toxicity.

    PubMed

    Kandel, Sylvie E; Lampe, Jed N

    2014-09-15

    Through their unique oxidative chemistry, cytochrome P450 monooxygenases (CYPs) catalyze the elimination of most drugs and toxins from the human body. Protein-protein interactions play a critical role in this process. Historically, the study of CYP-protein interactions has focused on their electron transfer partners and allosteric mediators, cytochrome P450 reductase and cytochrome b5. However, CYPs can bind other proteins that also affect CYP function. Some examples include the progesterone receptor membrane component 1, damage resistance protein 1, human and bovine serum albumin, and intestinal fatty acid binding protein, in addition to other CYP isoforms. Furthermore, disruption of these interactions can lead to altered paths of metabolism and the production of toxic metabolites. In this review, we summarize the available evidence for CYP protein-protein interactions from the literature and offer a discussion of the potential impact of future studies aimed at characterizing noncanonical protein-protein interactions with CYP enzymes.

  6. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?

    PubMed Central

    Ekman, Diana; Light, Sara; Björklund, Åsa K; Elofsson, Arne

    2006-01-01

    Background Most proteins interact with only a few other proteins while a small number of proteins (hubs) have many interaction partners. Hub proteins and non-hub proteins differ in several respects; however, understanding is not complete about what properties characterize the hubs and set them apart from proteins of low connectivity. Therefore, we have investigated what differentiates hubs from non-hubs and static hubs (party hubs) from dynamic hubs (date hubs) in the protein-protein interaction network of Saccharomyces cerevisiae. Results The many interactions of hub proteins can only partly be explained by bindings to similar proteins or domains. It is evident that domain repeats, which are associated with binding, are enriched in hubs. Moreover, there is an over representation of multi-domain proteins and long proteins among the hubs. In addition, there are clear differences between party hubs and date hubs. Fewer of the party hubs contain long disordered regions compared to date hubs, indicating that these regions are important for flexible binding but less so for static interactions. Furthermore, party hubs interact to a large extent with each other, supporting the idea of party hubs as the cores of highly clustered functional modules. In addition, hub proteins, and in particular party hubs, are more often ancient. Finally, the more recent paralogs of party hubs are underrepresented. Conclusion Our results indicate that multiple and repeated domains are enriched in hub proteins and, further, that long disordered regions, which are common in date hubs, are particularly important for flexible binding. PMID:16780599

  7. Structural principles within the human-virus protein-protein interaction network

    PubMed Central

    Franzosa, Eric A.; Xia, Yu

    2011-01-01

    General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

  8. Development of a multiplexed microfluidic proteomic reactor and its application for studying protein-protein interactions.

    PubMed

    Tian, Ruijun; Hoa, Xuyen Dai; Lambert, Jean-Philippe; Pezacki, John Paul; Veres, Teodor; Figeys, Daniel

    2011-06-01

    Mass spectrometry-based proteomics techniques have been very successful for the identification and study of protein-protein interactions. Typically, immunopurification of protein complexes is conducted, followed by protein separation by gel electrophoresis and in-gel protein digestion, and finally, mass spectrometry is performed to identify the interacting partners. However, the manual processing of the samples is time-consuming and error-prone. Here, we developed a polymer-based microfluidic proteomic reactor aimed at the parallel analysis of minute amounts of protein samples obtained from immunoprecipitation. The design of the proteomic reactor allows for the simultaneous processing of multiple samples on the same devices. Each proteomic reactor on the device consists of SCX beads packed and restricted into a 1 cm microchannel by two integrated pillar frits. The device is fabricated using a combination of low-cost hard cyclic olefin copolymer thermoplastic and elastomeric thermoplastic materials (styrene/(ethylene/butylenes)/styrene) using rapid hot-embossing replication techniques with a polymer-based stamp. Three immunopurified protein samples are simultaneously captured, reduced, alkylated, and digested on the device within 2-3 h instead of the days required for the conventional protein-protein interaction studies. The limit of detection of the microfluidic proteomic reactor was shown to be lower than 2 ng of protein. Furthermore, the application of the microfluidic proteomic reactor was demonstrated for the simultaneous processing of the interactome of the histone variant Htz1 in wild-type yeast and in a swr1Δ yeast strain compared to an untagged control using a novel three-channel microfluidic proteomic reactor.

  9. A protein-protein interaction dictates Borrelial infectivity.

    PubMed

    Thakur, Meghna; Sharma, Kavita; Chao, Kinlin; Smith, Alexis A; Herzberg, Osnat; Pal, Utpal

    2017-06-07

    Two Borrelia burgdorferi interacting proteins, BB0238 and BB0323, play distinct roles in pathogen biology and infectivity although a significance of their interaction remained enigmatic. Here we identified the polypeptide segment essential for BB0238-BB0323 interaction and examined how it supports spirochete infectivity. We show that the interaction region in BB0323 requires amino acid residues 22-200, suggesting that the binding encompasses discontinuous protein segments. In contrast, the interaction region in BB0238 spans only 11 amino acids, residues 120-130. A deletion of these 11 amino acids neither alters the overall secondary structure of the protein, nor affects its stability or oligomerization property, however, it reduces the post-translational stability of the binding partner, BB0323. Mutant B. burgdorferi isolates producing BB0238 lacking the 11-amino acid interaction region were able to persist in ticks but failed to transmit to mice or to establish infection. These results suggest that BB0238-BB0323 interaction is critical for post-translational stability of BB0323, and that this interaction is important for mammalian infectivity and transmission of B. burgdorferi. We show that saturation or inhibition of BB0238-BB0323 interaction could be studied in a luciferase assay, which could be amenable for future identification of small molecule inhibitors to combat B. burgdorferi infection.

  10. Small Molecule Inhibitors to Disrupt Protein-protein Interactions of Heat Shock Protein 90 Chaperone Machinery.

    PubMed

    Seo, Young Ho

    2015-03-01

    Heat shock protein 90 (Hsp90) is an adenosine triphosphate dependent molecular chaperone in eukaryotic cells that regulates the activation and maintenance of numerous regulatory and signaling proteins including epidermal growth factor receptor, human epidermal growth factor receptor 2, mesenchymal-epithelial transition factor, cyclin-dependent kinase-4, protein kinase B, hypoxia-inducible factor 1α, and matrix metalloproteinase-2. Since many of Hsp90 clients are oncogenic proteins, Hsp90 has become an attractive therapeutic target for treatment of cancer. To discover small molecule inhibitors targeting Hsp90 chaperone machinery, several strategies have been employed, which results in three classes of inhibitors such as N-terminal inhibitors, C-terminal inhibitors, and inhibitors disrupting protein-protein interactions of Hsp90 chaperone machinery. Developing small molecule inhibitors that modulate protein-protein interactions of Hsp90 is a challenging task, although it offers many alternative opportunities for therapeutic intervention. The lack of well-defined binding pocket and starting points for drug design challenges medicinal chemists to discover small molecule inhibitors disrupting protein-protein interactions of Hsp90. The present review will focus on the current studies on small molecule inhibitors disrupting protein-protein interactions of Hsp90 chaperone machinery, provide biological background on the structure, function and mechanism of Hsp90's protein-protein interactions, and discuss the challenges and promise of its small molecule modulations.

  11. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.

    PubMed

    Zahiri, Javad; Yaghoubi, Omid; Mohammad-Noori, Morteza; Ebrahimpour, Reza; Masoudi-Nejad, Ali

    2013-10-01

    Protein-protein interactions regulate a variety of cellular processes. There is a great need for computational methods as a complement to experimental methods with which to predict protein interactions due to the existence of many limitations involved in experimental techniques. Here, we introduce a novel evolutionary based feature extraction algorithm for protein-protein interaction (PPI) prediction. The algorithm is called PPIevo and extracts the evolutionary feature from Position-Specific Scoring Matrix (PSSM) of protein with known sequence. The algorithm does not depend on the protein annotations, and the features are based on the evolutionary history of the proteins. This enables the algorithm to have more power for predicting protein-protein interaction than many sequence based algorithms. Results on the HPRD database show better performance and robustness of the proposed method. They also reveal that the negative dataset selection could lead to an acute performance overestimation which is the principal drawback of the available methods.

  12. Proteins interacting with cloning scars: a source of false positive protein-protein interactions.

    PubMed

    Banks, Charles A S; Boanca, Gina; Lee, Zachary T; Florens, Laurence; Washburn, Michael P

    2015-02-23

    A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

  13. History of protein-protein interactions: from egg-white to complex networks.

    PubMed

    Braun, Pascal; Gingras, Anne-Claude

    2012-05-01

    Today, it is widely appreciated that protein-protein interactions play a fundamental role in biological processes. This was not always the case. The study of protein interactions started slowly and evolved considerably, together with conceptual and technological progress in different areas of research through the late 19th and the 20th centuries. In this review, we present some of the key experiments that have introduced major conceptual advances in biochemistry and molecular biology, and review technological breakthroughs that have paved the way for today's systems-wide approaches to protein-protein interaction analysis. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Pharmacological interference with protein-protein interactions mediated by coiled-coil motifs.

    PubMed

    Strauss, H M; Keller, S

    2008-01-01

    , and a given protein can participate in multiple assembly-disassembly equilibria among several coiled coils differing in stoichiometry and topology (Portwich et al. 2007). Protein complexes whose oligomeric quaternary structures - and, hence, biological activities - depend on coiled-coil interactions include transcription factors, tRNA synthetases (Biou et al. 1994; Cusack et al. 1990), cytoskeletal and signal-transduction proteins, enzyme complexes, proteins involved in vesicular trafficking, viral coat proteins, and membrane proteins (Langosch and Heringa 1998). It is thus not surprising that coiled-coil motifs have gained great attention as potential targets for modulating protein-protein interactions implicated in a large number of diseases. In this review, we will first discuss some fundamental functional and structural aspects of a simple and well-characterized representative of coiled-coil transcription factors (Sect. 1) before considering two more complex coiled coils found in scaffolding proteins involved in mitosis and meiosis and vesicular trafficking Sect. 2). This will set the stage for addressing the role of coiled coils in viral infection (Sect. 3) as well as strategies of interfering with such protein-protein interactions therapeutically (Sect. 4 and 5).

  15. Developing algorithms for predicting protein-protein interactions of homology modeled proteins.

    SciTech Connect

    Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel; Roe, Diana C.

    2006-01-01

    The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.

  16. Protein-protein interactions in complex cosolvent solutions.

    PubMed

    Javid, Nadeem; Vogtt, Karsten; Krywka, Chris; Tolan, Metin; Winter, Roland

    2007-04-02

    The effects of various kosmotropic and chaotropic cosolvents and salts on the intermolecular interaction potential of positively charged lysozyme is evaluated at varying protein concentrations by using synchrotron small-angle X-ray scattering in combination with liquid-state theoretical approaches. The experimentally derived static structure factors S(Q) obtained without and with added cosolvents and salts are analysed with a statistical mechanical model based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential, which accounts for repulsive and attractive interactions between the protein molecules. Different cosolvents and salts influence the interactions between protein molecules differently as a result of changes in the hydration level or solvation, in charge screening, specific adsorption of the additives at the protein surface, or increased hydrophobic interactions. Intermolecular interaction effects are significant above protein concentrations of 1 wt %, and with increasing protein concentration, the repulsive nature of the intermolecular pair potential V(r) increases markedly. Kosmotropic cosolvents like glycerol and sucrose exhibit strong concentration-dependent effects on the interaction potential, leading to an increase of repulsive forces between the protein molecules at low to medium high osmolyte concentrations. Addition of trifluoroethanol exhibits a multiphasic effect on V(r) when changing its concentration. Salts like sodium chloride and potassium sulfate exhibit strong concentration-dependent changes of the interaction potential due to charge screening of the positively charged protein molecules. Guanidinium chloride (GdmCl) at low concentrations exhibits a similar charge-screening effect, resulting in increased attractive interactions between the protein molecules. At higher GdmCl concentrations, V(r) becomes more repulsive in nature due to the presence of high concentrations of Gdm(+) ions binding to the protein molecules. Our findings also

  17. Integrating protein-protein interaction networks with phenotypes reveals signs of interactions

    PubMed Central

    Vinayagam, Arunachalam; Zirin, Jonathan; Roesel, Charles; Hu, Yanhui; Yilmazel, Bahar; Samsonova, Anastasia A.; Neumüller, Ralph A.; Mohr, Stephanie E.; Perrimon, Norbert

    2013-01-01

    A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease. PMID:24240319

  18. Regulation of PXR and CAR by protein-protein interaction and signaling crosstalk

    PubMed Central

    Oladimeji, Peter; Cui, Hongmei; Zhang, Chen; Chen, Taosheng

    2016-01-01

    Introduction Protein-protein interaction and signaling crosstalk contribute to the regulation of pregnane X receptor (PXR) and constitutive androstane receptor (CAR) and broaden their cellular function. Area covered This review covers key historic discoveries and recent advances in our understanding of the broad function of PXR and CAR and their regulation by protein-protein interaction and signaling crosstalk. Expert opinion PXR and CAR were first discovered as xenobiotic receptors. However, it is clear that PXR and CAR perform a much broader range of cellular functions through protein-protein interaction and signaling crosstalk, which typically mutually affect the function of all the partners involved. Future research on PXR and CAR should, therefore, look beyond their xenobiotic function. PMID:27295009

  19. Inhibition of Protein-Protein Interactions and Signaling by Small Molecules

    NASA Astrophysics Data System (ADS)

    Freire, Ernesto

    2010-03-01

    Protein-protein interactions are at the core of cell signaling pathways as well as many bacterial and viral infection processes. As such, they define critical targets for drug development against diseases such as cancer, arthritis, obesity, AIDS and many others. Until now, the clinical inhibition of protein-protein interactions and signaling has been accomplished with the use of antibodies or soluble versions of receptor molecules. Small molecule replacements of these therapeutic agents have been extremely difficult to develop; either the necessary potency has been hard to achieve or the expected biological effect has not been obtained. In this presentation, we show that a rigorous thermodynamic approach that combines differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC) provides a unique platform for the identification and optimization of small molecular weight inhibitors of protein-protein interactions. Recent advances in the development of cell entry inhibitors of HIV-1 using this approach will be discussed.

  20. AAV Vectors for FRET-Based Analysis of Protein-Protein Interactions in Photoreceptor Outer Segments

    PubMed Central

    Becirovic, Elvir; Böhm, Sybille; Nguyen, Ong N. P.; Riedmayr, Lisa M.; Hammelmann, Verena; Schön, Christian; Butz, Elisabeth S.; Wahl-Schott, Christian; Biel, Martin; Michalakis, Stylianos

    2016-01-01

    Fluorescence resonance energy transfer (FRET) is a powerful method for the detection and quantification of stationary and dynamic protein-protein interactions. Technical limitations have hampered systematic in vivo FRET experiments to study protein-protein interactions in their native environment. Here, we describe a rapid and robust protocol that combines adeno-associated virus (AAV) vector-mediated in vivo delivery of genetically encoded FRET partners with ex vivo FRET measurements. The method was established on acutely isolated outer segments of murine rod and cone photoreceptors and relies on the high co-transduction efficiency of retinal photoreceptors by co-delivered AAV vectors. The procedure can be used for the systematic analysis of protein-protein interactions of wild type or mutant outer segment proteins in their native environment. Conclusively, our protocol can help to characterize the physiological and pathophysiological relevance of photoreceptor specific proteins and, in principle, should also be transferable to other cell types. PMID:27516733

  1. Mapping protein-protein interactions by localized oxidation: consequences of the reach of hydroxyl radical.

    PubMed

    Cheal, Sarah M; Ng, Mindy; Barrios, Brianda; Miao, Zheng; Kalani, Amir K; Meares, Claude F

    2009-06-02

    Hydroxyl radicals generated from a variety of methods, including not only synchrotron radiation but also Fenton reactions involving chelated iron, have become an accepted macromolecular footprinting tool. Hydroxyl radicals react with proteins via multiple mechanisms that lead to both polypeptide backbone cleavage events and side chain modifications (e.g., hydroxylation and carbonyl formation). The use of site-specifically tethered iron chelates can reveal protein-protein interactions, but the interpretation of such experiments will be strengthened by improving our understanding of how hydroxyl radicals produced at a point on a protein react with other protein sites. We have developed methods for monitoring carbonyl formation on proteins as a function of distance from a hydroxyl generator, iron-(S)-1-[p-(bromoacetamido)benzyl]EDTA (FeBABE), conjugated to an engineered cysteine residue. After activation of the chelated iron with ascorbate and peroxide produces new protein carbonyl groups, their positions can be identified using element-coded affinity tagging (ECAT), with carbonyl-specific tags {e.g., rare earth chelates of (S)-2-[4-(2-aminooxy)acetamidobenzyl]-1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid (AOD)} that allow for affinity purification, identification, and relative quantitation of oxidation sites using mass spectrometry. Intraprotein oxidation of single-cysteine mutants of Escherichia coli sigma(70) by tethered FeBABE was used to calibrate the reach of hydroxyl radical by comparison to the crystal structure; the application to protein-protein interactions was demonstrated using the same sigma(70) FeBABE conjugates in complexes with the RNA polymerase core enzyme. The results provide fundamental information for interpreting protein footprinting experiments in other systems.

  2. Mapping Protein-Protein Interactions by Localized Oxidation: Consequences of the Reach of Hydroxyl Radical†

    PubMed Central

    Cheal, Sarah M.; Ng, Mindy; Barrios, Brianda; Miao, Zheng; Kalani, Amir K.; Meares, Claude F.

    2009-01-01

    Hydroxyl radicals generated from a variety of methods, including not only synchrotron radiation but also Fenton reactions involving chelated iron, have become an accepted macromolecular footprinting tool. Hydroxyl radicals react with proteins via multiple mechanisms that lead to both polypeptide backbone cleavage events and side chain modifications (e.g., hydroxylation and carbonyl formation). The use of site-specifically tethered iron chelates can reveal protein-protein interactions, but the interpretation of such experiments will be strengthened by better understanding of how hydroxyl radicals produced at a point on a protein react with other protein sites. We have developed methods to monitor carbonyl formation on proteins as a function of distance from a hydroxyl generator, iron-(S)-1-[p-(bromoacetamido)benzyl]EDTA, “FeBABE”, conjugated to an engineered cysteine residue. After activation of the chelated iron with ascorbate and peroxide produces new protein carbonyl groups, their positions can be identified using element-coded affinity tagging (ECAT), with carbonyl-specific tags (e.g., rare earth chelates of ((S)-2-(4-(2-aminooxy)acetamidobenzyl)-1, 4, 7, 10-tetraazacyclododecane-N, N′, N″, N‴-tetraacetic acid, “AOD”) that allow for affinity purification, identification, and relative quantitation of oxidation sites using mass spectrometry. Intraprotein oxidation of single-cysteine mutants of E. coli σ70 by tethered FeBABE was used to calibrate the reach of hydroxyl radical by comparison to the crystal structure; the application to protein-protein interactions was demonstrated using the same σ70 FeBABE conjugates in complexes with RNA polymerase core enzyme. The results provide fundamental information for interpreting protein footprinting experiments in other systems. PMID:19354299

  3. Inferring homologous protein-protein interactions through pair position specific scoring matrix

    PubMed Central

    2013-01-01

    Background The protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species. Results In this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli). Conclusions Our pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species. PMID:23367879

  4. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces.

    PubMed

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

    The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

  5. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

    NASA Astrophysics Data System (ADS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

    The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

  6. What Evidence Is There for the Homology of Protein-Protein Interactions?

    PubMed Central

    Lewis, Anna C. F.; Jones, Nick S.; Porter, Mason A.; Deane, Charlotte M.

    2012-01-01

    The notion that sequence homology implies functional similarity underlies much of computational biology. In the case of protein-protein interactions, an interaction can be inferred between two proteins on the basis that sequence-similar proteins have been observed to interact. The use of transferred interactions is common, but the legitimacy of such inferred interactions is not clear. Here we investigate transferred interactions and whether data incompleteness explains the lack of evidence found for them. Using definitions of homology associated with functional annotation transfer, we estimate that conservation rates of interactions are low even after taking interactome incompleteness into account. For example, at a blastp -value threshold of , we estimate the conservation rate to be about between S. cerevisiae and H. sapiens. Our method also produces estimates of interactome sizes (which are similar to those previously proposed). Using our estimates of interaction conservation we estimate the rate at which protein-protein interactions are lost across species. To our knowledge, this is the first such study based on large-scale data. Previous work has suggested that interactions transferred within species are more reliable than interactions transferred across species. By controlling for factors that are specific to within-species interaction prediction, we propose that the transfer of interactions within species might be less reliable than transfers between species. Protein-protein interactions appear to be very rarely conserved unless very high sequence similarity is observed. Consequently, inferred interactions should be used with care. PMID:23028270

  7. Characterization and modeling of protein protein interaction networks

    NASA Astrophysics Data System (ADS)

    Colizza, Vittoria; Flammini, Alessandro; Maritan, Amos; Vespignani, Alessandro

    2005-07-01

    The recent availability of high-throughput gene expression and proteomics techniques has created an unprecedented opportunity for a comprehensive study of the structure and dynamics of many biological networks. Global proteomic interaction data, in particular, are synthetically represented as undirected networks exhibiting features far from the random paradigm which has dominated past effort in network theory. This evidence, along with the advances in the theory of complex networks, has triggered an intense research activity aimed at exploiting the evolutionary and biological significance of the resulting network's topology. Here we present a review of the results obtained in the characterization and modeling of the yeast Saccharomyces Cerevisiae protein interaction networks obtained with different experimental techniques. We provide a comparative assessment of the topological properties and discuss possible biases in interaction networks obtained with different techniques. We report on dynamical models based on duplication mechanisms that cast the protein interaction networks in the family of dynamically growing complex networks. Finally, we discuss various results and analysis correlating the networks’ topology with the biological function of proteins.

  8. Protein- protein interaction detection system using fluorescent protein microdomains

    DOEpatents

    Waldo, Geoffrey S.; Cabantous, Stephanie

    2010-02-23

    The invention provides a protein labeling and interaction detection system based on engineered fragments of fluorescent and chromophoric proteins that require fused interacting polypeptides to drive the association of the fragments, and further are soluble and stable, and do not change the solubility of polypeptides to which they are fused. In one embodiment, a test protein X is fused to a sixteen amino acid fragment of GFP (.beta.-strand 10, amino acids 198-214), engineered to not perturb fusion protein solubility. A second test protein Y is fused to a sixteen amino acid fragment of GFP (.beta.-strand 11, amino acids 215-230), engineered to not perturb fusion protein solubility. When X and Y interact, they bring the GFP strands into proximity, and are detected by complementation with a third GFP fragment consisting of GFP amino acids 1-198 (strands 1-9). When GFP strands 10 and 11 are held together by interaction of protein X and Y, they spontaneous association with GFP strands 1-9, resulting in structural complementation, folding, and concomitant GFP fluorescence.

  9. Protein-protein interaction networks in the spinocerebellar ataxias

    PubMed Central

    Rubinsztein, David C

    2006-01-01

    A large yeast two-hybrid study investigating whether the proteins mutated in different forms of spinocerebellar ataxia have interacting protein partners in common suggests that some forms do share common pathways, and will provide a valuable resource for future work on these diseases. PMID:16904001

  10. A novel method for protein-protein interaction site prediction using phylogenetic substitution models

    PubMed Central

    La, David; Kihara, Daisuke

    2011-01-01

    Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. Based on this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and non-binding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins. PMID:21989996

  11. An ontology-based search engine for protein-protein interactions

    PubMed Central

    2010-01-01

    Background Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. Results We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Conclusion Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology. PMID:20122195

  12. An ontology-based search engine for protein-protein interactions.

    PubMed

    Park, Byungkyu; Han, Kyungsook

    2010-01-18

    Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

  13. Deciphering peculiar protein-protein interacting modules in Deinococcus radiodurans

    PubMed Central

    Mezhoud, Karim; Sghaier, Haïtham; Barkallah, Insaf

    2009-01-01

    Interactomes of proteins under positive selection from ionizing-radiation-resistant bacteria (IRRB) might be a part of the answer to the question as to how IRRB, particularly Deinococcus radiodurans R1 (Deira), resist ionizing radiation. Here, using the Database of Interacting Proteins (DIP) and the Protein Structural Interactome (PSI)-base server for PSI map, we have predicted novel interactions of orthologs of the 58 proteins under positive selection in Deira and other IRRB, but which are absent in IRSB. Among these, 18 domains and their interactomes have been identified in DNA checkpoint and repair; kinases pathways; energy and nucleotide metabolisms were the important biological processes that were found to be involved. This finding provides new clues to the cellular pathways that can to be important for ionizing-radiation resistance in Deira. PMID:19356244

  14. Detection of protein-protein interactions using tandem affinity purification.

    PubMed

    Goodfellow, Ian; Bailey, Dalan

    2014-01-01

    Tandem affinity purification (TAP) is an invaluable technique for identifying interaction partners for an affinity tagged bait protein. The approach relies on the fusion of dual tags to the bait before separate rounds of affinity purification and precipitation. Frequently two specific elution steps are also performed to increase the specificity of the overall technique. In the method detailed here, the two tags used are protein G and a short streptavidin binding peptide; however, many variations can be employed. In our example the tags are separated by a cleavable tobacco etch virus protease target sequence, allowing for specific elution after the first round of affinity purification. Proteins isolated after the final elution step in this process are concentrated before being identified by mass spectrometry. The use of dual affinity tags and specific elution in this technique dramatically increases both the specificity and stringency of the pull-downs, ensuring a low level of background nonspecific interactions.

  15. TIMBAL v2: update of a database holding small molecules modulating protein-protein interactions.

    PubMed

    Higueruelo, Alicia P; Jubb, Harry; Blundell, Tom L

    2013-01-01

    TIMBAL is a database holding molecules of molecular weight <1200 Daltons that modulate protein-protein interactions. Since its first release, the database has been extended to cover 50 known protein-protein interactions drug targets, including protein complexes that can be stabilized by small molecules with therapeutic effect. The resource contains 14 890 data points for 6896 distinct small molecules. UniProt codes and Protein Data Bank entries are also included. Database URL: http://www-cryst.bioc.cam.ac.uk/timbal

  16. The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

    PubMed Central

    Rattei, Thomas; Makse, Hernán A.

    2013-01-01

    Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI

  17. The fragility of protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Schneider, C. M.; Andrade, R. F. S.; Shinbrot, T.; Herrmann, H. J.

    2011-07-01

    The capacity to resist perturbations from the environment is crucial to the survival of all organisms. We quantitatively analyze the susceptibility of protein interaction networks of numerous organisms to random and targeted failures. We find for all organisms studied that random rewiring improves protein network robustness, so that actual networks are more fragile than rewired surrogates. This unexpected fragility contrasts with the behavior of networks such as the Internet, whose robustness decreases with random rewiring. We trace this surprising effect to the modular structure of protein networks.

  18. Inhibition of protein-protein interactions with low molecular weight compounds

    PubMed Central

    Matthews, Marilyn M.; Weber, David J.; Shapiro, Paul S.; Coop, Andrew; MacKerell, Alexander D.

    2010-01-01

    An overview of issues associated with the design and development of low molecular weight inhibitors of protein-protein interactions is presented. Areas discussed include information on the nature of protein-protein interfaces, methods to characterize those interfaces and methods by which that information is applied towards ligand identification and design. Specific examples of the strategy for the identification of inhibitors of protein-protein interactions involving the proteins p56lck kinase, ERK2 and the calcium-binding protein S100B are presented. Physical characterization of the inhibitors identified in those studies shows them to have drug-like and lead-like properties, indicating their potential to be developed into therapeutic agents. PMID:21927717

  19. Protein-protein interactions and protein modules in the control of neurotransmitter release.

    PubMed Central

    Benfenati, F; Onofri, F; Giovedí, S

    1999-01-01

    Information transfer among neurons is operated by neurotransmitters stored in synaptic vesicles and released to the extracellular space by an efficient process of regulated exocytosis. Synaptic vesicles are organized into two distinct functional pools, a large reserve pool in which vesicles are restrained by the actin-based cytoskeleton, and a quantitatively smaller releasable pool in which vesicles approach the presynaptic membrane and eventually fuse with it on stimulation. Both synaptic vesicle trafficking and neurotransmitter release depend on a precise sequence of events that include release from the reserve pool, targeting to the active zone, docking, priming, fusion and endocytotic retrieval of synaptic vesicles. These steps are mediated by a series of specific interactions among cytoskeletal, synaptic vesicle, presynaptic membrane and cytosolic proteins that, by acting in concert, promote the spatial and temporal regulation of the exocytotic machinery. The majority of these interactions are mediated by specific protein modules and domains that are found in many proteins and are involved in numerous intracellular processes. In this paper, the possible physiological role of these multiple protein-protein interactions is analysed, with ensuing updating and clarification of the present molecular model of the process of neurotransmitter release. PMID:10212473

  20. Quantification of the Influence of Protein-Protein Interactions on Adsorbed Protein Structure and Bioactivity

    PubMed Central

    Wei, Yang; Thyparambil, Aby A.; Latour, Robert A.

    2013-01-01

    While protein-surface interactions have been widely studied, relatively little is understood at this time regarding how protein-surface interaction effects are influenced by protein-protein interactions and how these effects combine with the internal stability of a protein to influence its adsorbed-state structure and bioactivity. The objectives of this study were to develop a method to study these combined effects under widely varying protein-protein interaction conditions using hen egg-white lysozyme (HEWL) adsorbed on silica glass, poly(methyl methacrylate), and polyethylene as our model systems. In order to vary protein-protein interaction effects over a wide range, HEWL was first adsorbed to each surface type under widely varying protein solution concentrations for 2 h to saturate the surface, followed by immersion in pure buffer solution for 15 h to equilibrate the adsorbed protein layers in the absence of additionally adsorbing protein. Periodic measurements were made at selected time points of the areal density of the adsorbed protein layer as an indicator of the level of protein-protein interaction effects within the layer, and these values were then correlated with measurements of the adsorbed protein’s secondary structure and bioactivity. The results from these studies indicate that protein-protein interaction effects help stabilize the structure of HEWL adsorbed on silica glass, have little influence on the structural behavior of HEWL on HDPE, and actually serve to destabilize HEWL’s structure on PMMA. The bioactivity of HEWL on silica glass and HDPE was found to decrease in direct proportion to the degree of adsorption-induce protein unfolding. A direct correlation between bioactivity and the conformational state of adsorbed HEWL was less apparent on PMMA, thus suggesting that other factors influenced HEWL’s bioactivity on this surface, such as the accessibility of HEWL’s bioactive site being blocked by neighboring proteins or the surface

  1. Diversity in protein-protein interactions of connexins: emerging roles.

    PubMed

    Hervé, Jean-Claude; Bourmeyster, Nicolas; Sarrouilhe, Denis

    2004-03-23

    Gap junctions, specialised membrane structures that mediate cell-to-cell communication in almost all tissues, are composed of channel-forming integral membrane proteins termed connexins. The activity of these intercellular channels is closely regulated, particularly by intramolecular modifications as phosphorylations of proteins by protein kinases, which appear to regulate the gap junction at several levels, including assembly of channels in the plasma membrane, connexin turnover as well as directly affecting the opening and closure ("gating") of channels. The regulation of membrane channels by protein phosphorylation/dephosphorylation processes commonly requires the formation of a multiprotein complex, where pore-forming subunits bind to auxiliary proteins (e.g. scaffolding proteins, catalytic and regulatory subunits), that play essential roles in channel localisation and activity, linking signalling enzymes, substrates and effectors into a structure frequently anchored to the cytoskeleton. The present review summarises the up-to-date progress regarding the proteins capable of interacting or at least of co-localising with connexins and their functional importance.

  2. Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

    PubMed

    Zhang, Shu-Bo; Tang, Qiang-Rong

    2016-07-21

    Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction.

  3. Evolutionary diversification of protein-protein interactions by interface add-ons.

    PubMed

    Plach, Maximilian G; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H; Merkl, Rainer; Sterner, Reinhard

    2017-09-18

    Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein-protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein-protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein-protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein-protein interactions.

  4. Protein/Protein Interactions in the Mammalian Heme Degradation Pathway

    PubMed Central

    Spencer, Andrea L. M.; Bagai, Ireena; Becker, Donald F.; Zuiderweg, Erik R. P.; Ragsdale, Stephen W.

    2014-01-01

    Heme oxygenase (HO) catalyzes the rate-limiting step in the O2-dependent degradation of heme to biliverdin, CO, and iron with electrons delivered from NADPH via cytochrome P450 reductase (CPR). Biliverdin reductase (BVR) then catalyzes conversion of biliverdin to bilirubin. We describe mutagenesis combined with kinetic, spectroscopic (fluorescence and NMR), surface plasmon resonance, cross-linking, gel filtration, and analytical ultracentrifugation studies aimed at evaluating interactions of HO-2 with CPR and BVR. Based on these results, we propose a model in which HO-2 and CPR form a dynamic ensemble of complex(es) that precede formation of the productive electron transfer complex. The 1H-15N TROSY NMR spectrum of HO-2 reveals specific residues, including Leu-201, near the heme face of HO-2 that are affected by the addition of CPR, implicating these residues at the HO/CPR interface. Alanine substitutions at HO-2 residues Leu-201 and Lys-169 cause a respective 3- and 22-fold increase in Km values for CPR, consistent with a role for these residues in CPR binding. Sedimentation velocity experiments confirm the transient nature of the HO-2·CPR complex (Kd = 15.1 μm). Our results also indicate that HO-2 and BVR form a very weak complex that is only captured by cross-linking. For example, under conditions where CPR affects the 1H-15N TROSY NMR spectrum of HO-2, BVR has no effect. Fluorescence quenching experiments also suggest that BVR binds HO-2 weakly, if at all, and that the previously reported high affinity of BVR for HO is artifactual, resulting from the effects of free heme (dissociated from HO) on BVR fluorescence. PMID:25196843

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

    PubMed

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

    2014-11-20

    One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with

  6. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    PubMed

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  7. Direct AKAP-mediated protein-protein interactions as potential drug targets.

    PubMed

    Hundsrucker, C; Klussmann, E

    2008-01-01

    A-kinase-anchoring proteins (AKAPs) are a diverse family of about 50 scaffolding proteins. They are defined by the presence of a structurally conserved protein kinase A (PKA)-binding domain. AKAPs tether PKA and other signalling proteins such as further protein kinases, protein phosphatases and phosphodiesterases by direct protein-protein interactions to cellular compartments. Thus, AKAPs form the basis of signalling modules that integrate cellular signalling processes and limit these to defined sites. Disruption of AKAP functions by gene targeting, knockdown approaches and, in particular, pharmacological disruption of defined AKAP-dependent protein-protein interactions has revealed key roles of AKAPs in numerous processes, including the regulation of cardiac myocyte contractility and vasopressin-mediated water reabsorption in the kidney. Dysregulation of such processes causes diseases, including cardiovascular and renal disorders. In this review, we discuss AKAP functions elucidated by gene targeting and knockdown approaches, but mainly focus on studies utilizing peptides for disruption of direct AKAP-mediated protein-protein interactions. The latter studies point to direct AKAP-mediated protein-protein interactions as targets for novel drugs.

  8. A fluorescent probe for imaging p53-MDM2 protein-protein interaction.

    PubMed

    Liu, Zhenzhen; Miao, Zhenyuan; Li, Jin; Fang, Kun; Zhuang, Chunlin; Du, Lupei; Sheng, Chunquan; Li, Minyong

    2015-04-01

    In this article, we describe a no-wash small-molecule fluorescent probe for detecting and imaging p53-MDM2 protein-protein interaction based on an environment-sensitive fluorescent turn-on mechanism. After extensive biological examination, this probe L1 exhibited practical activity and selectivity in vitro and in cellulo.

  9. Classification methods for finding articles describing protein-protein interactions in PubMed.

    PubMed

    Matos, Sérgio; Oliveira, José Luís

    2011-09-16

    With the rapid expansion in the number of published papers in the biomedical field, finding relevant articles has become a demanding task for researchers. This has led to increasing interest in the use of text mining tools that help search the literature and identify the most relevant documents or information. One specific topic of interest is related to the identification of articles that might be used for extracting protein-protein interactions. Using the BioCreative III Article Classification Task dataset, composed of PubMed abstracts classified as relevant or non-relevant for describing protein-protein interactions, we compare different classification methods with different sets of features. The best results--area under the interpolated precision-recall curve of 0.654--indicate that the proposed classification strategy could be incorporated in the database curation workflows in order to prioritize articles for extraction of protein-protein interactions. Furthermore, we also analysed the use of this method for ranking documents resulting from general PubMed queries, and propose that this approach could be useful for general researchers looking for publications describing protein-protein interactions within a particular topic of interest.

  10. Manipulating fatty acid biosynthesis in microalgae for biofuel through protein-protein interactions.

    PubMed

    Blatti, Jillian L; Beld, Joris; Behnke, Craig A; Mendez, Michael; Mayfield, Stephen P; Burkart, Michael D

    2012-01-01

    Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP) and thioesterase (TE) govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr) as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes.

  11. Manipulating Fatty Acid Biosynthesis in Microalgae for Biofuel through Protein-Protein Interactions

    PubMed Central

    Blatti, Jillian L.; Beld, Joris; Behnke, Craig A.; Mendez, Michael; Mayfield, Stephen P.; Burkart, Michael D.

    2012-01-01

    Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP) and thioesterase (TE) govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr) as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes. PMID:23028438

  12. ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

    PubMed

    Veres, Daniel V; Gyurkó, Dávid M; Thaler, Benedek; Szalay, Kristóf Z; Fazekas, Dávid; Korcsmáros, Tamás; Csermely, Peter

    2015-01-01

    Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

  13. Large-scale mapping of human protein-protein interactions by mass spectrometry.

    PubMed

    Ewing, Rob M; Chu, Peter; Elisma, Fred; Li, Hongyan; Taylor, Paul; Climie, Shane; McBroom-Cerajewski, Linda; Robinson, Mark D; O'Connor, Liam; Li, Michael; Taylor, Rod; Dharsee, Moyez; Ho, Yuen; Heilbut, Adrian; Moore, Lynda; Zhang, Shudong; Ornatsky, Olga; Bukhman, Yury V; Ethier, Martin; Sheng, Yinglun; Vasilescu, Julian; Abu-Farha, Mohamed; Lambert, Jean-Philippe; Duewel, Henry S; Stewart, Ian I; Kuehl, Bonnie; Hogue, Kelly; Colwill, Karen; Gladwish, Katharine; Muskat, Brenda; Kinach, Robert; Adams, Sally-Lin; Moran, Michael F; Morin, Gregg B; Topaloglou, Thodoros; Figeys, Daniel

    2007-01-01

    Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

  14. An evaluation of in vitro protein-protein interaction techniques: assessing contaminating background proteins.

    PubMed

    Howell, Jenika M; Winstone, Tara L; Coorssen, Jens R; Turner, Raymond J

    2006-04-01

    Determination of protein-protein interactions is an important component in assigning function and discerning the biological relevance of proteins within a broader cellular context. In vitro protein-protein interaction methodologies, including affinity chromatography, coimmunoprecipitation, and newer approaches such as protein chip arrays, hold much promise in the detection of protein interactions, particularly in well-characterized organisms with sequenced genomes. However, each of these approaches attracts certain background proteins that can thwart detection and identification of true interactors. In addition, recombinant proteins expressed in Escherichia coli are also extensively used to assess protein-protein interactions, and background proteins in these isolates can thus contaminate interaction studies. Rigorous validation of a true interaction thus requires not only that an interaction be found by alternate techniques, but more importantly that researchers be aware of and control for matrix/support dependence. Here, we evaluate these methods for proteins interacting with DmsD (an E. coli redox enzyme maturation protein chaperone), in vitro, using E. coli subcellular fractions as prey sources. We compare and contrast the various in vitro interaction methods to identify some of the background proteins and protein profiles that are inherent to each of the methods in an E. coli system.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26608097

  17. Prediction of virus-host protein-protein interactions mediated by short linear motifs.

    PubMed

    Becerra, Andrés; Bucheli, Victor A; Moreno, Pedro A

    2017-03-09

    Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

  18. Small Molecules Engage Hot Spots through Cooperative Binding To Inhibit a Tight Protein-Protein Interaction.

    PubMed

    Liu, Degang; Xu, David; Liu, Min; Knabe, William Eric; Yuan, Cai; Zhou, Donghui; Huang, Mingdong; Meroueh, Samy O

    2017-03-28

    Protein-protein interactions drive every aspect of cell signaling, yet only a few small-molecule inhibitors of these interactions exist. Despite our ability to identify critical residues known as hot spots, little is known about how to effectively engage them to disrupt protein-protein interactions. Here, we take advantage of the ease of preparation and stability of pyrrolinone 1, a small-molecule inhibitor of the tight interaction between the urokinase receptor (uPAR) and its binding partner, the urokinase-type plasminogen activator uPA, to synthesize more than 40 derivatives and explore their effect on the protein-protein interaction. We report the crystal structure of uPAR bound to previously discovered pyrazole 3 and to pyrrolinone 12. While both 3 and 12 bind to uPAR and compete with a fluorescently labeled peptide probe, only 12 and its derivatives inhibit the full uPAR·uPA interaction. Compounds 3 and 12 mimic and engage different hot-spot residues on uPA and uPAR, respectively. Interestingly, 12 is involved in a π-cation interaction with Arg-53, which is not considered a hot spot. Explicit-solvent molecular dynamics simulations reveal that 3 and 12 exhibit dramatically different correlations of motion with residues on uPAR. Free energy calculations for the wild-type and mutant uPAR bound to uPA or 12 show that Arg-53 interacts with uPA or with 12 in a highly cooperative manner, thereby altering the contributions of hot spots to uPAR binding. The direct engagement of peripheral residues not considered hot spots through π-cation or salt-bridge interactions could provide new opportunities for enhanced small-molecule engagement of hot spots to disrupt challenging protein-protein interactions.

  19. Investigating protein-protein interactions in live cells using bioluminescence resonance energy transfer.

    PubMed

    Deriziotis, Pelagia; Graham, Sarah A; Estruch, Sara B; Fisher, Simon E

    2014-05-26

    Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live cells. BRET is the non-radiative transfer of energy from a 'donor' luciferase enzyme to an 'acceptor' fluorescent protein. In the most common configuration of this assay, the donor is Renilla reniformis luciferase and the acceptor is Yellow Fluorescent Protein (YFP). Because the efficiency of energy transfer is strongly distance-dependent, observation of the BRET phenomenon requires that the donor and acceptor be in close proximity. To test for an interaction between two proteins of interest in cultured mammalian cells, one protein is expressed as a fusion with luciferase and the second as a fusion with YFP. An interaction between the two proteins of interest may bring the donor and acceptor sufficiently close for energy transfer to occur. Compared to other techniques for investigating protein-protein interactions, the BRET assay is sensitive, requires little hands-on time and few reagents, and is able to detect interactions which are weak, transient, or dependent on the biochemical environment found within a live cell. It is therefore an ideal approach for confirming putative interactions suggested by yeast two-hybrid or mass spectrometry proteomics studies, and in addition it is well-suited for mapping interacting regions, assessing the effect of post-translational modifications on protein-protein interactions, and evaluating the impact of mutations identified in patient DNA.

  20. Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks.

    PubMed

    Monji, Hiroyuki; Koizumi, Satoshi; Ozaki, Tomonobu; Ohkawa, Takenao

    2011-02-15

    Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored. We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process. The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

  1. Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.

    PubMed

    Tuller, T; Atar, S; Ruppin, E; Gurevich, M; Achiron, A

    2013-03-01

    The aim of this study is to understand intracellular regulatory mechanisms in peripheral blood mononuclear cells (PBMCs), which are either common to many autoimmune diseases or specific to some of them. We incorporated large-scale data such as protein-protein interactions, gene expression and demographical information of hundreds of patients and healthy subjects, related to six autoimmune diseases with available large-scale gene expression measurements: multiple sclerosis (MS), systemic lupus erythematosus (SLE), juvenile rheumatoid arthritis (JRA), Crohn's disease (CD), ulcerative colitis (UC) and type 1 diabetes (T1D). These data were analyzed concurrently by statistical and systems biology approaches tailored for this purpose. We found that chemokines such as CXCL1-3, 5, 6 and the interleukin (IL) IL8 tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In addition, the anti-apoptotic gene BCL3, interferon-γ (IFNG), and the vitamin D receptor (VDR) gene physically interact with significantly many genes that tend to be differentially expressed in PBMCs of patients with the analyzed autoimmune diseases. In general, similar cellular processes tend to be differentially expressed in PBMC in the analyzed autoimmune diseases. Specifically, the cellular processes related to cell proliferation (for example, epidermal growth factor, platelet-derived growth factor, nuclear factor-κB, Wnt/β-catenin signaling, stress-activated protein kinase c-Jun NH2-terminal kinase), inflammatory response (for example, interleukins IL2 and IL6, the cytokine granulocyte-macrophage colony-stimulating factor and the B-cell receptor), general signaling cascades (for example, mitogen-activated protein kinase, extracellular signal-regulated kinase, p38 and TRK) and apoptosis are activated in most of the analyzed autoimmune diseases. However, our results suggest that in each of the analyzed diseases, apoptosis and chemotaxis are activated via

  2. Multifunctionality of the linker histones: an emerging role for protein-protein interactions.

    PubMed

    McBryant, Steven J; Lu, Xu; Hansen, Jeffrey C

    2010-05-01

    Linker histones, e.g., H1, are best known for their ability to bind to nucleosomes and stabilize both nucleosome structure and condensed higher-order chromatin structures. However, over the years many investigators have reported specific interactions between linker histones and proteins involved in important cellular processes. The purpose of this review is to highlight evidence indicating an important alternative mode of action for H1, namely protein-protein interactions. We first review key aspects of the traditional view of linker histone action, including the importance of the H1 C-terminal domain. We then discuss the current state of knowledge of linker histone interactions with other proteins, and, where possible, highlight the mechanism of linker histone-mediated protein-protein interactions. Taken together, the data suggest a combinatorial role for the linker histones, functioning both as primary chromatin architectural proteins and simultaneously as recruitment hubs for proteins involved in accessing and modifying the chromatin fiber.

  3. Multifunctionality of the linker histones: an emerging role for protein-protein interactions

    PubMed Central

    McBryant, Steven J; Lu, Xu; Hansen, Jeffrey C

    2010-01-01

    Linker histones, e.g., H1, are best known for their ability to bind to nucleosomes and stabilize both nucleosome structure and condensed higher-order chromatin structures. However, over the years many investigators have reported specific interactions between linker histones and proteins involved in important cellular processes. The purpose of this review is to highlight evidence indicating an important alternative mode of action for H1, namely protein-protein interactions. We first review key aspects of the traditional view of linker histone action, including the importance of the H1 C-terminal domain. We then discuss the current state of knowledge of linker histone interactions with other proteins, and, where possible, highlight the mechanism of linker histone-mediated protein-protein interactions. Taken together, the data suggest a combinatorial role for the linker histones, functioning both as primary chromatin architectural proteins and simultaneously as recruitment hubs for proteins involved in accessing and modifying the chromatin fiber. PMID:20309017

  4. Crosslinking Studies of Protein-Protein Interactions in Nonribosomal Peptide Biosynthesis

    PubMed Central

    Hur, Gene H.; Meier, Jordan L.; Baskin, Jeremy; Codelli, Julian A.; Bertozzi, Carolyn R.; Marahiel, Mohamed A.; Burkart, Michael D.

    2009-01-01

    Summary Selective protein-protein interactions between nonribosomal peptide synthetase (NRPS) proteins, governed by communication-mediating (COM) domains, are responsible for proper translocation of biosynthetic intermediates to produce the natural product. In this study, we developed a crosslinking assay, utilizing bioorthogonal probes compatible with carrier protein modification, for probing the protein interactions between COM domains of NRPS enzymes. Employing the Huisgen 1,3-dipolar cycloaddition of azides and alkynes, we examined crosslinking of cognate NRPS modules within the tyrocidine pathway and demonstrated the sensitivity of our panel of crosslinking probes towards the selective protein interactions of compatible COM domains. These studies indicate that copper-free crosslinking substrates uniquely offer a diagnostic probe for protein-protein interactions. Likewise, these crosslinking probes serve as ideal chemical tools for structural studies between NRPS modules where functional assays are lacking. PMID:19345117

  5. Pin-Align: a new dynamic programming approach to align protein-protein interaction networks.

    PubMed

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

    2014-01-01

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

  6. Crosslinking studies of protein-protein interactions in nonribosomal peptide biosynthesis.

    PubMed

    Hur, Gene H; Meier, Jordan L; Baskin, Jeremy; Codelli, Julian A; Bertozzi, Carolyn R; Marahiel, Mohamed A; Burkart, Michael D

    2009-04-24

    Selective protein-protein interactions between nonribosomal peptide synthetase (NRPS) proteins, governed by communication-mediating (COM) domains, are responsible for proper translocation of biosynthetic intermediates to produce the natural product. In this study, we developed a crosslinking assay, utilizing bioorthogonal probes compatible with carrier protein modification, for probing the protein interactions between COM domains of NRPS enzymes. Employing the Huisgen 1,3-dipolar cycloaddition of azides and alkynes, we examined crosslinking of cognate NRPS modules within the tyrocidine pathway and demonstrated the sensitivity of our panel of crosslinking probes toward the selective protein interactions of compatible COM domains. These studies indicate that copper-free crosslinking substrates uniquely offer a diagnostic probe for protein-protein interactions. Likewise, these crosslinking probes serve as ideal chemical tools for structural studies between NRPS modules where functional assays are lacking.

  7. Dynamic proteomics in modeling of the living cell. Protein-protein interactions.

    PubMed

    Terentiev, A A; Moldogazieva, N T; Shaitan, K V

    2009-12-01

    This review is devoted to describing, summarizing, and analyzing of dynamic proteomics data obtained over the last few years and concerning the role of protein-protein interactions in modeling of the living cell. Principles of modern high-throughput experimental methods for investigation of protein-protein interactions are described. Systems biology approaches based on integrative view on cellular processes are used to analyze organization of protein interaction networks. It is proposed that finding of some proteins in different protein complexes can be explained by their multi-modular and polyfunctional properties; the different protein modules can be located in the nodes of protein interaction networks. Mathematical and computational approaches to modeling of the living cell with emphasis on molecular dynamics simulation are provided. The role of the network analysis in fundamental medicine is also briefly reviewed.

  8. A Yeast Two-Hybrid approach for probing protein-protein interactions at the centrosome

    PubMed Central

    Galletta, Brian J.; Rusan, Nasser M.

    2016-01-01

    As a large, non-membrane bound organelle, the centrosome must rely heavily on protein-protein interactions to assemble itself in the cytoplasm and perform its functions as a microtubule-organizing center. Therefore, to understand how this organelle is built and functions, one must understand the protein-protein interactions made by each centrosome protein. Unfortunately, the highly interconnected nature of the centrosome, combined with its predicted unstructured, coil-rich proteins, has made the use of many standard approaches to studying protein-protein interactions very challenging. The yeast-two hybrid (Y2H) system is well suited for studying the centrosome and is an important complement to other biochemical approaches. In this chapter we describe how to carry out a directed Y2H screen to identify the direct interactions between a given centrosome protein and a library of others. Specifically, we detail using a bioinformatics based approach (structure prediction programs) to subdivide proteins and screen for interactions using an array-based Y2H approach. We also describe how to use the interaction information garnered from this screen to generate mutations to disrupt specific interactions using mutagenic-PCR and a “reverse” Y2H screen. Finally, we discuss how information from such a screen can be integrated into existing models of centrosome assembly and how it can initiate and guide extensive in vitro and in vivo experimentation to test these models. PMID:26175443

  9. Optimization of rhodanine scaffold for the development of protein-protein interaction inhibitors.

    PubMed

    Ferro, Stefania; De Luca, Laura; Agharbaoui, Fatima Ezzahra; Christ, Frauke; Debyser, Zeger; Gitto, Rosaria

    2015-07-01

    Searching for novel protein-protein interactions inhibitors (PPIs) herein we describe the identification of a new series of rhodanine derivatives. The selection was performed by means virtual-screening, docking studies, Molecular Dynamic (MD) simulations and synthetic approaches. All the new obtained compounds were tested in order to evaluate their ability to inhibit the interaction between the HIV-1 integrase (IN) enzyme and the nuclear protein lens epithelium growth factor LEDGF/p75.

  10. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset.

    PubMed

    Shi, Ming-Guang; Xia, Jun-Feng; Li, Xue-Ling; Huang, De-Shuang

    2010-03-01

    Identifying protein-protein interactions (PPIs) is critical for understanding the cellular function of the proteins and the machinery of a proteome. Data of PPIs derived from high-throughput technologies are often incomplete and noisy. Therefore, it is important to develop computational methods and high-quality interaction dataset for predicting PPIs. A sequence-based method is proposed by combining correlation coefficient (CC) transformation and support vector machine (SVM). CC transformation not only adequately considers the neighboring effect of protein sequence but describes the level of CC between two protein sequences. A gold standard positives (interacting) dataset MIPS Core and a gold standard negatives (non-interacting) dataset GO-NEG of yeast Saccharomyces cerevisiae were mined to objectively evaluate the above method and attenuate the bias. The SVM model combined with CC transformation yielded the best performance with a high accuracy of 87.94% using gold standard positives and gold standard negatives datasets. The source code of MATLAB and the datasets are available on request under smgsmg@mail.ustc.edu.cn.

  11. Comparative analysis of protein-protein interactions in the defense response of rice and wheat.

    PubMed

    Cantu, Dario; Yang, Baoju; Ruan, Randy; Li, Kun; Menzo, Virginia; Fu, Daolin; Chern, Mawsheng; Ronald, Pamela C; Dubcovsky, Jorge

    2013-03-12

    Despite the importance of wheat as a major staple crop and the negative impact of diseases on its production worldwide, the genetic mechanisms and gene interactions involved in the resistance response in wheat are still poorly understood. The complete sequence of the rice genome has provided an extremely useful parallel road map for genetic and genomics studies in wheat. The recent construction of a defense response interactome in rice has the potential to further enhance the translation of advances in rice to wheat and other grasses. The objective of this study was to determine the degree of conservation in the protein-protein interactions in the rice and wheat defense response interactomes. As entry points we selected proteins that serve as key regulators of the rice defense response: the RAR1/SGT1/HSP90 protein complex, NPR1, XA21, and XB12 (XA21 interacting protein 12). Using available wheat sequence databases and phylogenetic analyses we identified and cloned the wheat orthologs of these four rice proteins, including recently duplicated paralogs, and their known direct interactors and tested 86 binary protein interactions using yeast-two-hybrid (Y2H) assays. All interactions between wheat proteins were further tested using in planta bimolecular fluorescence complementation (BiFC). Eighty three percent of the known rice interactions were confirmed when wheat proteins were tested with rice interactors and 76% were confirmed using wheat protein pairs. All interactions in the RAR1/SGT1/ HSP90, NPR1 and XB12 nodes were confirmed for the identified orthologous wheat proteins, whereas only forty four percent of the interactions were confirmed in the interactome node centered on XA21. We hypothesize that this reduction may be associated with a different sub-functionalization history of the multiple duplications that occurred in this gene family after the divergence of the wheat and rice lineages. The observed high conservation of interactions between proteins that

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

    PubMed

    Gillis, Jesse; Ballouz, Sara; Pavlidis, Paul

    2014-04-04

    Networks constructed from aggregated protein-protein interaction data are commonplace in biology. But the studies these data are derived from were conducted with their own hypotheses and foci. Focusing on data from budding yeast present in BioGRID, we determine that many of the downstream signals present in network data are significantly impacted by biases in the original data. We determine the degree to which selection bias in favor of biologically interesting bait proteins goes down with study size, while we also find that promiscuity in prey contributes more substantially in larger studies. We analyze interaction studies over time with respect to data in the Gene Ontology and find that reproducibly observed interactions are less likely to favor multifunctional proteins. We find that strong alignment between co-expression and protein-protein interaction data occurs only for extreme co-expression values, and use this data to suggest candidates for targets likely to reveal novel biology in follow-up studies. Protein-protein interaction data finds particularly heavy use in the interpretation of disease-causal variants. In principle, network data allows researchers to find novel commonalities among candidate genes. In this study, we detail several of the most salient biases contributing to aggregated protein-protein interaction databases. We find strong evidence for the role of selection and laboratory biases. Many of these effects contribute to the commonalities researchers find for disease genes. In order for characterization of disease genes and their interactions to not simply be an artifact of researcher preference, it is imperative to identify data biases explicitly. Based on this, we also suggest ways to move forward in producing candidates less influenced by prior knowledge. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes? Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Design of a fluorescence-activated cell sorting-based Mammalian protein-protein interaction trap.

    PubMed

    Lievens, Sam; Van der Heyden, José; Vertenten, Els; Plum, Jean; Vandekerckhove, Joël; Tavernier, Jan

    2004-01-01

    The mammalian protein-protein interaction trap (MAPPIT) is a two-hybrid assay based on insights in type I cytokine signal transduction. Bait and prey polypeptides are tethered to mutant cytokine receptor chimeras which are impaired in signaling. On bait-prey interaction and after ligand stimulation, the JAK-STAT signaling cascade is initiated leading to transcription of a reporter or marker gene under the control of the STAT3-responsive rPAP1 promoter. In addition to a physiologically relevant context for mammalian protein-protein interactions this method provides separation of interactor and effector zones, and can be applied for both analytical and screening purposes. In the protocol described here, a cytokine receptor derived surface tag is used as a selectable marker. After an initial presort step using magnetic-activated cell sorting (MACS), "positive" cells are selected by fluorescence-activated cell sorting (FACS).

  14. Some remarks on prediction of protein-protein interaction with machine learning.

    PubMed

    Zhang, Shao-Wu; Wei, Ze-Gang

    2015-01-01

    Protein-protein interactions (PPIs) play a key role in many cellular processes. Uncovering the PPIs and their function within the cell is a challenge of post-genomic biology and will improve our understanding of disease and help in the development of novel methods for disease diagnosis and forensics. The experimental methods currently used to identify PPIs are both time-consuming and expensive, and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. These obstacles could be overcome by developing computational approaches to predict PPIs and validate the obtained experimental results. In this work, we will describe the recent advances in predicting protein-protein interaction from the following aspects: i) the benchmark dataset construction, ii) the sequence representation approaches, iii) the common machine learning algorithms, and iv) the cross-validation test methods and assessment metrics.

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

    PubMed

    Yang, Lei; Zhao, Xudong; Tang, Xianglong

    2014-01-01

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

  16. Single-Molecule Study of Protein-Protein Interaction Dynamics in a Cell Signaling System

    SciTech Connect

    Tan, Xin; Nalbant, Perihan; Toutchkine, Alexei; Hu, Dehong; Vorpagel, Erich R.; Hahn, Klaus M.; Lu, H. Peter

    2004-01-01

    We report a study on protein-protein noncovalent interactions in an intracellular signaling protein complex, using single-molecule spectroscopy and molecular dynamics (MD) simulations. A Wiskott-Aldrich Syndrome Protein (WASP) fragment that binds only the activated intracellular signaling protein Cdc42 was labeled with a novel solvatochromic dye and used to probe hydrophobic interactions significant to Cdc42/WASP recognition. The study shows static and dynamic inhomogeneous conformational fluctuations of the protein complex that involve bound and loosely bound states. A two-coupled, two-state Markovian kinetic model is proposed for the conformational dynamics. Finally, the MD simulations explore the origin of these conformational states and associated conformational fluctuations in this protein-protein interaction system.

  17. Specific ion and buffer effects on protein-protein interactions of a monoclonal antibody.

    PubMed

    Roberts, D; Keeling, R; Tracka, M; van der Walle, C F; Uddin, S; Warwicker, J; Curtis, R

    2015-01-05

    Better predictive ability of salt and buffer effects on protein-protein interactions requires separating out contributions due to ionic screening, protein charge neutralization by ion binding, and salting-in(out) behavior. We have carried out a systematic study by measuring protein-protein interactions for a monoclonal antibody over an ionic strength range of 25 to 525 mM at 4 pH values (5, 6.5, 8, and 9) in solutions containing sodium chloride, calcium chloride, sodium sulfate, or sodium thiocyante. The salt ions are chosen so as to represent a range of affinities for protein charged and noncharged groups. The results are compared to effects of various buffers including acetate, citrate, phosphate, histidine, succinate, or tris. In low ionic strength solutions, anion binding affinity is reflected by the ability to reduce protein-protein repulsion, which follows the order thiocyanate > sulfate > chloride. The sulfate specific effect is screened at the same ionic strength required to screen the pH dependence of protein-protein interactions indicating sulfate binding only neutralizes protein charged groups. Thiocyanate specific effects occur over a larger ionic strength range reflecting adsorption to charged and noncharged regions of the protein. The latter leads to salting-in behavior and, at low pH, a nonmonotonic interaction profile with respect to sodium thiocyanate concentration. The effects of thiocyanate can not be rationalized in terms of only neutralizing double layer forces indicating the presence of an additional short-ranged protein-protein attraction at moderate ionic strength. Conversely, buffer specific effects can be explained through a charge neutralization mechanism, where buffers with greater valency are more effective at reducing double layer forces at low pH. Citrate binding at pH 6.5 leads to protein charge inversion and the formation of attractive electrostatic interactions. Throughout the report, we highlight similarities in the measured

  18. Bioluminescence resonance energy transfer system for measuring dynamic protein-protein interactions in bacteria.

    PubMed

    Cui, Boyu; Wang, Yao; Song, Yunhong; Wang, Tietao; Li, Changfu; Wei, Yahong; Luo, Zhao-Qing; Shen, Xihui

    2014-05-20

    Protein-protein interactions are important for virtually every biological process, and a number of elegant approaches have been designed to detect and evaluate such interactions. However, few of these methods allow the detection of dynamic and real-time protein-protein interactions in bacteria. Here we describe a bioluminescence resonance energy transfer (BRET) system based on the bacterial luciferase LuxAB. We found that enhanced yellow fluorescent protein (eYFP) accepts the emission from LuxAB and emits yellow fluorescence. Importantly, BRET occurred when LuxAB and eYFP were fused, respectively, to the interacting protein pair FlgM and FliA. Furthermore, we observed sirolimus (i.e., rapamycin)-inducible interactions between FRB and FKBP12 and a dose-dependent abolishment of such interactions by FK506, the ligand of FKBP12. Using this system, we showed that osmotic stress or low pH efficiently induced multimerization of the regulatory protein OmpR and that the multimerization induced by low pH can be reversed by a neutralizing agent, further indicating the usefulness of this system in the measurement of dynamic interactions. This method can be adapted to analyze dynamic protein-protein interactions and the importance of such interactions in bacterial processes such as development and pathogenicity. Real-time measurement of protein-protein interactions in prokaryotes is highly desirable for determining the roles of protein complex in the development or virulence of bacteria, but methods that allow such measurement are not available. Here we describe the development of a bioluminescence resonance energy transfer (BRET) technology that meets this need. The use of endogenous excitation light in this strategy circumvents the requirement for the sophisticated instrument demanded by standard fluorescence resonance energy transfer (FRET). Furthermore, because the LuxAB substrate decanal is membrane permeable, the assay can be performed without lysing the bacterial cells

  19. Assessing Energetic Contributions to Binding from a Disordered Region in a Protein-Protein Interaction

    SciTech Connect

    S Cho; C Swaminathan; D Bonsor; M Kerzic; R Guan; J Yang; C Kieke; P Anderson; D Kranz; et al.

    2011-12-31

    Many functional proteins are at least partially disordered prior to binding. Although the structural transitions upon binding of disordered protein regions can influence the affinity and specificity of protein complexes, their precise energetic contributions to binding are unknown. Here, we use a model protein-protein interaction system in which a locally disordered region has been modified by directed evolution to quantitatively assess the thermodynamic and structural contributions to binding of disorder-to-order transitions. Through X-ray structure determination of the protein binding partners before and after complex formation and isothermal titration calorimetry of the interactions, we observe a correlation between protein ordering and binding affinity for complexes along this affinity maturation pathway. Additionally, we show that discrepancies between observed and calculated heat capacities based on buried surface area changes in the protein complexes can be explained largely by heat capacity changes that would result solely from folding the locally disordered region. Previously developed algorithms for predicting binding energies of protein-protein interactions, however, are unable to correctly model the energetic contributions of the structural transitions in our model system. While this highlights the shortcomings of current computational methods in modeling conformational flexibility, it suggests that the experimental methods used here could provide training sets of molecular interactions for improving these algorithms and further rationalizing molecular recognition in protein-protein interactions.

  20. Stabilization and inhibition of protein-protein interactions: the 14-3-3 case study.

    PubMed

    Milroy, Lech-Gustav; Brunsveld, Luc; Ottmann, Christian

    2013-01-18

    Small-molecule modulation of protein-protein interactions (PPIs) is one of the most exciting but also difficult fields in chemical biology and drug development. As one of the most important "hub" proteins with at least 200-300 interaction partners, the 14-3-3 proteins are an especially fruitful case for PPI intervention. Here, we summarize recent success stories in small-molecule modulation, both inhibition and stabilization, of 14-3-3 PPIs. The chemical breath of modulators includes natural products such as fusicoccin A and derivatives but also compounds identified via high-throughput and in silico screening, which has yielded a toolbox of useful inhibitors and stabilizers for this interesting class of adapter proteins. Protein-protein interactions (PPIs) are involved in almost all biological processes, with any given protein typically engaged in complexes with other proteins for the majority of its lifetime. Hence, proteins function not simply as single, isolated entities but display their roles by interacting with other cellular components. These different interaction patterns are presumably as important as the intrinsic biochemical activity status of the protein itself. The biological role of a protein is therefore decisively dependent on the underlying PPI network that furthermore can show great spatial and temporal variations. A thorough appreciation and understanding of this concept and its regulation mechanisms could help to develop new therapeutic agents and concepts.

  1. A novel microfluidics-based method for probing weak protein-protein interactions.

    PubMed

    Tan, Darren Cherng-wen; Wijaya, I Putu Mahendra; Andreasson-Ochsner, Mirjam; Vasina, Elena Nikolaevna; Nallani, Madhavan; Hunziker, Walter; Sinner, Eva-Kathrin

    2012-08-07

    We report the use of a novel microfluidics-based method to detect weak protein-protein interactions between membrane proteins. The tight junction protein, claudin-2, synthesised in vitro using a cell-free expression system in the presence of polymer vesicles as membrane scaffolds, was used as a model membrane protein. Individual claudin-2 molecules interact weakly, although the cumulative effect of these interactions is significant. This effect results in a transient decrease of average vesicle dispersivity and reduction in transport speed of claudin-2-functionalised vesicles. Polymer vesicles functionalised with claudin-2 were perfused through a microfluidic channel and the time taken to traverse a defined distance within the channel was measured. Functionalised vesicles took 1.19 to 1.69 times longer to traverse this distance than unfunctionalised ones. Coating the channel walls with protein A and incubating the vesicles with anti-claudin-2 antibodies prior to perfusion resulted in the functionalised vesicles taking 1.75 to 2.5 times longer to traverse this distance compared to the controls. The data show that our system is able to detect weak as well as strong protein-protein interactions. This system offers researchers a portable, easily operated and customizable platform for the study of weak protein-protein interactions, particularly between membrane proteins.

  2. Do cancer proteins really interact strongly in the human protein-protein interaction network?

    PubMed

    Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

    2011-06-01

    Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

  3. Biochemical and Physiological Characterization: Development & Apply Optical Methods for Charaterizing Biochemical Protein-Protein Interactions in MR-1

    SciTech Connect

    Weiss, Shimon

    2006-08-30

    The objectives of this report are to: Develop novel site-specific protein labeling chemistries for assaying protein-protein interactions in MR-1; and development of a novel optical acquisition and data analysis method for characterizing protein-protein interactions in MR-1 model systems. Our work on analyzing protein-protein interactions in MR-1 is divided in four areas: (1) expression and labeling of MR-1 proteins; (2) general scheme for site-specific fluorescent labeling of expressed proteins; (3) methodology development for monitoring protein-protein interactions; and (4) study of protein-protein interactions in MR-1. In this final report, we give an account for our advances in all areas.

  4. Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces.

    PubMed

    Aytuna, A Selim; Gursoy, Attila; Keskin, Ozlem

    2005-06-15

    Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. http://gordion.hpc.eng.ku.edu.tr/prism agursoy@ku.edu.tr; okeskin@ku.edu.tr.

  5. A coevolution analysis for identifying protein-protein interactions by Fourier transform

    PubMed Central

    Yin, Changchuan; Yau, Stephen S. -T.

    2017-01-01

    Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI). PMID:28430779

  6. A coevolution analysis for identifying protein-protein interactions by Fourier transform.

    PubMed

    Yin, Changchuan; Yau, Stephen S-T

    2017-01-01

    Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).

  7. Fluorescence Anisotropy as a Tool to Study Protein-protein Interactions.

    PubMed

    Gijsbers, Abril; Nishigaki, Takuya; Sánchez-Puig, Nuria

    2016-10-21

    Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is necessary to characterize it at the structural and mechanistic level. Several biochemical and biophysical methods exist for such purpose. Among them, fluorescence anisotropy is a powerful technique particularly used when the fluorescence intensity of a fluorophore-labeled protein remains constant upon protein-protein interaction. In this technique, a fluorophore-labeled protein is excited with vertically polarized light of an appropriate wavelength that selectively excites a subset of the fluorophores according to their relative orientation with the incoming beam. The resulting emission also has a directionality whose relationship in the vertical and horizontal planes defines anisotropy (r) as follows: r=(IVV-IVH)/(IVV+2IVH), where IVV and IVH are the fluorescence intensities of the vertical and horizontal components, respectively. Fluorescence anisotropy is sensitive to the rotational diffusion of a fluorophore, namely the apparent molecular size of a fluorophore attached to a protein, which is altered upon protein-protein interaction. In the present text, the use of fluorescence anisotropy as a tool to study protein-protein interactions was exemplified to address the binding between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor like-1 GTPase (EFL1). Conventionally, labeling of a protein with a fluorophore is carried out on the thiol groups (cysteine) or in the amino groups (the N-terminal amine or lysine) of the protein. However, SBDS possesses several cysteines and lysines that did not allow site directed labeling of it. As an alternative technique, the dye 4',5'-bis(1,3,2 dithioarsolan-2-yl) fluorescein was used to specifically label a tetracysteine motif, Cys-Cys-Pro-Gly-Cys-Cys, genetically engineered in the C-terminus of the recombinant SBDS protein. Fitting of the

  8. Structural Interface Parameters Are Discriminatory in Recognising Near-Native Poses of Protein-Protein Interactions

    PubMed Central

    Malhotra, Sony; Sankar, Kannan; Sowdhamini, Ramanathan

    2014-01-01

    Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets. PMID:24498255

  9. A New Protein-Protein Interaction Sensor Based on Tripartite Split-GFP Association

    PubMed Central

    Cabantous, Stéphanie; Nguyen, Hau B.; Pedelacq, Jean-Denis; Koraïchi, Faten; Chaudhary, Anu; Ganguly, Kumkum; Lockard, Meghan A.; Favre, Gilles; Terwilliger, Thomas C.; Waldo, Geoffrey S.

    2013-01-01

    Monitoring protein-protein interactions in living cells is key to unraveling their roles in numerous cellular processes and various diseases. Previously described split-GFP based sensors suffer from poor folding and/or self-assembly background fluorescence. Here, we have engineered a micro-tagging system to monitor protein-protein interactions in vivo and in vitro. The assay is based on tripartite association between two twenty amino-acids long GFP tags, GFP10 and GFP11, fused to interacting protein partners, and the complementary GFP1-9 detector. When proteins interact, GFP10 and GFP11 self-associate with GFP1-9 to reconstitute a functional GFP. Using coiled-coils and FRB/FKBP12 model systems we characterize the sensor in vitro and in Escherichia coli. We extend the studies to mammalian cells and examine the FK-506 inhibition of the rapamycin-induced association of FRB/FKBP12. The small size of these tags and their minimal effect on fusion protein behavior and solubility should enable new experiments for monitoring protein-protein association by fluorescence. PMID:24092409

  10. A new protein-protein interaction sensor based on tripartite split-GFP association.

    PubMed

    Cabantous, Stéphanie; Nguyen, Hau B; Pedelacq, Jean-Denis; Koraïchi, Faten; Chaudhary, Anu; Ganguly, Kumkum; Lockard, Meghan A; Favre, Gilles; Terwilliger, Thomas C; Waldo, Geoffrey S

    2013-10-04

    Monitoring protein-protein interactions in living cells is key to unraveling their roles in numerous cellular processes and various diseases. Previously described split-GFP based sensors suffer from poor folding and/or self-assembly background fluorescence. Here, we have engineered a micro-tagging system to monitor protein-protein interactions in vivo and in vitro. The assay is based on tripartite association between two twenty amino-acids long GFP tags, GFP10 and GFP11, fused to interacting protein partners, and the complementary GFP1-9 detector. When proteins interact, GFP10 and GFP11 self-associate with GFP1-9 to reconstitute a functional GFP. Using coiled-coils and FRB/FKBP12 model systems we characterize the sensor in vitro and in Escherichia coli. We extend the studies to mammalian cells and examine the FK-506 inhibition of the rapamycin-induced association of FRB/FKBP12. The small size of these tags and their minimal effect on fusion protein behavior and solubility should enable new experiments for monitoring protein-protein association by fluorescence.

  11. Identification of essential proteins based on ranking edge-weights in protein-protein interaction networks.

    PubMed

    Wang, Yan; Sun, Huiyan; Du, Wei; Blanzieri, Enrico; Viero, Gabriella; Xu, Ying; Liang, Yanchun

    2014-01-01

    Essential proteins are those that are indispensable to cellular survival and development. Existing methods for essential protein identification generally rely on knock-out experiments and/or the relative density of their interactions (edges) with other proteins in a Protein-Protein Interaction (PPI) network. Here, we present a computational method, called EW, to first rank protein-protein interactions in terms of their Edge Weights, and then identify sub-PPI-networks consisting of only the highly-ranked edges and predict their proteins as essential proteins. We have applied this method to publicly-available PPI data on Saccharomyces cerevisiae (Yeast) and Escherichia coli (E. coli) for essential protein identification, and demonstrated that EW achieves better performance than the state-of-the-art methods in terms of the precision-recall and Jackknife measures. The highly-ranked protein-protein interactions by our prediction tend to be biologically significant in both the Yeast and E. coli PPI networks. Further analyses on systematically perturbed Yeast and E. coli PPI networks through randomly deleting edges demonstrate that the proposed method is robust and the top-ranked edges tend to be more associated with known essential proteins than the lowly-ranked edges.

  12. DAPPER: a data-mining resource for protein-protein interactions.

    PubMed

    Haider, Syed; Lipinszki, Zoltan; Przewloka, Marcin R; Ladak, Yaseen; D'Avino, Pier Paolo; Kimata, Yuu; Lio', Pietro; Glover, David M

    2015-01-01

    The identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster. This proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart's data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available. DAPPER offers an easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.

  13. Sulfhydryl-reactive, cleavable, and radioiodinatable benzophenone photoprobes for study of protein-protein interaction.

    PubMed

    Guo, Lian-Wang; Hajipour, Abdol R; Gavala, Monica L; Arbabian, Marty; Martemyanov, Kirill A; Arshavsky, Vadim Y; Ruoho, Arnold E

    2005-01-01

    The major task in proteomics is to understand how proteins interact with their partners. The photo-cross-linking technique enables direct probing of protein-protein interaction. Here we report the development of three novel sulfhydryl-reactive benzophenone photoprobes of short "arm" length, each with a substitution of either amino, iodo, or nitro at the para-position, rendering the benzophenone moiety directly radioiodinatable. Their potential for study of protein-protein interaction was assessed using the inhibitory subunit of rod cGMP phosphodiesterase (PDEgamma) and the activated transducin alphasubunit (G alpha t-GTPgammaS) as a model system. These photoprobes proved to be stable at neutral pH and dithiothreitol-cleavable in addition. The PDEgamma constructs derivatized at the C-terminal positions with these probes could be readily purified, had unaltered PDEgamma functional activity, and were shown to photo-cross-link to G alpha t-GTPgammaS with an efficiency as high as 40%. Additionally, the amino benzophenone probe was radioiodinated, facilitating sensitive detection of label transfer. The uniquely combined features of these benzophenone photoprobes promise robust and flexible methods for characterization of protein-protein interaction, either by mass spectrometry when a nonradioactive label is available or by autoradiography when using radioiodinated derivatives.

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

    PubMed Central

    Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

    2015-01-01

    As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw. PMID:26469276

  15. Structural Instability Tuning as a Regulatory Mechanism in Protein-Protein Interactions

    PubMed Central

    Chen, Li; Balabanidou, Vassilia; Remeta, David P.; Minetti, Conceição A.S.A.; Portaliou, Athina G.; Economou, Anastassios; Kalodimos, Charalampos G.

    2011-01-01

    SUMMARY Protein-protein interactions mediate a vast number of cellular processes. Here we present a regulatory mechanism in protein-protein interactions mediated by finely-tuned structural instability coupled with molecular mimicry. We show that a set of type III secretion (TTS) autoinhibited homodimeric chaperones adopt a molten-globule-like state that transiently exposes the substrate binding site as a means to become rapidly poised for binding to their cognate protein substrates. Packing defects at the homodimeric interface stimulate binding whereas correction of these defects results in less labile chaperones that give rise to non-functional biological systems. The protein substrates use structural mimicry to offset the “weak spots” in the chaperones and to counteract their autoinhibitory conformation. This regulatory mechanism of protein activity is evolutionary conserved among several TSS systems and presents a lucid example of functional advantage conferred upon a biological system by finely-tuned structural instability. PMID:22152477

  16. A conserved NAD(+) binding pocket that regulates protein-protein interactions during aging.

    PubMed

    Li, Jun; Bonkowski, Michael S; Moniot, Sébastien; Zhang, Dapeng; Hubbard, Basil P; Ling, Alvin J Y; Rajman, Luis A; Qin, Bo; Lou, Zhenkun; Gorbunova, Vera; Aravind, L; Steegborn, Clemens; Sinclair, David A

    2017-03-24

    DNA repair is essential for life, yet its efficiency declines with age for reasons that are unclear. Numerous proteins possess Nudix homology domains (NHDs) that have no known function. We show that NHDs are NAD(+) (oxidized form of nicotinamide adenine dinucleotide) binding domains that regulate protein-protein interactions. The binding of NAD(+) to the NHD domain of DBC1 (deleted in breast cancer 1) prevents it from inhibiting PARP1 [poly(adenosine diphosphate-ribose) polymerase], a critical DNA repair protein. As mice age and NAD(+) concentrations decline, DBC1 is increasingly bound to PARP1, causing DNA damage to accumulate, a process rapidly reversed by restoring the abundance of NAD(+) Thus, NAD(+) directly regulates protein-protein interactions, the modulation of which may protect against cancer, radiation, and aging.

  17. Characterization of protein-protein interaction interfaces from a single species.

    PubMed

    Talavera, David; Robertson, David L; Lovell, Simon C

    2011-01-01

    Most proteins attain their biological functions through specific interactions with other proteins. Thus, the study of protein-protein interactions and the interfaces that mediate these interactions is of prime importance for the understanding of biological function. In particular the precise determinants of binding specificity and their contributions to binding energy within protein interfaces are not well understood. In order to better understand these determinants an appropriate description of the interaction surface is needed. Available data from the yeast Saccharomyces cerevisiae allow us to focus on a single species and to use all the available structures, correcting for redundancy, instead of using structural representatives. This allows us to control for potentially confounding factors that may affect sequence propensities. We find a significant contribution of main-chain atoms to protein-protein interactions. These include interactions both with other main-chain and side-chain atoms on the interacting chain. We find that the type of interaction depends on both amino acid and secondary structure type involved in the contact. For example, residues in α-helices and large amino acids are the most likely to be involved in interactions through their side-chain atoms. We find an intriguing homogeneity when calculating the average solvation energy of different areas of the protein surface. Unexpectedly, homo- and hetero-complexes have quite similar results for all analyses. Our findings demonstrate that the manner in which protein-protein interactions are formed is determined by the residue type and the secondary structure found in the interface. However the homogeneity of the desolvation energy despite heterogeneity of interface properties suggests a complex relationship between interface composition and binding energy.

  18. Finding the evidence for protein-protein interactions from PubMed abstracts.

    PubMed

    Jang, Hyunchul; Lim, Jaesoo; Lim, Joon-Ho; Park, Soo-Jun; Lee, Kyu-Chul; Park, Seon-Hee

    2006-07-15

    Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. Contact authors.

  19. MEGADOCK: An All-to-All Protein-Protein Interaction Prediction System Using Tertiary Structure Data

    PubMed Central

    Ohue, Masahito; Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ishida, Takashi; Akiyama, Yutaka

    2014-01-01

    The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock. PMID:23855673

  20. Alternative splicing tends to avoid partial removals of protein-protein interaction sites

    PubMed Central

    2013-01-01

    Background Anecdotal evidence of the involvement of alternative splicing (AS) in the regulation of protein-protein interactions has been reported by several studies. AS events have been shown to significantly occur in regions where a protein interaction domain or a short linear motif is present. Several AS variants show partial or complete loss of interface residues, suggesting that AS can play a major role in the interaction regulation by selectively targeting the protein binding sites. In the present study we performed a statistical analysis of the alternative splicing of a non-redundant dataset of human protein-protein interfaces known at molecular level to determine the importance of this way of modulation of protein-protein interactions through AS. Results Using a Cochran-Mantel-Haenszel chi-square test we demonstrated that the alternative splicing-mediated partial removal of both heterodimeric and homodimeric binding sites occurs at lower frequencies than expected, and this holds true even if we consider only those isoforms whose sequence is less different from that of the canonical protein and which therefore allow to selectively regulate functional regions of the protein. On the other hand, large removals of the binding site are not significantly prevented, possibly because they are associated to drastic structural changes of the protein. The observed protection of the binding sites from AS is not preferentially directed towards putative hot spot interface residues, and is widespread to all protein functional classes. Conclusions Our findings indicate that protein-protein binding sites are generally protected from alternative splicing-mediated partial removals. However, some cases in which the binding site is selectively removed exist, and here we discuss one of them. PMID:23758645

  1. α/β-Peptide Foldamers Targeting Intracellular Protein-Protein Interactions with Activity in Living Cells.

    PubMed

    Checco, James W; Lee, Erinna F; Evangelista, Marco; Sleebs, Nerida J; Rogers, Kelly; Pettikiriarachchi, Anne; Kershaw, Nadia J; Eddinger, Geoffrey A; Belair, David G; Wilson, Julia L; Eller, Chelcie H; Raines, Ronald T; Murphy, William L; Smith, Brian J; Gellman, Samuel H; Fairlie, W Douglas

    2015-09-09

    Peptides can be developed as effective antagonists of protein-protein interactions, but conventional peptides (i.e., oligomers of l-α-amino acids) suffer from significant limitations in vivo. Short half-lives due to rapid proteolytic degradation and an inability to cross cell membranes often preclude biological applications of peptides. Oligomers that contain both α- and β-amino acid residues ("α/β-peptides") manifest decreased susceptibility to proteolytic degradation, and when properly designed these unnatural oligomers can mimic the protein-recognition properties of analogous "α-peptides". This report documents an extension of the α/β-peptide approach to target intracellular protein-protein interactions. Specifically, we have generated α/β-peptides based on a "stapled" Bim BH3 α-peptide, which contains a hydrocarbon cross-link to enhance α-helix stability. We show that a stapled α/β-peptide can structurally and functionally mimic the parent stapled α-peptide in its ability to enter certain types of cells and block protein-protein interactions associated with apoptotic signaling. However, the α/β-peptide is nearly 100-fold more resistant to proteolysis than is the parent stapled α-peptide. These results show that backbone modification, a strategy that has received relatively little attention in terms of peptide engineering for biomedical applications, can be combined with more commonly deployed peripheral modifications such as side chain cross-linking to produce synergistic benefits.

  2. Mapping and identification of protein-protein interactions by two-dimensional far-Western immunoblotting.

    PubMed

    Pasquali, C; Vilbois, F; Curchod, M L; Hooft van Huijsduijnen, R; Arigoni, F

    2000-10-01

    Studies of protein-protein interactions have proved to be a useful approach to link proteins of unknown function to known cellular processes. In this study we have combined several existing methods to attempt the comprehensive identification of substrates for poorly characterized human protein tyrosine phosphatases (PTPs). We took advantage of so-called "substrate trapping" mutants, a procedure originally described by Flint et al. (Proc. Natl. Acad. Sci. USA 1997, 94, 1680-1685) to identify binding partners of cloned PTPs. This procedure was adapted to a proteome-wide approach to probe for candidate substrates in cellular extracts that were separated by two-dimensional (2-D) gel electrophoresis and blotted onto membranes. Protein-protein interactions were revealed by far-Western immunoblotting and positive binding proteins were subsequently identified from silver-stained gels using tandem mass spectrometry. With this method we were able to identify possible substrates for PTPs without using any radio-labeled cDNA or protein probes and showed that they corresponded to tyrosine phosphorylated proteins. We believe that this method could be generally applied to identify possible protein-protein interactions.

  3. Distinct Mechanism Evolved for Mycobacterial RNA Polymerase and Topoisomerase I Protein-Protein Interaction.

    PubMed

    Banda, Srikanth; Cao, Nan; Tse-Dinh, Yuk-Ching

    2017-09-15

    We report here a distinct mechanism of interaction between topoisomerase I and RNA polymerase in Mycobacterium tuberculosis and Mycobacterium smegmatis that has evolved independently from the previously characterized interaction between bacterial topoisomerase I and RNA polymerase. Bacterial DNA topoisomerase I is responsible for preventing the hyper-negative supercoiling of genomic DNA. The association of topoisomerase I with RNA polymerase during transcription elongation could efficiently relieve transcription-driven negative supercoiling. Our results demonstrate a direct physical interaction between the C-terminal domains of topoisomerase I (TopoI-CTDs) and the β' subunit of RNA polymerase of M. smegmatis in the absence of DNA. The TopoI-CTDs in mycobacteria are evolutionarily unrelated in amino acid sequence and three-dimensional structure to the TopoI-CTD found in the majority of bacterial species outside Actinobacteria, including Escherichia coli. The functional interaction between topoisomerase I and RNA polymerase has evolved independently in mycobacteria and E. coli, with distinctively different structural elements of TopoI-CTD utilized for this protein-protein interaction. Zinc ribbon motifs in E. coli TopoI-CTD are involved in the interaction with RNA polymerase. For M. smegmatis TopoI-CTD, a 27-amino-acid tail that is rich in basic residues at the C-terminal end is responsible for the interaction with RNA polymerase. Overexpression of recombinant TopoI-CTD in M. smegmatis competed with the endogenous topoisomerase I for protein-protein interactions with RNA polymerase. The TopoI-CTD overexpression resulted in decreased survival following treatment with antibiotics and hydrogen peroxide, supporting the importance of the protein-protein interaction between topoisomerase I and RNA polymerase during stress response of mycobacteria. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Sequence Motifs in MADS Transcription Factors Responsible for Specificity and Diversification of Protein-Protein Interaction

    PubMed Central

    van Dijk, Aalt D. J.; Morabito, Giuseppa; Fiers, Martijn; van Ham, Roeland C. H. J.; Angenent, Gerco C.; Immink, Richard G. H.

    2010-01-01

    Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution. PMID

  5. Evolutionary Dynamics of Floral Homeotic Transcription Factor Protein-Protein Interactions.

    PubMed

    Bartlett, Madelaine; Thompson, Beth; Brabazon, Holly; Del Gizzi, Robert; Zhang, Thompson; Whipple, Clinton

    2016-06-01

    Protein-protein interactions (PPIs) have widely acknowledged roles in the regulation of development, but few studies have addressed the timing and mechanism of shifting PPIs over evolutionary history. The B-class MADS-box transcription factors, PISTILLATA (PI) and APETALA3 (AP3) are key regulators of floral development. PI-like (PI(L)) and AP3-like (AP3(L)) proteins from a number of plants, including Arabidopsis thaliana (Arabidopsis) and the grass Zea mays (maize), bind DNA as obligate heterodimers. However, a PI(L) protein from the grass relative Joinvillea can bind DNA as a homodimer. To ascertain whether Joinvillea PI(L) homodimerization is an anomaly or indicative of broader trends, we characterized PI(L) dimerization across the Poales and uncovered unexpected evolutionary lability. Both obligate B-class heterodimerization and PI(L) homodimerization have evolved multiple times in the order, by distinct molecular mechanisms. For example, obligate B-class heterodimerization in maize evolved very recently from PI(L) homodimerization. A single amino acid change, fixed during domestication, is sufficient to toggle one maize PI(L) protein between homodimerization and obligate heterodimerization. We detected a signature of positive selection acting on residues preferentially clustered in predicted sites of contact between MADS-box monomers and dimers, and in motifs that mediate MADS PPI specificity in Arabidopsis. Changing one positively selected residue can alter PI(L) dimerization activity. Furthermore, ectopic expression of a Joinvillea PI(L) homodimer in Arabidopsis can homeotically transform sepals into petals. Our results provide a window into the evolutionary remodeling of PPIs, and show that novel interactions have the potential to alter plant form in a context-dependent manner. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  6. Automatic extraction of biological information from scientific text: protein-protein interactions.

    PubMed

    Blaschke, C; Andrade, M A; Ouzounis, C; Valencia, A

    1999-01-01

    We describe the basic design of a system for automatic detection of protein-protein interactions extracted from scientific abstracts. By restricting the problem domain and imposing a number of strong assumptions which include pre-specified protein names and a limited set of verbs that represent actions, we show that it is possible to perform accurate information extraction. The performance of the system is evaluated with different cases of real-world interaction networks, including the Drosophila cell cycle control. The results obtained computationally are in good agreement with current biological knowledge and demonstrate the feasibility of developing a fully automated system able to describe networks of protein interactions with sufficient accuracy.

  7. The visible touch: in planta visualization of protein-protein interactions by fluorophore-based methods

    PubMed Central

    Bhat, Riyaz A; Lahaye, Thomas; Panstruga, Ralph

    2006-01-01

    Non-invasive fluorophore-based protein interaction assays like fluorescence resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC, also referred to as "split YFP") have been proven invaluable tools to study protein-protein interactions in living cells. Both methods are now frequently used in the plant sciences and are likely to develop into standard techniques for the identification, verification and in-depth analysis of polypeptide interactions. In this review, we address the individual strengths and weaknesses of both approaches and provide an outlook about new directions and possible future developments for both techniques. PMID:16800872

  8. Studying Protein-Protein Interactions In Planta Using Advanced Fluorescence Microscopy.

    PubMed

    Somssich, Marc; Simon, Rüdiger

    2017-01-01

    The formation of protein complexes through direct protein-protein interaction is essential for virtually every biological process, and accordingly the ability to determine the interaction properties of specific proteins is important to understand these processes. Förster resonance energy transfer (FRET) measurements are state-of-the-art confocal fluorescence microscopy- and imaging-based techniques that allow the analysis of protein interactions in vivo and in planta, in specific compartments of single cells or tissues. Here we provide a step-by-step guide to perform FRET measurements by acceptor photobleaching (APB) and fluorescence lifetime imaging microscopy (FLIM) in the plant expression system Nicotiana benthamiana.

  9. False positive reduction in protein-protein interaction predictions using gene ontology annotations.

    PubMed

    Mahdavi, Mahmoud A; Lin, Yen-Han

    2007-07-23

    Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives

  10. A conserved patch of hydrophobic amino acids modulates Myb activity by mediating protein-protein interactions.

    PubMed

    Dukare, Sandeep; Klempnauer, Karl-Heinz

    2016-07-01

    The transcription factor c-Myb plays a key role in the control of proliferation and differentiation in hematopoietic progenitor cells and has been implicated in the development of leukemia and certain non-hematopoietic tumors. c-Myb activity is highly dependent on the interaction with the coactivator p300 which is mediated by the transactivation domain of c-Myb and the KIX domain of p300. We have previously observed that conservative valine-to-isoleucine amino acid substitutions in a conserved stretch of hydrophobic amino acids have a profound effect on Myb activity. Here, we have explored the function of the hydrophobic region as a mediator of protein-protein interactions. We show that the hydrophobic region facilitates Myb self-interaction and binding of the histone acetyl transferase Tip60, a previously identified Myb interacting protein. We show that these interactions are affected by the valine-to-isoleucine amino acid substitutions and suppress Myb activity by interfering with the interaction of Myb and the KIX domain of p300. Taken together, our work identifies the hydrophobic region in the Myb transactivation domain as a binding site for homo- and heteromeric protein interactions and leads to a picture of the c-Myb transactivation domain as a composite protein binding region that facilitates interdependent protein-protein interactions of Myb with regulatory proteins. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

    PubMed

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

    2017-01-01

    Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

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

    SciTech Connect

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

    2015-07-31

    Biofuels can be produced from a variety of plant feedstocks. The value of a particular feedstock for biofuels production depends in part on the degree of difficulty associated with the extraction of fermentable sugars from the plant biomass. The wood of trees is potentially a rich source fermentable sugars. However, the sugars in wood exist in a tightly cross-linked matrix of cellulose, hemicellulose, and lignin, making them largely recalcitrant to release and fermentation for biofuels production. Before breeders and genetic engineers can effectively develop plants with reduced recalcitrance to fermentation, it is necessary to gain a better understanding of the fundamental biology of the mechanisms responsible for wood formation. Regulatory, structural, and enzymatic proteins are required for the complicated process of wood formation. To function properly, proteins must interact with other proteins. Yet, very few of the protein-protein interactions necessary for wood formation are known. The main objectives of this project were to 1) identify new protein-protein interactions relevant to wood formation, and 2) perform in-depth characterizations of selected protein-protein interactions. To identify relevant protein-protein interactions, we cloned a set of approximately 400 genes that were highly expressed in the wood-forming tissue (known as secondary xylem) of poplar (Populus trichocarpa). We tested whether the proteins encoded by these biomass genes interacted with each other in a binary matrix design using the yeast two-hybrid (Y2H) method for protein-protein interaction discovery. We also tested a subset of the 400 biomass proteins for interactions with all proteins present in wood-forming tissue of poplar in a biomass library screen design using Y2H. Together, these two Y2H screens yielded over 270 interactions involving over 75 biomass proteins. For the second main objective we selected several interacting pairs or groups of interacting proteins for in

  13. BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

    PubMed

    Wei, Qing; La, David; Kihara, Daisuke

    2017-01-01

    Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

  14. Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

    PubMed

    Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

    2015-01-01

    Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

  15. Surface energetics and protein-protein interactions: analysis and mechanistic implications

    NASA Astrophysics Data System (ADS)

    Peri, Claudio; Morra, Giulia; Colombo, Giorgio

    2016-04-01

    Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

  16. Using singular value decomposition to characterize protein-protein interactions by in-cell NMR spectroscopy.

    PubMed

    Majumder, Subhabrata; DeMott, Christopher M; Burz, David S; Shekhtman, Alexander

    2014-05-05

    Distinct differences between how model proteins interact in-cell and in vitro suggest that the cytosol might have a profound effect in modulating protein-protein and/or protein-ligand interactions that are not observed in vitro. Analyses of in-cell NMR spectra of target proteins interacting with physiological partners are further complicated by low signal-to-noise ratios, and the long overexpression times used in protein-protein interaction studies may lead to changes in the in-cell spectra over the course of the experiment. To unambiguously resolve the principal binding mode between two interacting species against the dynamic cellular background, we analyzed in-cell spectral data of a target protein over the time course of overexpression of its interacting partner by using single-value decomposition (SVD). SVD differentiates between concentration-dependent and concentration-independent events and identifies the principal binding mode between the two species. The analysis implicates a set of amino acids involved in the specific interaction that differs from previous NMR analyses but is in good agreement with crystallographic data. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets*

    PubMed Central

    Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

    2011-01-01

    We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not

  18. Categorizing biases in high-confidence high-throughput protein-protein interaction data sets.

    PubMed

    Yu, Xueping; Ivanic, Joseph; Memisević, Vesna; Wallqvist, Anders; Reifman, Jaques

    2011-12-01

    We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not

  19. Signal transduction meets systems biology: deciphering specificity determinants for protein-protein interactions

    PubMed Central

    Bourret, Robert B.

    2008-01-01

    Two recent papers [Gao et al. Mol. Microbiol. 69, 1358 (2008); Skerker et al. Cell 133, 1043 (2008)] describe investigations into the specificity of protein-protein interactions that occur during signal transduction by two-component regulatory systems. This MicroCommentary summarizes and provides context for the reported findings. The results offer insights into molecular determinants that provide specificity to maintain signal separation and thus prevent deleterious crosstalk between pathways, as well as the potential extent and nature of interactions that may combine signals to achieve beneficial cross regulation among pathways. The methods employed are suitable for application to other systems. PMID:18694439

  20. RNA polymerase II conserved protein domains as platforms for protein-protein interactions

    PubMed Central

    García-López, M Carmen

    2011-01-01

    RNA polymerase II establishes many protein-protein interactions with transcriptional regulators to coordinate gene expression, but little is known about protein domains involved in the contact with them. We use a new approach to look for conserved regions of the RNA pol II of S. cerevisiae located at the surface of the structure of the complex, hypothesizing that they might be involved in the interaction with transcriptional regulators. We defined five different conserved domains and demonstrate that all of them make contact with transcriptional regulators. PMID:21922063

  1. PPI layouts: BioJS components for the display of Protein-Protein Interactions

    PubMed Central

    Salazar, Gustavo A.; Meintjes, Ayton; Mulder, Nicola

    2014-01-01

    Summary: We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. Availability: http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753 PMID:25075288

  2. PPI layouts: BioJS components for the display of Protein-Protein Interactions.

    PubMed

    Salazar, Gustavo A; Meintjes, Ayton; Mulder, Nicola

    2014-01-01

    We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.

  3. Prediction of the Ebola virus infection related human genes using protein-protein interaction network.

    PubMed

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

    2017-03-10

    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.

  4. Efficient fold-change detection based on protein-protein interactions.

    PubMed

    Buijsman, W; Sheinman, M

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  5. A Contingent Replication Assay for the Detection of Protein-Protein Interactions in Animal Cells

    NASA Astrophysics Data System (ADS)

    Vasavada, Haren A.; Ganguly, Subinay; Germino, F. Joseph; Wang, Zhen Xi; Weissman, Sherman M.

    1991-12-01

    We have developed a sensitive and rapid assay system termed the contingent replication assay (CRA) for selecting cDNAs with desired functional properties from a cDNA library. The system functions in animal cells and permits enrichment of the desired cDNA in small-scale and convenient experiments. The assay can be used for the enrichment of proteins that activate transcription from conditional enhancers, bind to specific DNA sequences, or interact with target proteins of interest. In this communication we report the application of this assay to study protein-protein interactions in animal cells.

  6. Effective protein-protein interaction from structure factor data of a lysozyme solution

    SciTech Connect

    Abramo, M. C.; Caccamo, C.; Costa, D.; Ruberto, R.; Wanderlingh, U.; Cavero, M.; Pellicane, G.

    2013-08-07

    We report the determination of an effective protein-protein central potential for a lysozyme solution, obtained from the direct inversion of the total structure factor of the system, as extracted from small angle neutron scattering. The inversion scheme rests on a hypernetted-chain relationship between the effective potential and the structural functions, and is preliminarily tested for the case of a Lennard-Jones interaction. The characteristics of our potential are discussed in comparison with current models of effective interactions in complex fluids. The phase behavior predictions are also investigated.

  7. [Brownian dynamics simulations of protein-protein interactions in photosynthetic electron transport chain].

    PubMed

    Khruschev, S S; Abaturova, A M; Diakonova, A N; Fedorov, V A; Ustinin, D M; Kovalenko, I B; Riznichenko, G Yu; Rubin, A B

    2015-01-01

    The application of Brownian dynamics for simulation of transient protein-protein interactions is reviewed. The review focuses on theoretical basics of Brownian dynamics method, its particular implementations, advantages and drawbacks of the method. The outlook for future development of Brownian dynamics-based simulation techniques is discussed. Special attention is given to analysis of Brownian dynamics trajectories. The second part of the review is dedicated to the role of Brownian dynamics simulations in studying photosynthetic electron transport. Interactions of mobile electron carriers (plastocyanin, cytochrome c6, and ferredoxin) with their reaction partners (cytochrome b6f complex, photosystem I, ferredoxin:NADP-reductase, and hydrogenase) are considered.

  8. Efficient fold-change detection based on protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Buijsman, W.; Sheinman, M.

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  9. Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions

    PubMed Central

    Xing, Chuanhua; Dunson, David B.

    2011-01-01

    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility. PMID:21829334

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

    PubMed

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

    2014-01-01

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

  11. Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

    PubMed

    Xing, Chuanhua; Dunson, David B

    2011-07-01

    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.

  12. Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks

    PubMed Central

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

    2014-01-01

    In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph model by integrating fuzzy sets and rough sets, employs the upper and lower approximations of rough sets to deal with overlapping complexes, and calculates the number of complexes automatically. Fuzzy relation between proteins is established and then transformed into fuzzy equivalence relation. Non-overlapping complexes correspond to equivalence classes satisfying certain equivalence relation. To obtain overlapping complexes, we calculate the similarity between one protein and each complex, and then determine whether the protein belongs to one or multiple complexes by computing the ratio of each similarity to maximum similarity. To validate RFC quantitatively, we test it in Gavin, Collins, Krogan and BioGRID datasets. Experiment results show that there is a good correspondence to reference complexes in MIPS and SGD databases. Then we compare RFC with several previous methods, including ClusterONE, CMC, MCL, GCE, OSLOM and CFinder. Results show the precision, sensitivity and separation are 32.4%, 42.9% and 81.9% higher than mean of the five methods in four weighted networks, and are 0.5%, 11.2% and 66.1% higher than mean of the six methods in five unweighted networks. Our method RFC works well for protein complexes detection and provides a new insight of network division, and it can also be applied to identify overlapping community structure in social networks and LFR benchmark networks. PMID:24642838

  13. Protein-protein interactions in the regulation of WRKY transcription factors.

    PubMed

    Chi, Yingjun; Yang, Yan; Zhou, Yuan; Zhou, Jie; Fan, Baofang; Yu, Jing-Quan; Chen, Zhixiang

    2013-03-01

    It has been almost 20 years since the first report of a WRKY transcription factor, SPF1, from sweet potato. Great progress has been made since then in establishing the diverse biological roles of WRKY transcription factors in plant growth, development, and responses to biotic and abiotic stress. Despite the functional diversity, almost all analyzed WRKY proteins recognize the TTGACC/T W-box sequences and, therefore, mechanisms other than mere recognition of the core W-box promoter elements are necessary to achieve the regulatory specificity of WRKY transcription factors. Research over the past several years has revealed that WRKY transcription factors physically interact with a wide range of proteins with roles in signaling, transcription, and chromatin remodeling. Studies of WRKY-interacting proteins have provided important insights into the regulation and mode of action of members of the important family of transcription factors. It has also emerged that the slightly varied WRKY domains and other protein motifs conserved within each of the seven WRKY subfamilies participate in protein-protein interactions and mediate complex functional interactions between WRKY proteins and between WRKY and other regulatory proteins in the modulation of important biological processes. In this review, we summarize studies of protein-protein interactions for WRKY transcription factors and discuss how the interacting partners contribute, at different levels, to the establishment of the complex regulatory and functional network of WRKY transcription factors.

  14. Screening for in planta protein-protein interactions combining bimolecular fluorescence complementation with flow cytometry

    PubMed Central

    2012-01-01

    Understanding protein and gene function requires identifying interaction partners using biochemical, molecular or genetic tools. In plants, searching for novel protein-protein interactions is limited to protein purification assays, heterologous in vivo systems such as the yeast-two-hybrid or mutant screens. Ideally one would be able to search for novel protein partners in living plant cells. We demonstrate that it is possible to screen for novel protein-protein interactions from a random library in protoplasted Arabidopsis plant cells and recover some of the interacting partners. Our screen is based on capturing the bi-molecular complementation of mYFP between an YN-bait fusion partner and a completely random prey YC-cDNA library with FACS. The candidate interactions were confirmed using in planta BiFC assays and in planta FRET-FLIM assays. From this work, we show that the well characterized protein Calcium Dependent Protein Kinase 3 (CPK3) interacts with APX3, HMGB5, ORP2A and a ricin B-related lectin domain containing protein At2g39050. This is one of the first randomin planta screens to be successfully employed. PMID:22789293

  15. Improving the prediction of yeast protein function using weighted protein-protein interactions

    PubMed Central

    2011-01-01

    Background Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins. Results A new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior. Conclusions A new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations. PMID:21524280

  16. Screening for in planta protein-protein interactions combining bimolecular fluorescence complementation with flow cytometry.

    PubMed

    Berendzen, Kenneth Wayne; Böhmer, Maik; Wallmeroth, Niklas; Peter, Sébastien; Vesić, Marko; Zhou, Ying; Tiesler, Franziska Katharina Elisabeth; Schleifenbaum, Frank; Harter, Klaus

    2012-07-12

    Understanding protein and gene function requires identifying interaction partners using biochemical, molecular or genetic tools. In plants, searching for novel protein-protein interactions is limited to protein purification assays, heterologous in vivo systems such as the yeast-two-hybrid or mutant screens. Ideally one would be able to search for novel protein partners in living plant cells. We demonstrate that it is possible to screen for novel protein-protein interactions from a random library in protoplasted Arabidopsis plant cells and recover some of the interacting partners. Our screen is based on capturing the bi-molecular complementation of mYFP between an YN-bait fusion partner and a completely random prey YC-cDNA library with FACS. The candidate interactions were confirmed using in planta BiFC assays and in planta FRET-FLIM assays. From this work, we show that the well characterized protein Calcium Dependent Protein Kinase 3 (CPK3) interacts with APX3, HMGB5, ORP2A and a ricin B-related lectin domain containing protein At2g39050. This is one of the first randomin planta screens to be successfully employed.

  17. Energy Landscape of All-Atom Protein-Protein Interactions Revealed by Multiscale Enhanced Sampling

    PubMed Central

    Moritsugu, Kei; Terada, Tohru; Kidera, Akinori

    2014-01-01

    Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES) simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape. PMID:25340714

  18. Energy landscape of all-atom protein-protein interactions revealed by multiscale enhanced sampling.

    PubMed

    Moritsugu, Kei; Terada, Tohru; Kidera, Akinori

    2014-10-01

    Protein-protein interactions are regulated by a subtle balance of complicated atomic interactions and solvation at the interface. To understand such an elusive phenomenon, it is necessary to thoroughly survey the large configurational space from the stable complex structure to the dissociated states using the all-atom model in explicit solvent and to delineate the energy landscape of protein-protein interactions. In this study, we carried out a multiscale enhanced sampling (MSES) simulation of the formation of a barnase-barstar complex, which is a protein complex characterized by an extraordinary tight and fast binding, to determine the energy landscape of atomistic protein-protein interactions. The MSES adopts a multicopy and multiscale scheme to enable for the enhanced sampling of the all-atom model of large proteins including explicit solvent. During the 100-ns MSES simulation of the barnase-barstar system, we observed the association-dissociation processes of the atomistic protein complex in solution several times, which contained not only the native complex structure but also fully non-native configurations. The sampled distributions suggest that a large variety of non-native states went downhill to the stable complex structure, like a fast folding on a funnel-like potential. This funnel landscape is attributed to dominant configurations in the early stage of the association process characterized by near-native orientations, which will accelerate the native inter-molecular interactions. These configurations are guided mostly by the shape complementarity between barnase and barstar, and lead to the fast formation of the final complex structure along the downhill energy landscape.

  19. Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis

    NASA Astrophysics Data System (ADS)

    Pattin, Kristine A.; Payne, Joshua L.; Hill, Douglas P.; Caldwell, Thomas; Fisher, Jonathan M.; Moore, Jason H.

    The etiology of common human disease often involves a complex genetic architecture, where numerous points of genetic variation interact to influence disease susceptibility. Automating the detection of such epistatic genetic risk factors poses a major computational challenge, as the number of possible gene-gene interactions increases combinatorially with the number of sequence variations. Previously, we addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods. Our results demonstrated that CES is capable of efficiently navigating these large and rugged epistatic landscapes toward the discovery of biologically meaningful genetic models of disease predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge. We anticipate that biological expert knowledge, such as genetic regulatory or protein-protein interaction maps, will provide complementary information, and further improve the ability of CES to model the genetic architectures of common human disease. The goal of this study is to test this hypothesis, utilizing publicly available protein-protein interaction information. We show that by incorporating this source of expert knowledge, the system is able to identify functional interactions that represent more concise models of disease susceptibility with improved accuracy. Our ability to incorporate biological knowledge into learning algorithms is an essential step toward the routine use of methods such as CES for identifying genetic risk factors for common human diseases.

  20. Dissecting protein:protein interactions between transcription factors with an RNA aptamer.

    PubMed Central

    Tian, Y; Adya, N; Wagner, S; Giam, C Z; Green, M R; Ellington, A D

    1995-01-01

    Nucleic acid aptamers isolated from random sequence pools have generally proven useful at inhibiting the interactions of nucleic acid binding proteins with their cognate nucleic acids. In order to develop reagents that could also be used to study protein:protein interactions, we have used in vitro selection to search for RNA aptamers that could interact with the transactivating protein Tax from human T-cell leukemia virus. Tax does not normally bind to nucleic acids, but instead stimulates transcription by interacting with a variety of cellular transcription factors, including the cyclic AMP-response element binding protein (CREB), NF-kappa B, and the serum response factor (SRF). Starting from a pool of greater than 10(13) different RNAs with a core of 120 random sequence positions, RNAs were selected for their ability to be co-retained on nitrocellulose filters with Tax. After five cycles of selection and amplification, a single nucleic acid species remained. This aptamer was found to bind Tax with high affinity and specificity, and could disrupt complex formation between Tax and NF-kappa B, but not with SRF. The differential effects of our aptamer probe on protein:protein interactions suggest a model for how the transcription factor binding sites on the surface of the Tax protein are organized. This model is consistent with data from a variety of other studies. PMID:7489503

  1. Dataset of integrin-linked kinase protein: Protein interactions in cardiomyocytes identified by mass spectrometry.

    PubMed

    Traister, Alexandra; Lu, Mingliang; Coles, John G; Maynes, Jason T

    2016-06-01

    Using hearts from mice overexpressing integrin linked kinase (ILK) behind the cardiac specific promoter αMHC, we have performed immunoprecipitation and mass spectrometry to identify novel ILK protein:protein interactions that regulate cardiomyocyte activity and calcium flux. Integrin linked kinase complexes were captured from mouse heart lysates using a commercial antibody, with subsequent liquid chromatography tandem mass spectral analysis. Interacting partners were identified using the MASCOT server, and important interactions verified using reverse immunoprecipitation and mass spectrometry. All ILK interacting proteins were identified in a non-biased manner, and are stored in the ProteomeXchange Consortium via the PRIDE partner repository (reference ID PRIDE: PXD001053). The functional role of identified ILK interactions in cardiomyocyte function and arrhythmia were subsequently confirmed in human iPSC-cardiomyocytes.

  2. Identification of protein-protein interactions of the occlusion-derived virus-associated proteins of Helicoverpa armigera nucleopolyhedrovirus.

    PubMed

    Peng, Ke; Wu, Minzhi; Deng, Fei; Song, Jingjiao; Dong, Chunsheng; Wang, Hualin; Hu, Zhihong

    2010-03-01

    The purpose of this study was to identify protein-protein interactions among the components of the occlusion-derived virus (ODV) of Helicoverpa armigera nucleopolyhedrovirus (HearNPV), a group II alphabaculovirus in the family Baculoviridae. To achieve this, 39 selected genes of potential ODV structural proteins were cloned and expressed in the Gal4 yeast two-hybrid (Y2H) system. The direct-cross Y2H assays identified 22 interactions comprising 13 binary interactions [HA9-ODV-EC43, ODV-E56-38K, ODV-E56-PIF3, LEF3-helicase, LEF3-alkaline nuclease (AN), GP41-38K, GP41-HA90, 38K-PIF3, 38K-PIF2, VP80-HA100, ODV-E66-PIF3, ODV-E66-PIF2 and PIF3-PIF2] and nine self-associations (IE1, HA44, LEF3, HA66, GP41, CG30, 38K, PIF3 and P24). Five of these interactions - LEF3-helicase and LEF3-AN, and the self-associations of IE1, LEF3 and 38K - have been reported previously in Autographa californica multiple nucleopolyhedrovirus. As HA44 and HA100 were two newly identified ODV proteins of group II viruses, their interactions were further confirmed. The self-association of HA44 was verified with a His pull-down assay and the interaction of VP80-HA100 was confirmed by a co-immunoprecipitation assay. A summary of the protein-protein interactions of baculoviruses reported so far, comprising 68 interactions with 45 viral proteins and five host proteins, is presented, which will facilitate our understanding of the molecular mechanisms of baculovirus infection.

  3. A method for investigating protein-protein interactions related to Salmonella typhimurium pathogenesis

    SciTech Connect

    Chowdhury, Saiful M.; Shi, Liang; Yoon, Hyunjin; Ansong, Charles; Rommereim, Leah M.; Norbeck, Angela D.; Auberry, Kenneth J.; Moore, R. J.; Adkins, Joshua N.; Heffron, Fred; Smith, Richard D.

    2009-02-10

    We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella typhimurium (STM). This method includes i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques; ii) in vivo cross-linking with formaldehyde; iii) tandem affinity purification of bait proteins under fully denaturing conditions; and iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of non-cross-linked proteins to bait proteins. Two different negative controls were employed to reduce false-positive identification. In an initial demonstration of this approach, we tagged three selected STM proteins- HimD, PduB and PhoP- with known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified with each bait protein, including the known binding partners such as HimA for HimD, as well as anticipated and unexpected binding partners. Our results suggest that novel protein-protein interactions may be critical to pathogenesis by Salmonella typhimurium. .

  4. Detection of protein-protein interactions using Aequorea victoria bioluminescence resonance energy transfer

    NASA Astrophysics Data System (ADS)

    Vinokurov, Leonid M.; Gorokhovatsky, Andrey Y.; Rudenko, Natalia V.; Marchenkov, Victor V.; Skosyrev, Vitaly S.; Arzhanov, Maxim A.; Zakharov, Mikhail V.; Burkhardt, Nils; Semisotnov, Gennady V.; Alakhov, Yuli B.

    2003-07-01

    Bioluminescence resonance energy transfer (BRET) is a naturally occurring phenomenon taking place in some marine coelenterates. Emission of light in these organisms involves the energy transfer between chromophores of donor luciferase and acceptor fluorescent protein. Due to the strict dependence of BRET efficiency on the inter-chromophore distance, the phenomenon has been applied to study protein-protein interactions by fusing interacting partners with either donor or acceptor proteins. Here we describe a BRET-based homogeneous protein-protein interaction assay exploiting novel donor-acceptor pair formed by photoproteins of jellyfish Aequorea victoria bioluminescent system, aequorin and green fluorescent protein enhanced variant (EGFP). Two known interacting proteins, streptavidin (SAV) and biotin carboxyl carrier protein (BCCP) were fused, respectively, with aequorin and EGFP. The fusions were purified after expression of the corresponding genes in Escherichia coli cells. Association of SAV-Aequorin and BCCP-EGFP was followed by BRET between aequorin (donor) and EGFP (acceptor) resulting in significantly increasing 510 nm and decreasing 470 nm bioluminescence intensity. It was shown that free biotin inhibited BRET due to its competition with BCCP-EGFP for binding to SAV-Aequorin. These properties were exploited to demonstrate competitive homogeneous BRET assay for biotin.

  5. Uncovering viral protein-protein interactions and their role in arenavirus life cycle.

    PubMed

    Loureiro, Maria Eugenia; D'Antuono, Alejandra; Levingston Macleod, Jesica M; López, Nora

    2012-09-01

    The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein) and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article.

  6. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    PubMed

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-09-01

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017. Published by Elsevier Ltd.

  7. Novel protein-protein interaction family proteins involved in chloroplast movement response.

    PubMed

    Kodama, Yutaka; Suetsugu, Noriyuki; Wada, Masamitsu

    2011-04-01

    To optimize photosynthetic activity, chloroplasts change their intracellular location in response to ambient light conditions; chloroplasts move toward low intensity light to maximize light capture, and away from high intensity light to avoid photodamage. Although several proteins have been reported to be involved in the chloroplast photorelocation movement response, any physical interaction among them was not found so far. We recently found a physical interaction between two plant-specific coiled-coil proteins, WEB1 (Weak Chloroplast Movement under Blue Light 1) and PMI2 (Plastid Movement Impaired 2), that were identified to regulate chloroplast movement velocity. Since the both coiled-coil regions of WEB1 and PMI2 were classified into an uncharacterized protein family having DUF827 (DUF: Domain of Unknown Function) domain, it was the first report that DUF827 proteins could mediate protein-protein interaction. In this mini-review article, we discuss regarding molecular function of WEB1 and PMI2, and also define a novel protein family composed of WEB1, PMI2 and WEB1/PMI2-like proteins for protein-protein interaction in land plants.

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

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

    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.

  9. Ensemble learning prediction of protein-protein interactions using proteins functional annotations.

    PubMed

    Saha, Indrajit; Zubek, Julian; Klingström, Tomas; Forsberg, Simon; Wikander, Johan; Kierczak, Marcin; Maulik, Ujjwal; Plewczynski, Dariusz

    2014-04-01

    Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at .

  10. Uncovering Viral Protein-Protein Interactions and their Role in Arenavirus Life Cycle

    PubMed Central

    Loureiro, Maria Eugenia; D’Antuono, Alejandra; Levingston Macleod, Jesica M.; López, Nora

    2012-01-01

    The Arenaviridae family includes widely distributed pathogens that cause severe hemorrhagic fever in humans. Replication and packaging of their single-stranded RNA genome involve RNA recognition by viral proteins and a number of key protein-protein interactions. Viral RNA synthesis is directed by the virus-encoded RNA dependent-RNA polymerase (L protein) and requires viral RNA encapsidation by the Nucleoprotein. In addition to the role that the interaction between L and the Nucleoprotein may have in the replication process, polymerase activity appears to be modulated by the association between L and the small multifunctional Z protein. Z is also a structural component of the virions that plays an essential role in viral morphogenesis. Indeed, interaction of the Z protein with the Nucleoprotein is critical for genome packaging. Furthermore, current evidence suggests that binding between Z and the viral envelope glycoprotein complex is required for virion infectivity, and that Z homo-oligomerization is an essential step for particle assembly and budding. Efforts to understand the molecular basis of arenavirus life cycle have revealed important details on these viral protein-protein interactions that will be reviewed in this article. PMID:23170177

  11. Phthalic Acid Chemical Probes Synthesized for Protein-Protein Interaction Analysis

    PubMed Central

    Liang, Shih-Shin; Liao, Wei-Ting; Kuo, Chao-Jen; Chou, Chi-Hsien; Wu, Chin-Jen; Wang, Hui-Min

    2013-01-01

    Plasticizers are additives that are used to increase the flexibility of plastic during manufacturing. However, in injection molding processes, plasticizers cannot be generated with monomers because they can peel off from the plastics into the surrounding environment, water, or food, or become attached to skin. Among the various plasticizers that are used, 1,2-benzenedicarboxylic acid (phthalic acid) is a typical precursor to generate phthalates. In addition, phthalic acid is a metabolite of diethylhexyl phthalate (DEHP). According to Gene_Ontology gene/protein database, phthalates can cause genital diseases, cardiotoxicity, hepatotoxicity, nephrotoxicity, etc. In this study, a silanized linker (3-aminopropyl triethoxyslane, APTES) was deposited on silicon dioxides (SiO2) particles and phthalate chemical probes were manufactured from phthalic acid and APTES–SiO2. These probes could be used for detecting proteins that targeted phthalic acid and for protein-protein interactions. The phthalic acid chemical probes we produced were incubated with epithelioid cell lysates of normal rat kidney (NRK-52E cells) to detect the interactions between phthalic acid and NRK-52E extracted proteins. These chemical probes interacted with a number of chaperones such as protein disulfide-isomerase A6, heat shock proteins, and Serpin H1. Ingenuity Pathways Analysis (IPA) software showed that these chemical probes were a practical technique for protein-protein interaction analysis. PMID:23797655

  12. Essential protein identification based on essential protein-protein interaction prediction by Integrated Edge Weights.

    PubMed

    Jiang, Yuexu; Wang, Yan; Pang, Wei; Chen, Liang; Sun, Huiyan; Liang, Yanchun; Blanzieri, Enrico

    2015-07-15

    Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein-protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein-protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision-recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein-protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms' essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.

  13. HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1*

    PubMed Central

    Hernandez, Anna; Buch, Anna; Sodeik, Beate; Cristea, Ileana Mihaela

    2016-01-01

    Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of

  14. Sugar recognition and protein-protein interaction of mammalian lectins conferring diverse functions.

    PubMed

    Nagae, Masamichi; Yamaguchi, Yoshiki

    2015-10-01

    Recent advances in structural analyses of mammalian lectins reveal atomic-level details of their fine specificities toward diverse endogenous and exogenous glycans. Local variations on a common scaffold can enable certain lectins to recognize complex carbohydrate ligands including branched glycans and O-glycosylated peptides. Simultaneous recognition of both glycan and the aglycon moieties enhances the affinity and specificity of lectins such as CLEC-2 and PILRα. Attention has been paid to the roles of galectin and RegIII family of proteins in protein-protein interactions involved in critical biological functions including signal transduction and bactericidal pore formation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Protein-protein interactions and human cellular cofactors as new targets for HIV therapy.

    PubMed

    Tintori, Cristina; Brai, Annalaura; Fallacara, Anna Lucia; Fazi, Roberta; Schenone, Silvia; Botta, Maurizio

    2014-10-01

    Two novel approaches for the development of new drugs against AIDS are summarized each leading to the achievement of important discoveries in anti-HIV therapy. Despite the success of HAART in reducing mortality, resistant strains continue to emerge in the clinic, underscoring the importance of developing next-generation drugs. Protein-protein interactions and human cellular cofactors represent the new targets of tomorrow in HIV research. The most relevant results obtained in the last few years by the two new strategies are described herein. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. A parallel edge-betweenness clustering tool for Protein-Protein Interaction networks.

    PubMed

    Yang, Qiaofeng; Lonardi, Stefano

    2007-01-01

    The increasing availability of protein-protein interaction graphs (PPI) requires new efficient tools capable of extracting valuable biological knowledge from these networks. Among the wide range of clustering algorithms, Girvan and Newman's edge betweenness algorithm showed remarkable performances in discovering clustering structures in several real-world networks. Unfortunately, their algorithm suffers from high computational cost and it is impractical for inputs of the size of large PPI networks. Here we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that achieves almost linear speed-up for up to 32 processors. The tool is available in the public domain from the authors' website.

  17. Evolutionary Capacitance and Control of Protein Stability in Protein-Protein Interaction Networks

    PubMed Central

    Dixit, Purushottam D.; Maslov, Sergei

    2013-01-01

    In addition to their biological function, protein complexes reduce the exposure of the constituent proteins to the risk of undesired oligomerization by reducing the concentration of the free monomeric state. We interpret this reduced risk as a stabilization of the functional state of the protein. We estimate that protein-protein interactions can account for of additional stabilization; a substantial contribution to intrinsic stability. We hypothesize that proteins in the interaction network act as evolutionary capacitors which allows their binding partners to explore regions of the sequence space which correspond to less stable proteins. In the interaction network of baker's yeast, we find that statistically proteins that receive higher energetic benefits from the interaction network are more likely to misfold. A simplified fitness landscape wherein the fitness of an organism is inversely proportional to the total concentration of unfolded proteins provides an evolutionary justification for the proposed trends. We conclude by outlining clear biophysical experiments to test our predictions. PMID:23592969

  18. Target validation and drug discovery using genomic and protein-protein interaction technologies.

    PubMed

    Pillutla, Renuka C; Fisher, Paul B; Blume, Arthur J; Goldstein, Neil I

    2002-08-01

    After the successful completion of the human genome project, mapping of the human proteome has become the next important challenge facing the biotech and pharmaceutical industries. Identification of the 'right' target(s) is now a critical part of the process because of the cost of drug discovery. Compounding this situation is the fact that the pharmaceutical industry faces a further challenge of being able to sustain current and historical growth rates. Hence, the discovery of new drug targets is important for developing new drug leads that can become preclinical drug candidates. Proteomics is the next phase of the effort whereby the human genome can be understood. However, mapping the human proteome presents a daunting challenge. Proteomics involves several essential components with the most significant being the discovery and description of all protein-protein interactions. Once this compendium is available, a secondary and equally important initiative will be to decipher proteins that are differentially expressed in any given disease condition. At this point, the critical focus will be to select the most relevant proteins, understand their partner interactions and then further winnow them to the point where they are relevant pharmaceutical target candidates. This paradigm can be compared to finding the relevant 'needle in the proteome haystack'. This review describes the use of genomic and protein-protein interaction technologies to identify and validate these 'needles' as the first step in the drug discovery process.

  19. 3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

    PubMed

    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.

  20. Investigation of the pH-dependence of dye-doped protein-protein interactions.

    PubMed

    Nudelman, Roman; Gloukhikh, Ekaterina; Rekun, Antonina; Richter, Shachar

    2016-11-01

    Proteins can dramatically change their conformation under environmental conditions such as temperature and pH. In this context, Glycoprotein's conformational determination is challenging. This is due to the variety of domains which contain rich chemical characters existing within this complex. Here we demonstrate a new, straightforward and efficient technique that uses the pH-dependent properties of dyes-doped Pig Gastric Mucin (PGM) for predicting and controlling protein-protein interaction and conformation. We utilize the PGM as natural host matrix which is capable of dynamically changing its conformational shape and adsorbing hydrophobic and hydrophilic dyes under different pH conditions and investigate and control the fluorescent properties of these composites in solution. It is shown at various pH conditions, a large variety of light emission from these complexes such as red, green and white is obtained. This phenomenon is explained by pH-dependent protein folding and protein-protein interactions that induce different emission spectra which are mediated and controlled by means of dye-dye interactions and surrounding environment. This process is used to form the technologically challenging white light-emitting liquid or solid coating for LED devices. © 2016 The Protein Society.

  1. Design, synthesis, and evaluation of an alpha-helix mimetic library targeting protein-protein interactions.

    PubMed

    Shaginian, Alex; Whitby, Landon R; Hong, Sukwon; Hwang, Inkyu; Farooqi, Bilal; Searcey, Mark; Chen, Jiandong; Vogt, Peter K; Boger, Dale L

    2009-04-22

    The design and solution-phase synthesis of an alpha-helix mimetic library as an integral component of a small-molecule library targeting protein-protein interactions are described. The iterative design, synthesis, and evaluation of the candidate alpha-helix mimetic was initiated from a precedented triaryl template and refined by screening the designs for inhibition of MDM2/p53 binding. Upon identifying a chemically and biologically satisfactory design and consistent with the screening capabilities of academic collaborators, the corresponding complete library was assembled as 400 mixtures of 20 compounds (20 x 20 x 20-mix), where the added subunits are designed to mimic all possible permutations of the naturally occurring i, i + 4, i + 7 amino acid side chains of an alpha-helix. The library (8000 compounds) was prepared using a solution-phase synthetic protocol enlisting acid/base liquid-liquid extractions for purification on a scale that insures its long-term availability for screening campaigns. Screening of the library for inhibition of MDM2/p53 binding not only identified the lead alpha-helix mimetic upon which the library was based, but also suggests that a digestion of the initial screening results that accompany the use of such a comprehensive library can provide insights into the nature of the interaction (e.g., an alpha-helix mediated protein-protein interaction) and define the key residues and their characteristics responsible for recognition.

  2. Two-hybrid system for characterization of protein-protein interactions in E. coli.

    PubMed

    Hays, L B; Chen, Y S; Hu, J C

    2000-08-01

    The yeast two-hybrid system has been used to characterize many protein-protein interactions. A two-hybrid system for E. coli was constructed in which one hybrid protein bound to a specific DNA site recruits another to an adjacent DNA binding site. The first hybrid comprises a test protein, the bait, fused to a chimeric protein containing the 434 repressor DNA binding domain. In the second hybrid, a second test protein, the prey, is fused downstream of a chimeric protein with the DNA binding specificity of the lambda repressor. Reporters were designed to express cat and lacZ under the control of a low-affinity lambda operator. At low expression levels, lambda repressor hybrids weakly repress the reporter genes. A high-affinity operator recognized by 434 repressor was placed nearby, in a position that does not yield repression by 434 repressor alone. If the test proteins interact, the 434 hybrid bound to the 434 operator stabilizes the binding of the lambda repressor hybrid to the lambda operator, causing increased repression of the reporter genes. Reconstruction experiments with the fos and jun leucine zippers detected protein-protein interactions between either homodimeric or heterodimeric leucine zippers.

  3. Probing protein-protein interaction in biomembranes using Fourier transform infrared spectroscopy.

    PubMed

    Haris, Parvez I

    2013-10-01

    The position, intensity and width of bands in infrared spectra that arise from vibrational modes within a protein can be used to probe protein secondary structure, amino acid side chain structure as well as protein dynamics and stability. FTIR spectroscopic studies on protein-protein interaction have been severely limited due to extensive overlap of peaks, from the interacting proteins. This problem is being addressed by combining data processing and acquisition techniques (difference spectroscopy and two-dimensional spectroscopy) with judicious modifications in the protein primary structure through molecular biological and chemical methods. These include the ability to modify amino acids (site-directed mutagenesis; chemical synthesis) and produce isotopically labelled proteins and peptides. Whilst great progress is being made towards overcoming the congestion of overlapping peaks, the slow progress in the assignment of bands continues to be a major hindrance in the use of infrared spectroscopy for obtaining highly accurate and precise information on protein structure. This review discusses some of these problems and presents examples of infrared studies on protein-protein interaction in biomembrane systems. This article is part of a Special Issue entitled: FTIR in membrane proteins and peptide studies.

  4. [A novel biological pathway expansion method based on the knowledge of protein-protein interactions].

    PubMed

    Zhao, Xiaolei; Zuo, Xiaoyu; Qin, Jiheng; Liang, Yan; Zhang, Naizun; Luan, Yizhao; Rao, Shaoqi

    2014-04-01

    Biological pathways have been widely used in gene function studies; however, the current knowledge for biological pathways is per se incomplete and has to be further expanded. Bioinformatics prediction provides us a cheap but effective way for pathway expansion. Here, we proposed a novel method for biological pathway prediction, by intergrating prior knowledge of protein?protein interactions and Gene Ontology (GO) database. First, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to which the interacting neighbors of a targe gene (at the level of protein?protein interaction) belong were chosen as the candidate pathways. Then, the pathways to which the target gene belong were determined by testing whether the genes in the candidate pathways were enriched in the GO terms to which the target gene were annotated. The protein?protein interaction data obtained from the Human Protein Reference Database (HPRD) and Biological General Repository for Interaction Datasets (BioGRID) were respectively used to predict the pathway attribution(s) of the target gene. The results demanstrated that both the average accuracy (the ratio of the correctly predicted pathways to the totally pathways to which all the target genes were annotated) and the relative accuracy (of the genes with at least one annotated pathway being successful predicted, the percentage of the genes with all the annotated pathways being correctly predicted) for pathway predictions were increased with the number of the interacting neighbours. When the number of interacting neighbours reached 22, the average accuracy was 96.2% (HPRD) and 96.3% (BioGRID), respectively, and the relative accuracy was 93.3% (HPRD) and 84.1% (BioGRID), respectively. Further validation analysis of 89 genes whose pathway knowledge was updated in a new database release indicated that 50 genes were correctly predicted for at least one updated pathway, and 43 genes were accurately predicted for all the updated pathways, giving an

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

    PubMed

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

    2012-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  7. Improving protein-protein interaction article classification using biological domain knowledge.

    PubMed

    Chen, Yifei; Guo, Hongjian; Liu, Feng; Manderick, Bernard

    2015-01-01

    Interaction Article Classification (IAC) is a specific text classification application in biological domain that tries to find out which articles describe Protein-Protein Interactions (PPIs) to help extract PPIs from biological literature more efficiently. However, the existing text representation and feature weighting schemes commonly used for text classification are not well suited for IAC. We capture and utilise biological domain knowledge, i.e. gene mentions also known as protein or gene names in the articles, to address the problem. We put forward a new gene mention order-based approach that highlights the important role of gene mentions to represent the texts. Furthermore, we also incorporate the information concerning gene mentions into a novel feature weighting scheme called Gene Mention-based Term Frequency (GMTF). By conducting experiments, we show that using the proposed representation and weighting schemes, our Interaction Article Classifier (IACer) performs better than other leading systems for the moment.

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

    PubMed

    Chowdhury, Archana; Rakshit, Pratyusha; Konar, Amit

    2016-06-01

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

  9. Small-molecule stabilization of the p53 - 14-3-3 protein-protein interaction.

    PubMed

    Doveston, Richard G; Kuusk, Ave; Andrei, Sebastian A; Leysen, Seppe; Cao, Qing; Castaldi, Maria P; Hendricks, Adam; Brunsveld, Luc; Chen, Hongming; Boyd, Helen; Ottmann, Christian

    2017-08-01

    14-3-3 proteins are positive regulators of the tumor suppressor p53, the mutation of which is implicated in many human cancers. Current strategies for targeting of p53 involve restoration of wild-type function or inhibition of the interaction with MDM2, its key negative regulator. Despite the efficacy of these strategies, the alternate approach of stabilizing the interaction of p53 with positive regulators and, thus, enhancing tumor suppressor activity, has not been explored. Here, we report the first example of small-molecule stabilization of the 14-3-3 - p53 protein-protein interaction (PPI) and demonstrate the potential of this approach as a therapeutic modality. We also observed a disconnect between biophysical and crystallographic data in the presence of a stabilizing molecule, which is unusual in 14-3-3 PPIs. © 2017 Federation of European Biochemical Societies.

  10. Small-molecule inhibitors of AF6 PDZ-mediated protein-protein interactions.

    PubMed

    Vargas, Carolyn; Radziwill, Gerald; Krause, Gerd; Diehl, Anne; Keller, Sandro; Kamdem, Nestor; Czekelius, Constantin; Kreuchwig, Annika; Schmieder, Peter; Doyle, Declan; Moelling, Karin; Hagen, Volker; Schade, Markus; Oschkinat, Hartmut

    2014-07-01

    PDZ (PSD-95, Dlg, ZO-1) domains are ubiquitous interaction modules that are involved in many cellular signal transduction pathways. Interference with PDZ-mediated protein-protein interactions has important implications in disease-related signaling processes. For this reason, PDZ domains have gained attention as potential targets for inhibitor design and, in the long run, drug development. Herein we report the development of small molecules to probe the function of the PDZ domain from human AF6 (ALL1-fused gene from chromosome 6), which is an essential component of cell-cell junctions. These compounds bind to AF6 PDZ with substantially higher affinity than the peptide (Ile-Gln-Ser-Val-Glu-Val) derived from its natural ligand, EphB2. In intact cells, the compounds inhibit the AF6-Bcr interaction and interfere with epidermal growth factor (EGF)-dependent signaling. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Protein-protein Interaction Networks of E. coli and S. cerevisiae are similar

    PubMed Central

    Wuchty, S.; Uetz, Peter

    2014-01-01

    Only recently novel high-throughput binary interaction data in E. coli became available that allowed us to compare experimentally obtained protein-protein interaction networks of prokaryotes and eukaryotes (i.e. E. coli and S. cerevisiae). Utilizing binary-Y2H, co-complex and binary literature curated interaction sets in both organisms we found that characteristics of interaction sets that were determined with the same experimental methods were strikingly similar. While essentiality is frequently considered a question of a protein's increasing number of interactions, we found that binary-Y2H interactions failed to show such a trend in both organisms. Furthermore, essential genes are enriched in protein complexes in both organisms. In turn, binary-Y2H interactions hold more bottleneck interactions than co-complex interactions while both binary-Y2H and co-complex interactions are strongly enriched among co-regulated proteins and transcription factors. We discuss if such similarities are a consequence of the underlying methodology or rather reflect truly different biological patterns. PMID:25431098

  12. Protein-protein interaction analysis in single microfluidic droplets using FRET and fluorescence lifetime detection.

    PubMed

    Benz, Christian; Retzbach, Heiko; Nagl, Stefan; Belder, Detlev

    2013-07-21

    Herein, we demonstrate the feasibility of a protein-protein interaction analysis and reaction progress monitoring in microfluidic droplets using FRET and microscopic fluorescence lifetime measurements. The fabrication of microdroplet chips using soft- and photolithographic techniques is demonstrated and the resulting chips reliably generate microdroplets of 630 pL and 6.71 nL at frequencies of 7.9 and 0.75 Hz, respectively. They were used for detection of protein-protein interactions in microdroplets using a model system of Alexa Fluor 488 labelled biotinylated BSA, Alexa Fluor 594 labelled streptavidin and unlabelled chicken egg white avidin. These microchips could be used for quantitative detection of avidin and streptavidin in microdroplets in direct and competitive assay formats with nanomolar detection limits, corresponding to attomole protein amounts. Four droplets were found to be sufficient for analytical determination. Fluorescence intensity ratio and fluorescence lifetime measurements were performed and compared for microdroplet FRET determination. A competitive on-chip binding assay for determination of unlabelled avidin using fluorescence lifetime detection could be performed within 135 s only.

  13. Automated High-Throughput Fluorescence Lifetime Imaging Microscopy to Detect Protein-Protein Interactions.

    PubMed

    Guzmán, Camilo; Oetken-Lindholm, Christina; Abankwa, Daniel

    2016-04-01

    Fluorescence resonance energy transfer (FRET) is widely used to study conformational changes of macromolecules and protein-protein, protein-nucleic acid, and protein-small molecule interactions. FRET biosensors can serve as valuable secondary assays in drug discovery and for target validation in mammalian cells. Fluorescence lifetime imaging microscopy (FLIM) allows precise quantification of the FRET efficiency in intact cells, as FLIM is independent of fluorophore concentration, detection efficiency, and fluorescence intensity. We have developed an automated FLIM system using a commercial frequency domain FLIM attachment (Lambert Instruments) for wide-field imaging. Our automated FLIM system is capable of imaging and analyzing up to 50 different positions of a slide in less than 4 min, or the inner 60 wells of a 96-well plate in less than 20 min. Automation is achieved using a motorized stage and controller (Prior Scientific) coupled with a Zeiss Axio Observer body and full integration into the Lambert Instruments FLIM acquisition software. As an application example, we analyze the interaction of the oncoprotein Ras and its effector Raf after drug treatment. In conclusion, our automated FLIM imaging system requires only commercial components and may therefore allow for a broader use of this technique in chemogenomics projects. © 2015 Society for Laboratory Automation and Screening.

  14. Protein-Protein Interactions, Not Substrate Recognition, Dominate the Turnover of Chimeric Assembly Line Polyketide Synthases*

    PubMed Central

    Klaus, Maja; Ostrowski, Matthew P.; Austerjost, Jonas; Robbins, Thomas; Lowry, Brian; Cane, David E.; Khosla, Chaitan

    2016-01-01

    The potential for recombining intact polyketide synthase (PKS) modules has been extensively explored. Both enzyme-substrate and protein-protein interactions influence chimeric PKS activity, but their relative contributions are unclear. We now address this issue by studying a library of 11 bimodular and 8 trimodular chimeric PKSs harboring modules from the erythromycin, rifamycin, and rapamycin synthases. Although many chimeras yielded detectable products, nearly all had specific activities below 10% of the reference natural PKSs. Analysis of selected bimodular chimeras, each with the same upstream module, revealed that turnover correlated with the efficiency of intermodular chain translocation. Mutation of the acyl carrier protein (ACP) domain of the upstream module in one chimera at a residue predicted to influence ketosynthase-ACP recognition led to improved turnover. In contrast, replacement of the ketoreductase domain of the upstream module by a paralog that produced the enantiomeric ACP-bound diketide caused no changes in processing rates for each of six heterologous downstream modules compared with those of the native diketide. Taken together, these results demonstrate that protein-protein interactions play a larger role than enzyme-substrate recognition in the evolution or design of catalytically efficient chimeric PKSs. PMID:27246853

  15. Mass spectrometric analysis of post-translational modifications (PTMs) and protein-protein interactions (PPIs).

    PubMed

    Ngounou Wetie, Armand G; Woods, Alisa G; Darie, Costel C

    2014-01-01

    Of the 25,000-30,000 human genes, about 2 % code for proteins. However, there are about one to two million protein entities. This is primarily due to alternative splicing and post-translational modifications (PTMs). Identifying all these modifications in one proteome at a particular time point during development or during the transition from normal to cancerous cells is a great challenge to scientists. In addition, identifying the biological significance of all these modifications, as well as their nature, such as stable versus transient modifications, is an even more challenging. Furthermore, interaction of proteins and protein isoforms that have one or more stable or transient PTMs with other proteins and protein isoforms makes the study of proteins daunting and complex. Here we review some of the strategies to study proteins, protein isoforms, protein PTMs, and protein-protein interactions (PPIs). Our goal is to provide a thorough understanding of these proteins and their isoforms, PTMs and PPIs and to shed light on the biological significance of these factors.

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

    PubMed

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

    2012-07-01

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

  17. DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks.

    PubMed

    Du, Xiuquan; Sun, Shiwei; Hu, Changlin; Yao, Yu; Yan, Yuanting; Zhang, Yanping

    2017-06-26

    The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein-protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%, Specificity of 94.49%, Matthews Correlation Coefficient of 85.08% and Area Under the Curve of 97.43%, respectively. Extensive experiments show that DeepPPI can learn useful features of proteins pairs by a layer-wise abstraction, and thus achieves better prediction performance than existing methods. The source code of our approach can be available via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html .

  18. DUF581 Is Plant Specific FCS-Like Zinc Finger Involved in Protein-Protein Interaction

    PubMed Central

    K, Muhammed Jamsheer; Laxmi, Ashverya

    2014-01-01

    Zinc fingers are a ubiquitous class of protein domain with considerable variation in structure and function. Zf-FCS is a highly diverged group of C2-C2 zinc finger which is present in animals, prokaryotes and viruses, but not in plants. In this study we identified that a plant specific domain of unknown function, DUF581 is a zf-FCS type zinc finger. Based on HMM-HMM comparison and signature motif similarity we named this domain as FCS-Like Zinc finger (FLZ) domain. A genome wide survey identified that FLZ domain containing genes are bryophytic in origin and this gene family is expanded in spermatophytes. Expression analysis of selected FLZ gene family members of A. thaliana identified an overlapping expression pattern suggesting a possible redundancy in their function. Unlike the zf-FCS domain, the FLZ domain found to be highly conserved in sequence and structure. Using a combination of bioinformatic and protein-protein interaction tools, we identified that FLZ domain is involved in protein-protein interaction. PMID:24901469

  19. Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

    PubMed Central

    2011-01-01

    Background The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine. Results To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date. Conclusions The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data. PMID:21303559

  20. Inferring plant microRNA functional similarity using a weighted protein-protein interaction network.

    PubMed

    Meng, Jun; Liu, Dong; Luan, Yushi

    2015-11-04

    MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity. The functional similarity of miRNAs was inferred from the functional similarity of their target gene sets. A protein-protein interaction network with semantic similarity weights of edges generated using Gene Ontology terms was constructed to infer the functional similarity between two target genes that belong to two different miRNAs, and the score for functional similarity was calculated using the weighted shortest path for the two target genes through the whole network. The experimental results showed that the proposed method was more effective and reliable than previous methods (miRFunSim and GOSemSim) applied to Arabidopsis thaliana. Additionally, miRNAs responding to the same type of stress had higher functional similarity than miRNAs responding to different types of stress. For the first time, a protein-protein interaction network with semantic similarity weights generated using Gene Ontology terms was employed to calculate the functional similarity of plant miRNAs. A novel method based on calculating the weighted shortest path between two target genes was introduced.

  1. Reduced native state stability in crowded cellular environment due to protein-protein interactions

    PubMed Central

    Harada, Ryuhei; Tochio, Naoya; Kigawa, Takanori; Sugita, Yuji; Feig, Michael

    2013-01-01

    The effect of cellular crowding environments on protein structure and stability is a key issue in molecular and cellular biology. The classical view of crowding emphasizes the volume exclusion effect that generally favors compact, native states. Here, results from molecular dynamics simulations and NMR experiments show that protein crowders may destabilize native states via protein-protein interactions. In the model system considered here, mixtures of villin head piece and protein G at high concentrations, villin structures become increasingly destabilized upon increasing crowder concentrations. The denatured states observed in the simulation involve partial unfolding as well as more subtle conformational shifts. The unfolded states remain overall compact and only partially overlap with unfolded ensembles at high temperature and in the presence of urea. NMR measurements on the same systems confirm structural changes upon crowding based on changes of chemical shifts relative to dilute conditions. An analysis of protein-protein interactions and energetic aspects suggests the importance of enthalpic and solvation contributions to the crowding free energies that challenge an entropic-centered view of crowding effects. PMID:23402619

  2. Detection of protein-protein interactions in plants using bimolecular fluorescence complementation.

    PubMed

    Bracha-Drori, Keren; Shichrur, Keren; Katz, Aviva; Oliva, Moran; Angelovici, Ruthie; Yalovsky, Shaul; Ohad, Nir

    2004-11-01

    Protein function is often mediated via formation of stable or transient complexes. Here we report the determination of protein-protein interactions in plants using bimolecular fluorescence complementation (BiFC). The yellow fluorescent protein (YFP) was split into two non-overlapping N-terminal (YN) and C-terminal (YC) fragments. Each fragment was cloned in-frame to a gene of interest, enabling expression of fusion proteins. To demonstrate the feasibility of BiFC in plants, two pairs of interacting proteins were utilized: (i) the alpha and beta subunits of the Arabidopsis protein farnesyltransferase (PFT), and (ii) the polycomb proteins, FERTILIZATION-INDEPENDENT ENDOSPERM (FIE) and MEDEA (MEA). Members of each protein pair were transiently co-expressed in leaf epidermal cells of Nicotiana benthamiana or Arabidopsis. Reconstitution of a fluorescing YFP chromophore occurred only when the inquest proteins interacted. No fluorescence was detected following co-expression of free non-fused YN and YC or non-interacting protein pairs. Yellow fluorescence was detected in the cytoplasm of cells that expressed PFT alpha and beta subunits, or in nuclei and cytoplasm of cells that expressed FIE and MEA. In vivo measurements of fluorescence spectra emitted from reconstituted YFPs were identical to that of a non-split YFP, confirming reconstitution of the chromophore. Expression of the inquest proteins was verified by immunoblot analysis using monoclonal antibodies directed against tags within the hybrid proteins. In addition, protein interactions were confirmed by immunoprecipitations. These results demonstrate that plant BiFC is a simple, reliable and relatively fast method for determining protein-protein interactions in plants.

  3. Determining minimum set of driver nodes in protein-protein interaction networks.

    PubMed

    Zhang, Xiao-Fei; Ou-Yang, Le; Zhu, Yuan; Wu, Meng-Yun; Dai, Dao-Qing

    2015-05-07

    Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins. To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model. Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software

  4. Construction of an immunorelated protein-protein interaction network for clarifying the mechanism of burn.

    PubMed

    Gao, Yanbin; Nai, Wenqing; Yang, Lei; Lu, Zhiyang; Shi, Pengwei; Jin, Hui; Wen, Huangding; Wang, Guifang

    2016-03-01

    Severe burn is known to induce a series of pathological responses resulting in increased susceptibility to systemic inflammatory response and multiple organ failure, but the underlying molecular mechanism remains unclear at present. The main aim of this study was to expand our understanding of the events leading to circulating leukocyte response after burn by subjecting the gene expression profiles to a bioinformatic analysis. Comprehensive gene expression analysis was performed to identify differentially expressed genes (DEGs) using the expression profile GSE7404 (Mus musculus, circulating leukocyte, 25% of total body surface area (TBSA), full thickness) downloaded from the Gene Expression Omnibus, followed by the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, a postburn protein-protein interaction (PPI) network was constructed to identify potential biomarkers. Maximum changes in the gene expression profile were detected 1 day post burn. Separate Gene Ontology (GO) functional enrichment analysis for upregulated and downregulated DEGs revealed significant alterations of genes related to biological process such as "response to stimuli," "metabolic," "cellular and immune system processes," "biological regulation," and "death" in the leukocyte transcriptome after the burn. The KEGG pathway enrichment analysis showed that the upregulated DEGs were significantly enriched in the nodes of immunorelated and signal transduction-related pathways, and the downregulated genes were significantly enriched for the immunorelated pathways. The PPI network and module analysis revealed that, 1 day after the burn, lymphocyte-specific protein tyrosine kinase (Lck) (downregulated), Jun (upregulated), Cd19 (downregulated), Stat1 (downregulated), and Cdk1 (upregulated) were located centrally in both the PPI network and modules. Based on an integrated bioinformatic analysis, we concluded that Lck, Jun, Cd19, Stat1, and Cdk1 may be

  5. Physicochemical descriptors to discriminate protein-protein interactions in permanent and transient complexes selected by means of machine learning algorithms.

    PubMed

    Block, Peter; Paern, Juri; Hüllermeier, Eyke; Sanschagrin, Paul; Sotriffer, Christoph A; Klebe, Gerhard

    2006-11-15

    Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose, descriptors explaining the nature of different protein-protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (c) 2006 Wiley-Liss, Inc.

  6. Protein-Protein Interaction Inhibitors of BRCA1 Discovered Using Small Molecule Microarrays.

    PubMed

    Na, Zhenkun; Pan, Sijun; Uttamchandani, Mahesh; Yao, Shao Q

    2017-01-01

    Microarray screening technology has transformed the life sciences arena over the last decade. The platform is widely used in the area of mapping interaction networks, to molecular fingerprinting and small molecular inhibitor discovery. The technique has significantly impacted both basic and applied research. The microarray platform can likewise enable high-throughput screening and discovery of protein-protein interaction (PPI) inhibitors. Herein we demonstrate the application of microarray-guided PPI inhibitor discovery, using human BRCA1 as an example. Mutations in BRCA1 have been implicated in ~50 % of hereditary breast cancers. By targeting the (BRCT)2 domain, we showed compound 15a and its prodrug 15b inhibited BRCA1 activities in tumor cells. Unlike previously reported peptide-based PPI inhibitors of BRCA1, the compounds identified could be directly administered to tumor cells, thus making them useful in targeting BRCA1/PARP-related pathways involved in DNA damage and repair response, for cancer therapy.

  7. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    PubMed

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  8. Small-molecule stabilization of protein-protein interactions: an underestimated concept in drug discovery?

    PubMed

    Thiel, Philipp; Kaiser, Markus; Ottmann, Christian

    2012-02-27

    The modulation of protein-protein interactions (PPIs) has been recognized as one of the most challenging tasks in drug discovery. While their systematic development has long been considered as intractable, this view has changed over the last years, with the first drug candidates undergoing clinical studies. To date, the vast majority of PPI modulators are interaction inhibitors. However, in many biological contexts a prolonged lifespan of a PPI might be desirable, calling for the complementary approach of PPI stabilization. In fact, nature offers impressive examples of this concept and some PPI-stabilizing natural products have already found application as important drugs. Moreover, directed small-molecule stabilization has recently been demonstrated. Therefore, it is time to take a closer look at the constructive side of modulating PPIs. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Small-molecule modulators of 14-3-3 protein-protein interactions.

    PubMed

    Ottmann, Christian

    2013-07-15

    14-3-3 Proteins are eukaryotic adapter proteins that regulate a plethora of physiological processes by binding to several hundred partner proteins. They play a role in biological activities as diverse as signal transduction, cell cycle regulation, apoptosis, host-pathogen interactions and metabolic control. As such, 14-3-3s are implicated in disease areas like cancer, neurodegeneration, diabetes, pulmonary disease, and obesity. Targeted modulation of 14-3-3 protein-protein interactions (PPIs) by small molecules is therefore an attractive concept for disease intervention. In recent years a number of examples of inhibitors and stabilizers of 14-3-3 PPIs have been reported promising a vivid future in chemical biology and drug development for this remarkable class of proteins. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Context-based retrieval of functional modules in protein-protein interaction networks.

    PubMed

    Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

    2017-03-27

    Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference.

  11. A comparative study of monoclonal antibodies. 1. Phase behavior and protein-protein interactions.

    PubMed

    Lewus, Rachael A; Levy, Nicholas E; Lenhoff, Abraham M; Sandler, Stanley I

    2015-01-01

    Protein phase behavior is involved in numerous aspects of downstream processing, either by design as in crystallization or precipitation processes, or as an undesired effect, such as aggregation. This work explores the phase behavior of eight monoclonal antibodies (mAbs) that exhibit liquid-liquid separation, aggregation, gelation, and crystallization. The phase behavior has been studied systematically as a function of a number of factors, including solution composition and pH, in order to explore the degree of variability among different antibodies. Comparisons of the locations of phase boundaries show consistent trends as a function of solution composition; however, changing the solution pH has different effects on each of the antibodies studied. Furthermore, the types of dense phases formed varied among the antibodies. Protein-protein interactions, as reflected by values of the osmotic second virial coefficient, are used to correlate the phase behavior. The primary findings are that values of the osmotic second virial coefficient are useful for correlating phase boundary locations, though there is appreciable variability among the antibodies in the apparent strengths of the intrinsic protein-protein attraction manifested. However, the osmotic second virial coefficient does not provide a clear basis to predict the type of dense phase likely to result under a given set of solution conditions.

  12. Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery.

    PubMed

    Milhas, Sabine; Raux, Brigitt; Betzi, Stéphane; Derviaux, Carine; Roche, Philippe; Restouin, Audrey; Basse, Marie-Jeanne; Rebuffet, Etienne; Lugari, Adrien; Badol, Marion; Kashyap, Rudra; Lissitzky, Jean-Claude; Eydoux, Cécilia; Hamon, Véronique; Gourdel, Marie-Edith; Combes, Sébastien; Zimmermann, Pascale; Aurrand-Lions, Michel; Roux, Thomas; Rogers, Catherine; Müller, Susanne; Knapp, Stefan; Trinquet, Eric; Collette, Yves; Guillemot, Jean-Claude; Morelli, Xavier

    2016-08-19

    Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.

  13. Modeling implicit reorganization in continuum descriptions of protein-protein interactions.

    PubMed

    Olson, M A; Reinke, L T

    2000-01-01

    The determination of free energies that govern protein-protein recognition is essential for a detailed molecular understanding of biological specificity. Continuum models of macromolecular interactions, in which the solvent is treated by an implicit representation and the proteins are treated semi-microscopically, are computationally tractable for estimating free energies, yet many questions remain concerning their accuracy. This article reports a continuum analysis of the free-energy changes underlying the binding of 31 interfacial alanine substitutions of two complexes of the antihen egg white lysozyme (HEL) antibody D1.3 bound with HEL or the antibody E5.2. Two implicit schemes for modeling the effects of protein and solvent relaxation were examined, in which the protein environment was treated as either homogeneous with a "protein dielectric constant" of epsilon(p) = 4 or inhomogeneous, with epsilon(p) = 4 for neutral residues and epsilon(p) = 25 for ionized residues. The results showed that the nonuniform dielectric model reproduced the experimental differences better, with an average absolute error of +/-1.1 kcal/mol, compared with +/-1.4 kcal/mol for the uniform model. More importantly, the error for charged residues in the nonuniform model is +/-0.8 kcal/mol and is nearly half of that corresponding to the uniform model. Several substitutions were clearly problematic in determining qualitative trends and probably required explicit structural reorganization at the protein-protein interface.

  14. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

    PubMed

    Yang, Lei; Tang, Xianglong

    2014-01-01

    Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  15. Detecting Protein-Protein Interactions in Vesicular Stomatitis Virus Using a Cytoplasmic Yeast Two Hybrid System

    PubMed Central

    Moerdyk-Schauwecker, Megan; DeStephanis, Darla; Hastie, Eric; Grdzelishvili, Valery Z.

    2011-01-01

    Summary Protein-protein interactions play an important role in many virus-encoded functions and in virus-host interactions. While a “classical” yeast two-hybrid system (Y2H) is one of the most common techniques to detect such interactions, it has a number of limitations, including a requirement for the proteins of interest to be relocated to the nucleus. Modified Y2H, such as the Sos recruitment system (SRS), which detect interactions occurring in the cytoplasm rather than the nucleus, allow proteins from viruses replicating in the cytoplasm to be tested in a more natural context. In this study, a SRS was used to detect interactions involving proteins from vesicular stomatitis virus (VSV), a prototypic non-segmented negative strand RNA (NNS) virus. All five full-length VSV proteins, as well as several truncated proteins, were screened against each other. Using the SRS, most interactions demonstrated previously involving VSV phosphoprotein, nucleocapsid (N) and large polymerase proteins were confirmed independently, while difficulties were encountered using the membrane associated matrix and glycoproteins. A human cDNA library was also screened against VSV N protein and one cellular protein, SFRS18, was identified which interacted with N in this context. The system presented can be redesigned easily for studies in other less tractable NNS viruses. PMID:21320532

  16. PathPPI: an integrated dataset of human pathways and protein-protein interactions.

    PubMed

    Tang, HaiLin; Zhong, Fan; Liu, Wei; He, FuChu; Xie, HongWei

    2015-06-01

    Integration of pathway and protein-protein interaction (PPI) data can provide more information that could lead to new biological insights. PPIs are usually represented by a simple binary model, whereas pathways are represented by more complicated models. We developed a series of rules for transforming protein interactions from pathway to binary model, and the protein interactions from seven pathway databases, including PID, BioCarta, Reactome, NetPath, INOH, SPIKE and KEGG, were transformed based on these rules. These pathway-derived binary protein interactions were integrated with PPIs from other five PPI databases including HPRD, IntAct, BioGRID, MINT and DIP, to develop integrated dataset (named PathPPI). More detailed interaction type and modification information on protein interactions can be preserved in PathPPI than other existing datasets. Comparison analysis results indicate that most of the interaction overlaps values (O AB) among these pathway databases were less than 5%, and these databases must be used conjunctively. The PathPPI data was provided at http://proteomeview.hupo.org.cn/PathPPI/PathPPI.html.

  17. Protein-protein interaction and molecular dynamics analysis for identification of novel inhibitors in Burkholderia cepacia GG4.

    PubMed

    Gupta, Money; Chauhan, Rashi; Prasad, Yamuna; Wadhwa, Gulshan; Jain, Chakresh Kumar

    2016-12-01

    The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of -5.7kcalmol(-1) and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes.

  18. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    PubMed Central

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  19. Establishing an osteosarcoma associated protein-protein interaction network to explore the pathogenesis of osteosarcoma

    PubMed Central

    2013-01-01

    Background The aim of this study was to establish an osteosarcoma (OS) associated protein-protein interaction network and explore the pathogenesis of osteosarcoma. Methods The gene expression profile GSE9508 was downloaded from the Gene Expression Omnibus database, including five samples of non-malignant bone (the control), seven samples for non-metastatic patients (six of which were analyzed in duplicate), and 11 samples for metastatic patients (10 of which were analyzed in duplicate). Differentially expressed genes (DEGs) between osteosarcoma and control samples were identified by packages in R with the threshold of |logFC (fold change)| > 1 and false discovery rate < 0.05. Osprey software was used to construct the interaction network of DEGs, and genes at protein-protein interaction (PPI) nodes with high degrees were identified. The Database for Annotation, Visualization and Integrated Discovery and WebGestalt software were then used to perform functional annotation and pathway enrichment analyses for PPI networks, in which P < 0.05 was considered statistically significant. Results Compared to the control samples, the expressions of 42 and 341 genes were altered in non-metastatic OS and metastatic OS samples, respectively. A total of 15 significantly enriched functions were obtained with Gene Ontology analysis (P < 0.05). The DEGs were classified and significantly enriched in three pathways, including the tricarboxylic acid cycle, lysosome and axon guidance. Genes such as HRAS, IDH3A, ATP6ap1, ATP6V0D2, SEMA3F and SEMA3A were involved in the enriched pathways. Conclusions The hub genes from metastatic OS samples are not only bio-markers of OS, but also help to improve therapies for OS. PMID:24330838

  20. Protein-protein interaction interface residue pair prediction based on deep learning architecture.

    PubMed

    Zhao, Zhenni; Gong, Xinqi

    2017-05-19

    Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interaction between two monomer proteins. The correct prediction of interface residue pairs from two monomer proteins is still an open question and has great significance for practical experimental applications in the life sciences. We hope to build a method for the prediction of interface residue pairs that is suitable for those applications. Here, we developed a novel deep network architecture called the multi-layered Long-Short Term Memory networks (LSTMs) approach for the prediction of protein interface residue pairs. Firstly, we created three new descriptions and used other six worked characterizations to describe an amino acid, then we employed these features to discriminate between interface residue pairs and non-interface residue pairs. Secondly, we used two thresholds to select residue pairs that are more likely to be interface residue pairs. Furthermore, this step increases the proportion of interface residue pairs and reduces the influence of imbalanced data. Thirdly, we built deep network architectures based on Long-Short Term Memory networks algorithm to organize and refine the prediction of interface residue pairs by employing features mentioned above. We trained the deep networks on dimers in the unbound state in the international Protein-protein Docking Benchmark version 3.0. The updated data sets in the versions 4.0 and 5.0 were used as the validation set and test set respectively. For our best model, the accuracy rate was over 62% when we chose the top 0.2% pairs of every dimer in the test set as predictions, which will be very helpful for the understanding of protein-protein interaction mechanisms and for guidance in biological experiments.

  1. The protein-protein interaction network of the human Sirtuin family.

    PubMed

    Sharma, Ankush; Costantini, Susan; Colonna, Giovanni

    2013-10-01

    Protein-protein interaction networks are useful for studying human diseases and to look for possible health care through a holistic approach. Networks are playing an increasing and important role in the understanding of physiological processes such as homeostasis, signaling, spatial and temporal organizations, and pathological conditions. In this article we show the complex system of interactions determined by human Sirtuins (Sirt) largely involved in many metabolic processes as well as in different diseases. The Sirtuin family consists of seven homologous Sirt-s having structurally similar cores but different terminal segments, being rather variable in length and/or intrinsically disordered. Many studies have determined their cellular location as well as biological functions although molecular mechanisms through which they act are actually little known therefore, the aim of this work was to define, explore and understand the Sirtuin-related human interactome. As a first step, we have integrated the experimentally determined protein-protein interactions of the Sirtuin-family as well as their first and second neighbors to a Sirtuin-related sub-interactome. Our data showed that the second-neighbor network of Sirtuins encompasses 25% of the entire human interactome, and exhibits a scale-free degree distribution and interconnectedness among top degree nodes. Moreover, the Sirtuin sub interactome showed a modular structure around the core comprising mixed functions. Finally, we extracted from the Sirtuin sub-interactome subnets related to cancer, aging and post-translational modifications for information on key nodes and topological space of the subnets in the Sirt family network.

  2. A profile of protein-protein interaction: Crystal structure of a lectin-lectin complex.

    PubMed

    Surya, Sukumaran; Abhilash, Joseph; Geethanandan, Krishnan; Sadasivan, Chittalakkottu; Haridas, Madhathilkovilakathu

    2016-06-01

    Proteins may utilize complex networks of interactions to create/proceed signaling pathways of highly adaptive responses such as programmed cell death. Direct binary interactions study of proteins may help propose models for protein-protein interaction. Towards this goal we applied a combination of thermodynamic kinetics and crystal structure analyses to elucidate the complexity and diversity in such interactions. By determining the heat change on the association of two galactose-specific legume lectins from Butea monosperma (BML) and Spatholobus parviflorus (SPL) belonging to Fabaceae family helped to compute the binding equilibrium. It was extended further by X-ray structural analysis of BML-SPL binary complex. In order to chart the proteins interacting mainly through their interfaces, identification of the nature of forces which stabilized the association of the lectin-lectin complex was examined. Comprehensive analysis of the BMLSPL complex by isothermal titration calorimetry and X-ray crystal structure threw new light on the lectin-lectin interactions suggesting of their use in diverse areas of glycobiology.

  3. Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

    PubMed Central

    Andersen, Tonni Grube; Nintemann, Sebastian J.; Marek, Magdalena; Halkier, Barbara A.; Schulz, Alexander; Burow, Meike

    2016-01-01

    When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true- from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality. PMID:27282591

  4. Analysis of protein-protein interactions in the feline calicivirus replication complex.

    PubMed

    Kaiser, William J; Chaudhry, Yasmin; Sosnovtsev, Stanislav V; Goodfellow, Ian G

    2006-02-01

    Caliciviruses are a major cause of gastroenteritis in humans and cause a wide variety of other diseases in animals. Here, the characterization of protein-protein interactions between the individual proteins of Feline calicivirus (FCV), a model system for other members of the family Caliciviridae, is reported. Using the yeast two-hybrid system combined with a number of other approaches, it is demonstrated that the p32 protein (the picornavirus 2B analogue) of FCV interacts with p39 (2C), p30 (3A) and p76 (3CD). The FCV protease/RNA polymerase (ProPol) p76 was found to form homo-oligomers, as well as to interact with VPg and ORF2, the region encoding the major capsid protein VP1. A weak interaction was also observed between p76 and the minor capsid protein encoded by ORF3 (VP2). ORF2 protein was found to interact with VPg, p76 and VP2. The potential roles of the interactions in calicivirus replication are discussed.

  5. PPISEARCHENGINE: gene ontology-based search for protein-protein interactions.

    PubMed

    Park, Byungkyu; Cui, Guangyu; Lee, Hyunjin; Huang, De-Shuang; Han, Kyungsook

    2013-01-01

    This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/.

  6. Peptide inhibitors of the Keap1-Nrf2 protein-protein interaction.

    PubMed

    Hancock, Rowena; Bertrand, Hélène C; Tsujita, Tadayuki; Naz, Shama; El-Bakry, Ayman; Laoruchupong, Jitnueng; Hayes, John D; Wells, Geoff

    2012-01-15

    Disruption of the interaction between the ubiquitination facilitator protein Keap1 and the cap'n'collar basic-region leucine-zipper transcription factor Nrf2 is a potential strategy to enhance expression of antioxidant and free radical detoxification gene products regulated by Nrf2. Agents that disrupt this protein-protein interaction may be useful pharmacological probes and future cancer-chemopreventive agents. We describe the structure-activity relationships for a series of peptides based upon regions of the Nrf2 Neh2 domain, of varying length and sequence, that interact with the Keap1 Kelch domain and disrupt the interaction with Nrf2. We have also investigated sequestosome-1 (p62) and prothymosin-α sequences that have been reported to interact with Keap1. To achieve this we have developed a high-throughput fluorescence polarization (FP) assay to screen inhibitors. In addition to screening synthetic peptides, we have used a phage display library approach to identify putative peptide ligands with non-native sequence motifs. Candidate peptides from the phage display library screening protocol were evaluated in the FP assay to quantify their binding activity. Hybrid peptides based upon the Nrf2 "ETGE" motif and the sequestosome-1 "Keap1-interaction region" have superior binding activity compared to either native peptide alone.

  7. Cryptic protein-protein interaction motifs in the cytoplasmic domain of MHCI proteins.

    PubMed

    Frietze, Karla K; Pappy, Adlai L; Melson, Jack W; O'Driscoll, Emily E; Tyler, Carolyn M; Perlman, David H; Boulanger, Lisa M

    2016-07-19

    Major histocompatibility complex class I (MHCI) proteins present antigenic peptides for immune surveillance and play critical roles in nervous system development and plasticity. Most MHCI are transmembrane proteins. The extracellular domain of MHCI interacts with immunoreceptors, peptides, and co-receptors to mediate immune signaling. While the cytoplasmic domain also plays important roles in endocytic trafficking, cross-presentation of extracellularly derived antigens, and CTL priming, the molecular mediators of cytoplasmic signaling by MHCI remain largely unknown. Here we show that the cytoplasmic domain of MHCI contains putative protein-protein interaction domains known as PDZ (PSD95/disc large/zonula occludens-1) ligands. PDZ ligands are motifs that bind to PDZ domains to organize and mediate signaling at cell-cell contacts. PDZ ligands are short, degenerate motifs, and are therefore difficult to identify via sequence homology alone, but several lines of evidence suggest that putative PDZ ligand motifs in MHCI are under positive selective pressure. Putative PDZ ligands are found in all of the 99 MHCI proteins examined from diverse species, and are enriched in the cytoplasmic domain, where PDZ interactions occur. Both the position of the PDZ ligand and the class of ligand motif are conserved across species, as well as among genes within a species. Non-synonymous substitutions, when they occur, frequently preserve the motif. Of the many specific possible PDZ ligand motifs, a handful are strikingly and selectively overrepresented in MHCI's cytoplasmic domain, but not elsewhere in the same proteins. Putative PDZ ligands in MHCI encompass conserved serine and tyrosine residues that are targets of phosphorylation, a post-translational modification that can regulate PDZ interactions. Finally, proof-of-principle in vitro interaction assays demonstrate that the cytoplasmic domains of particular MHCI proteins can bind directly and specifically to PDZ1 and PDZ4&5 of MAGI

  8. Screening bicyclic peptide libraries for protein-protein interaction inhibitors: discovery of a tumor necrosis factor-α antagonist.

    PubMed

    Lian, Wenlong; Upadhyaya, Punit; Rhodes, Curran A; Liu, Yusen; Pei, Dehua

    2013-08-14

    Protein-protein interactions represent a new class of exciting but challenging drug targets, because their large, flat binding sites lack well-defined pockets for small molecules to bind. We report here a methodology for chemical synthesis and screening of large combinatorial libraries of bicyclic peptides displayed on rigid small-molecule scaffolds. With planar trimesic acid as the scaffold, the resulting bicyclic peptides are effective for binding to protein surfaces such as the interfaces of protein-protein interactions. Screening of a bicyclic peptide library against tumor necrosis factor-α (TNFα) identified a potent antagonist that inhibits the TNFα-TNFα receptor interaction and protects cells from TNFα-induced cell death. Bicyclic peptides of this type may provide a general solution for inhibition of protein-protein interactions.

  9. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

    PubMed Central

    Laraia, Luca; McKenzie, Grahame; Spring, David R.; Venkitaraman, Ashok R.; Huggins, David J.

    2015-01-01

    Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs. PMID:26091166

  10. HippDB: a database of readily targeted helical protein-protein interactions.

    PubMed

    Bergey, Christina M; Watkins, Andrew M; Arora, Paramjit S

    2013-11-01

    HippDB catalogs every protein-protein interaction whose structure is available in the Protein Data Bank and which exhibits one or more helices at the interface. The Web site accepts queries on variables such as helix length and sequence, and it provides computational alanine scanning and change in solvent-accessible surface area values for every interfacial residue. HippDB is intended to serve as a starting point for structure-based small molecule and peptidomimetic drug development. HippDB is freely available on the web at http://www.nyu.edu/projects/arora/hippdb. The Web site is implemented in PHP, MySQL and Apache. Source code freely available for download at http://code.google.com/p/helidb, implemented in Perl and supported on Linux. arora@nyu.edu.

  11. Hot spot-based design of small-molecule inhibitors for protein-protein interactions.

    PubMed

    Guo, Wenxing; Wisniewski, John A; Ji, Haitao

    2014-06-01

    Protein-protein interactions (PPIs) are important targets for the development of chemical probes and therapeutic agents. From the initial discovery of the existence of hot spots at PPI interfaces, it has been proposed that hot spots might provide the key for developing small-molecule PPI inhibitors. However, there has been no review on the ways in which the knowledge of hot spots can be used to achieve inhibitor design, nor critical examination of successful examples. This Digest discusses the characteristics of hot spots and the identification of druggable hot spot pockets. An analysis of four examples of hot spot-based design reveals the importance of this strategy in discovering potent and selective PPI inhibitors. A general procedure for hot spot-based design of PPI inhibitors is outlined. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Investigation of stable and transient protein-protein interactions: past, present and future

    PubMed Central

    Ngounou Wetie, Armand G.; Sokolowska, Izabela; Woods, Alisa G.; Roy, Urmi; Loo, Joseph A.; Darie, Costel C.

    2013-01-01

    This article presents an overview of the literature and a review of recent advances in the analysis of stable and transient protein-protein interactions (PPIs) with a focus on their function within cells, organs and organisms. The significance of post-translational modifications within the PPIs is also discussed. We focus on methods to study PPIs and methods of detecting PPIs, with particular emphasis on electrophoresis-based and mass spectrometry (MS)-based investigation of PPIs, including specific examples. The validation of PPIs is emphasized and the limitations of the current methods for studying stable and transient PPIs are discussed. Perspectives regarding PPIs, with focus on bioinformatics and transient PPIs are also provided. PMID:23193082

  13. Prediction of Protein-Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests.

    PubMed

    Jia, Jianhua; Xiao, Xuan; Liu, Bingxiang

    2016-06-01

    Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF.

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

    PubMed Central

    Wang, Baoman; Yuan, Fei; Kong, Xiangyin; Hu, Lan-Dian; Cai, Yu-Dong

    2015-01-01

    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. PMID:26543496

  15. Diversity-oriented synthetic strategy for developing a chemical modulator of protein-protein interaction

    NASA Astrophysics Data System (ADS)

    Kim, Jonghoon; Jung, Jinjoo; Koo, Jaeyoung; Cho, Wansang; Lee, Won Seok; Kim, Chanwoo; Park, Wonwoo; Park, Seung Bum

    2016-10-01

    Diversity-oriented synthesis (DOS) can provide a collection of diverse and complex drug-like small molecules, which is critical in the development of new chemical probes for biological research of undruggable targets. However, the design and synthesis of small-molecule libraries with improved biological relevance as well as maximized molecular diversity represent a key challenge. Herein, we employ functional group-pairing strategy for the DOS of a chemical library containing privileged substructures, pyrimidodiazepine or pyrimidine moieties, as chemical navigators towards unexplored bioactive chemical space. To validate the utility of this DOS library, we identify a new small-molecule inhibitor of leucyl-tRNA synthetase-RagD protein-protein interaction, which regulates the amino acid-dependent activation of mechanistic target of rapamycin complex 1 signalling pathway. This work highlights that privileged substructure-based DOS strategy can be a powerful research tool for the construction of drug-like compounds to address challenging biological targets.

  16. ACC-FMD: ant colony clustering for functional module detection in protein-protein interaction networks.

    PubMed

    Ji, Junzhong; Liu, Hongxin; Zhang, Aidong; Liu, Zhijun; Liu, Chunnian

    2015-01-01

    Mining functional modules in Protein-Protein Interaction (PPI) networks is a very important research for revealing the structure-functionality relationships in biological processes. More recently, some swarm intelligence algorithms have been successfully applied in the field. This paper presents a new nature-inspired approach, ACC-FMD, which is based on ant colony clustering to detect functional modules. First, some proteins with the higher clustering coefficients are, respectively, selected as ant seed nodes. And then, the picking and dropping operations based on ant probabilistic models are developed and employed to assign proteins into the corresponding clusters represented by seeds. Finally, the best clustering result in each generation is used to perform the information transmission by updating the similarly function. Experimental results on some benchmarked datasets show that ACC-FMD outperforms the CFinder and MCODE algorithms and has comparative performance with the MINE, COACH, DPClus and Core algorithms in terms of the general evaluation metrics.

  17. Specific protein-protein interactions of calsequestrin with junctional sarcoplasmic reticulum of skeletal muscle

    SciTech Connect

    Damiani, E.; Margreth, A. )

    1990-11-15

    Minor protein components of triads and of sarcoplasmic reticulum (SR) terminal cisternae (TC), i.e. 47 and 37 kDa peptides and 31-30 kDa and 26-25 kDa peptide doublets, were identified from their ability to bind {sup 125}I calsequestrin (CS) in the presence of EGTA. The CS-binding peptides are specifically associated with the junctional membrane of TC, since they could not be detected in junctional transverse tubules and in longitudinal SR fragments. The 31-30 kDa peptide doublet, exclusively, did not bind CS in the presence of Ca{sup 2+}. Thus, different types of protein-protein interactions appear to be involved in selective binding of CS to junctional TC.

  18. Application of shotgun proteomics for discovery-driven protein-protein interaction.

    PubMed

    Goto-Silva, Livia; Maliga, Zoltan; Slabicki, Mikolaj; Murillo, Jimmy Rodriguez; Junqueira, Magno

    2014-01-01

    Affinity purification of protein complexes and identification of co-purified proteins by mass spectrometry is a powerful method to discover novel protein-protein interactions. Application of this method to the study of biological systems often requires the ability to process a large number of samples. Hence, there is great need to generate proteomic workflows compatible with large-scale studies. The major goal of this protocol is to present a fast, reliable, and scalable method to characterize protein complexes by mass spectrometry to overcome the limitations of conventional geLC-MS/MS or MudPIT protocols. This method was successfully employed for the discovery and characterization of novel protein complexes in cultured yeast, mammalian cells, and mice.

  19. Protein-protein interactions from linear-scaling first-principles quantum-mechanical calculations

    NASA Astrophysics Data System (ADS)

    Cole, D. J.; Skylaris, C.-K.; Rajendra, E.; Venkitaraman, A. R.; Payne, M. C.

    2010-08-01

    A modification of the MM-PBSA technique for calculating binding affinities of biomolecular complexes is presented. Classical molecular dynamics is used to explore the motion of the extended interface between two peptides derived from the BRC4 repeat of BRCA2 and the eukaryotic recombinase RAD51. The resulting trajectory is sampled using the linear-scaling density functional theory code, onetep, to determine from first principles, and with high computational efficiency, the relative free energies of binding of the ~2800 atom receptor-ligand complexes. This new method provides the basis for computational interrogation of protein-protein and protein-ligand interactions within fields ranging from chemical biological studies to small-molecule binding behaviour, with both unprecedented chemical accuracy and affordable computational expense.

  20. Protein-Protein Interactions from Linear-Scaling First Principles Quantum Mechanical Calculations

    NASA Astrophysics Data System (ADS)

    Cole, Daniel; Skylaris, Chris-Kriton; Rajendra, Eeson; Venkitaraman, Ashok; Payne, Mike

    2010-03-01

    A modification of the MM-PBSA technique for calculating binding affinities of biomolecular complexes is presented. Classical molecular dynamics is used to explore the motion of the extended interface between two peptides derived from the BRC4 repeat of BRCA2 and the eukaryotic recombinase RAD51. The resulting trajectory is sampled using the linear-scaling density functional theory code, onetep, to determine from first principles, and with high computational efficiency, the relative free energies of binding of the ˜2800 atom receptor-ligand complexes. This new method provides the basis for computational interrogation of protein-protein and protein-ligand interactions, within fields ranging from chemical biological studies to small molecule binding behaviour, with both unprecedented chemical accuracy and affordable computational expense.

  1. Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space.

    PubMed

    Zhang, Xu; Betzi, Stéphane; Morelli, Xavier; Roche, Philippe

    2014-07-01

    One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.

  2. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery.

    PubMed

    Murakami, Yoichi; Tripathi, Lokesh P; Prathipati, Philip; Mizuguchi, Kenji

    2017-03-29

    Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.

  3. Plant nuclear hormone receptors: a role for small molecules in protein-protein interactions.

    PubMed

    Lumba, Shelley; Cutler, Sean; McCourt, Peter

    2010-01-01

    Plant hormones are a group of chemically diverse small molecules that direct processes ranging from growth and development to biotic and abiotic stress responses. Surprisingly, genome analyses suggest that classic animal nuclear hormone receptor homologs do not exist in plants. It now appears that plants have co-opted several protein families to perceive hormones within the nucleus. In one solution to the problem, the hormones auxin and jasmonate (JA) act as “molecular glue” that promotes protein-protein interactions between receptor F-boxes and downstream corepressor targets. In another solution, gibberellins (GAs) bind and elicit a conformational change in a novel soluble receptor family related to hormone-sensitive lipases. Abscisic acid (ABA), like GA, also acts through an allosteric mechanism involving a START-domain protein. The molecular identification of plant nuclear hormone receptors will allow comparisons with animal nuclear receptors and testing of fundamental questions about hormone function in plant development and evolution.

  4. Potential toxicity of graphene to cell functions via disrupting protein-protein interactions.

    PubMed

    Luan, Binquan; Huynh, Tien; Zhao, Lin; Zhou, Ruhong

    2015-01-27

    While carbon-based nanomaterials such as graphene and carbon nanotubes (CNTs) have become popular in state-of-the-art nanotechnology, their biological safety and underlying molecular mechanism is still largely unknown. Experimental studies have been focused at the cellular level and revealed good correlations between cell's death and the application of CNTs or graphene. Using large-scale all-atom molecular dynamics simulations, we theoretically investigate the potential toxicity of graphene to a biological cell at molecular level. Simulation results show that the hydrophobic protein-protein interaction (or recognition) that is essential to biological functions can be interrupted by a graphene nanosheet. Due to the hydrophobic nature of graphene, it is energetically favorable for a graphene nanosheet to enter the hydrophobic interface of two contacting proteins, such as a dimer. The forced separation of two functional proteins can disrupt the cell's metabolism and even lead to the cell's mortality.

  5. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions.

    PubMed

    Laraia, Luca; McKenzie, Grahame; Spring, David R; Venkitaraman, Ashok R; Huggins, David J

    2015-06-18

    Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.

  6. Protein-protein interaction networks studies and importance of 3D structure knowledge.

    PubMed

    Lu, Hui-Chun; Fornili, Arianna; Fraternali, Franca

    2013-12-01

    Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

  7. Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions.

    PubMed

    Rietman, Edward A; Platig, John; Tuszynski, Jack A; Lakka Klement, Giannoula

    2016-06-01

    Thermodynamics is an important driving factor for chemical processes and for life. Earlier work has shown that each cancer has its own molecular signaling network that supports its life cycle and that different cancers have different thermodynamic entropies characterizing their signaling networks. The respective thermodynamic entropies correlate with 5-year survival for each cancer. We now show that by overlaying mRNA transcription data from a specific tumor type onto a human protein-protein interaction network, we can derive the Gibbs free energy for the specific cancer. The Gibbs free energy correlates with 5-year survival (Pearson correlation of -0.7181, p value of 0.0294). Using an expression relating entropy and Gibbs free energy to enthalpy, we derive an empirical relation for cancer network enthalpy. Combining this with previously published results, we now show a complete set of extensive thermodynamic properties and cancer type with 5-year survival.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    PubMed

    Mousavian, Zaynab; Díaz, José; Masoudi-Nejad, Ali

    2016-03-01

    By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Constrained Cyclic Peptides as Immunomodulatory Inhibitors of the CD2:CD58 Protein-Protein Interaction.

    PubMed

    Sable, Rushikesh; Durek, Thomas; Taneja, Veena; Craik, David J; Pallerla, Sandeep; Gauthier, Ted; Jois, Seetharama

    2016-08-19

    The interaction between the cell-cell adhesion proteins CD2 and CD58 plays a crucial role in lymphocyte recruitment to inflammatory sites, and inhibitors of this interaction have potential as immunomodulatory drugs in autoimmune diseases. Peptides from the CD2 adhesion domain were designed to inhibit CD2:CD58 interactions. To improve the stability of the peptides, β-sheet epitopes from the CD2 region implicated in CD58 recognition were grafted into the cyclic peptide frameworks of sunflower trypsin inhibitor and rhesus theta defensin. The designed multicyclic peptides were evaluated for their ability to modulate cell-cell interactions in three different cell adhesion assays, with one candidate, SFTI-a, showing potent activity in the nanomolar range (IC50: 51 nM). This peptide also suppresses the immune responses in T cells obtained from mice that exhibit the autoimmune disease rheumatoid arthritis. SFTI-a was resistant to thermal denaturation, as judged by circular dichroism spectroscopy and mass spectrometry, and had a half-life of ∼24 h in human serum. Binding of this peptide to CD58 was predicted by molecular docking studies and experimentally confirmed by surface plasmon resonance experiments. Our results suggest that cyclic peptides from natural sources are promising scaffolds for modulating protein-protein interactions that are typically difficult to target with small-molecule compounds.

  11. PPIcons: identification of protein-protein interaction sites in selected organisms.

    PubMed

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal; Plewczynski, Dariusz

    2013-09-01

    The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein-protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively.

  12. RefSOFI for Mapping Nanoscale Organization of Protein-protein Interactions in Living cells

    PubMed Central

    Hertel, Fabian; Mo, Gary C. H.; Duwé, Sam; Dedecker, Peter; Zhang, Jin

    2015-01-01

    Summary It has become increasingly clear that protein-protein interactions (PPIs) are compartmentalized in nanoscale domains that define the biochemical architecture of the cell. Despite tremendous advances in super-resolution imaging, strategies to observe PPIs at sufficient resolution to discern their organization are just emerging. Here we describe a strategy in which PPIs induce reconstitution of fluorescent proteins (FPs) that are capable of exhibiting single-molecule fluctuations suitable for Stochastic Optical Fluctuation Imaging (SOFI). Subsequently, spatial maps of these interactions can be resolved in super-resolution in living cells. Using this strategy, termed reconstituted fluorescence-based SOFI (refSOFI), we investigated the interaction between the endoplasmic reticulum Ca2+ sensor STIM1 and the pore-forming channel subunit ORAI1, a crucial process in store-operated Ca2+ entry (SOCE). Stimulating SOCE does not appear to change the size of existing STIM1/ORAI1 interaction puncta at the ER-plasma membrane junctions, but results in an apparent increase in the number of interaction puncta. PMID:26748717

  13. Immunoprecipitation and Mass Spectrometry Defines an Extensive RBM45 Protein-Protein Interaction Network

    PubMed Central

    Li, Yang; Collins, Mahlon; An, Jiyan; Geiser, Rachel; Tegeler, Tony; Tsantilas, Kristine; Garcia, Krystine; Pirrotte, Patrick; Bowser, Robert

    2016-01-01

    The pathological accumulation of RNA-binding proteins (RBPs) within inclusion bodies is a hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). RBP aggregation results in both toxic gain and loss of normal function. Determining the protein binding partners and normal functions of disease-associated RBPs is necessary to fully understand molecular mechanisms of RBPs in disease. Herein, we characterized the protein-protein interactions (PPIs) of RBM45, a RBP that localizes to inclusions in ALS/FTLD. Using immunoprecipitation coupled to mass spectrometry (IP-MS), we identified 132 proteins that specifically interact with RBM45 within HEK293 cells. Select PPIs were validated by immunoblot and immunocytochemistry, demonstrating that RBM45 associates with a number of other RBPs primarily via RNA-dependent interactions in the nucleus. Analysis of the biological processes and pathways associated with RBM45-interacting proteins indicates enrichment for nuclear RNA processing/splicing via association with hnRNP proteins and cytoplasmic RNA translation via eiF2 and eiF4 pathways. Moreover, several other ALS-linked RBPs, including TDP-43, FUS, Matrin-3, and hnRNP-A1, interact with RBM45, consistent with prior observations of these proteins within intracellular inclusions in ALS/FTLD. Taken together, our results define a PPI network for RBM45, suggest novel functions for this protein, and provide new insights into the contributions of RBM45 to neurodegeneration in ALS/FTLD. PMID:26979993

  14. Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

    PubMed Central

    2011-01-01

    Background Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. Results We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. Conclusions The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting. PMID:21679454

  15. Deriving Heterospecific Self-Assembling Protein-Protein Interactions Using a Computational Interactome Screen.

    PubMed

    Crooks, Richard O; Baxter, Daniel; Panek, Anna S; Lubben, Anneke T; Mason, Jody M

    2016-01-29

    Interactions between naturally occurring proteins are highly specific, with protein-network imbalances associated with numerous diseases. For designed protein-protein interactions (PPIs), required specificity can be notoriously difficult to engineer. To accelerate this process, we have derived peptides that form heterospecific PPIs when combined. This is achieved using software that generates large virtual libraries of peptide sequences and searches within the resulting interactome for preferentially interacting peptides. To demonstrate feasibility, we have (i) generated 1536 peptide sequences based on the parallel dimeric coiled-coil motif and varied residues known to be important for stability and specificity, (ii) screened the 1,180,416 member interactome for predicted Tm values and (iii) used predicted Tm cutoff points to isolate eight peptides that form four heterospecific PPIs when combined. This required that all 32 hypothetical off-target interactions within the eight-peptide interactome be disfavoured and that the four desired interactions pair correctly. Lastly, we have verified the approach by characterising all 36 pairs within the interactome. In analysing the output, we hypothesised that several sequences are capable of adopting antiparallel orientations. We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings. Our approach can be used to derive increasingly large and therefore complex sets of heterospecific PPIs with a wide range of potential downstream applications from disease modulation to the design of biomaterials and peptides in synthetic biology. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

    PubMed Central

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

    2012-01-01

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

  17. Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods.

    PubMed

    Chen, T Scott; Keating, Amy E

    2012-07-01

    Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.

  18. Protein-Protein Interactions in Clathrin Vesicular Assembly: Radial Distribution of Evolutionary Constraints in Interfaces

    PubMed Central

    Gadkari, Rupali A.; Srinivasan, Narayanaswamy

    2012-01-01

    In eukaryotic organisms clathrin-coated vesicles are instrumental in the processes of endocytosis as well as intracellular protein trafficking. Hence, it is important to understand how these vesicles have evolved across eukaryotes, to carry cargo molecules of varied shapes and sizes. The intricate nature and functional diversity of the vesicles are maintained by numerous interacting protein partners of the vesicle system. However, to delineate functionally important residues participating in protein-protein interactions of the assembly is a daunting task as there are no high-resolution structures of the intact assembly available. The two cryoEM structures closely representing intact assembly were determined at very low resolution and provide positions of Cα atoms alone. In the present study, using the method developed by us earlier, we predict the protein-protein interface residues in clathrin assembly, taking guidance from the available low-resolution structures. The conservation status of these interfaces when investigated across eukaryotes, revealed a radial distribution of evolutionary constraints, i.e., if the members of the clathrin vesicular assembly can be imagined to be arranged in spherical manner, the cargo being at the center and clathrins being at the periphery, the detailed phylogenetic analysis of these members of the assembly indicated high-residue variation in the members of the assembly closer to the cargo while high conservation was noted in clathrins and in other proteins at the periphery of the vesicle. This points to the strategy adopted by the nature to package diverse proteins but transport them through a highly conserved mechanism. PMID:22384024

  19. The food colorant erythrosine is a promiscuous protein-protein interaction inhibitor.

    PubMed

    Ganesan, Lakshmi; Margolles-Clark, Emilio; Song, Yun; Buchwald, Peter

    2011-03-15

    Following our observation that erythrosine B (FD&C Red No. 3) is a relatively potent inhibitor of the TNF-R-TNFα and CD40-CD154 protein-protein interactions, we investigated whether this inhibitory activity extends to any other protein-protein interactions (PPI) as well as whether any other approved food colors possess such inhibitory activity. We found erythrosine, a poly-iodinated xanthene dye, to be a non-specific promiscuous inhibitor of a number of PPIs within the tumor necrosis factor superfamily (TNF-R-TNFα, CD40-CD154, BAFF-R-BAFF, RANK-RANKL, OX40-OX40L, 4-1BB-4-1BBL) as well as outside of it (EGF-R-EGF) with a remarkably consistent median inhibitory concentration (IC(50)) in the 2-20 μM (approximately 2-20mg/L) range. In agreement with this, erythrosine also showed cellular effects including clear cytotoxic effects around this concentration range (IC₅₀≈50 μM). Among the seven FDA-approved food colorants, only erythrosine showed consistent PPI inhibitory activity in the sub-100 μM range, which might also explain (at least partially) why it also has the lowest approved acceptable daily intake (ADI) (0.1 mg/kg body weight/day). Among a number of xanthene structural analogs of erythrosine tested for activity, rose Bengal, a food colorant approved in Japan, showed similar, maybe even more pronounced, promiscuous inhibitory activity, whereas fluorescein was inactive and gallein, phloxine, and eosin were somewhat active in some of the assays.

  20. CapsidMaps: Protein-protein interaction pattern discovery platform for the structural analysis of virus capsids using Google Maps

    PubMed Central

    Carrillo-Tripp, Mauricio; Montiel-García, Daniel Jorge; Brooks, Charles L.; Reddy, Vijay

    2016-01-01

    Structural analysis and visualization of protein-protein interactions is a challenging task since it is difficult to appreciate easily the extent of all contacts made by the residues forming the interfaces. In the case of viruses, structural analysis becomes even more demanding because several interfaces coexist and, in most cases, these are formed by hundreds of contacting residues that belong to multiple interacting coat proteins. CapsidMaps is an interactive analysis and visualization tool that is designed to benefit the structural virology community. Developed as an improved extension of the φ-ψ Explorer, here we describe the details of its design and implementation. We present results of analysis of a spherical virus to showcase the features and utility of the new tool. CapsidMaps also facilitates the comparison of quaternary interactions between two spherical virus particles by computing a similarity (S)-score. The tool can also be used to identify residues that are solvent exposed and in the process of locating antigenic epitope regions as well as residues forming the inside surface of the capsid that interact with the nucleic acid genome. CapsidMaps is part of the VIPERdb Science Gateway, and is freely available as a web-based and cross-browser compliant application at http://viperdb.scripps.edu. PMID:25697908

  1. CapsidMaps: protein-protein interaction pattern discovery platform for the structural analysis of virus capsids using Google Maps.

    PubMed

    Carrillo-Tripp, Mauricio; Montiel-García, Daniel Jorge; Brooks, Charles L; Reddy, Vijay S

    2015-04-01

    Structural analysis and visualization of protein-protein interactions is a challenging task since it is difficult to appreciate easily the extent of all contacts made by the residues forming the interfaces. In the case of viruses, structural analysis becomes even more demanding because several interfaces coexist and, in most cases, these are formed by hundreds of contacting residues that belong to multiple interacting coat proteins. CapsidMaps is an interactive analysis and visualization tool that is designed to benefit the structural virology community. Developed as an improved extension of the φ-ψ Explorer, here we describe the details of its design and implementation. We present results of analysis of a spherical virus to showcase the features and utility of the new tool. CapsidMaps also facilitates the comparison of quaternary interactions between two spherical virus particles by computing a similarity (S)-score. The tool can also be used to identify residues that are solvent exposed and in the process of locating antigenic epitope regions as well as residues forming the inside surface of the capsid that interact with the nucleic acid genome. CapsidMaps is part of the VIPERdb Science Gateway, and is freely available as a web-based and cross-browser compliant application at http://viperdb.scripps.edu. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Effect of the quality of the interaction data on predicting protein function from protein-protein interactions.

    PubMed

    Ni, Qing-Shan; Wang, Zheng-Zhi; Li, Gang-Guo; Wang, Guang-Yun; Zhao, Ying-Jie

    2009-03-01

    Protein function prediction is an important issue in the post-genomic era. When protein function is deduced from protein interaction data, the traditional methods treat each interaction sample equally, where the qualities of the interaction samples are seldom taken into account. In this paper, we investigate the effect of the quality of protein-protein interaction data on predicting protein function. Moreover, two improved methods, weight neighbour counting method (WNC) and weight chi-square method (WCHI), are proposed by considering the quality of interaction samples with the neighbour counting method (NC) and chi-square method (CHI). Experimental results have shown that the qualities of interaction samples affect the performances of protein function prediction methods seriously. It is also demonstrated that WNC and WCHI methods outperform NC and CHI methods in protein function prediction when example weights are chosen properly.

  3. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

    PubMed Central

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    Background Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. Description We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactions as well as their protein-level interactions using the model cyanobacterium, Synechocystis sp. PCC 6803. It predicts the protein-protein interactions using public interaction databases that contain mutually complementary and redundant data. Furthermore, SynechoNET provides information on transmembrane topology, signal peptide, and domain structure in order to support the analysis of regulatory membrane proteins. Such biological information can be queried and visualized in user-friendly web interfaces that include the interactive network viewer and search pages by keyword and functional category. Conclusion SynechoNET is an integrated protein-protein interaction database designed to analyze regulatory membrane proteins in cyanobacteria. It provides a platform for biologists to extend the genomic data of cyanobacteria by predicting interaction partners, membrane association, and membrane topology of Synechocystis proteins. SynechoNET is freely available at or directly at . PMID:18315852

  4. Targeting protein-protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies?

    PubMed Central

    Cierpicki, Tomasz; Grembecka, Jolanta

    2015-01-01

    Summary Over the past several years, there has been an increasing research effort focused on inhibition of protein-protein interactions (PPIs) to develop novel therapeutic approaches for cancer, including hematologic malignancies. These efforts have led to development of small molecule inhibitors of PPIs, some of which already advanced to the stage of clinical trials while others are at different stages of pre-clinical optimization, emphasizing PPIs as an emerging and attractive class of drug targets. Here, we review several examples of recently developed inhibitors of protein-protein interactions highly relevant to hematologic cancers. We address the existing skepticism about feasibility of targeting PPIs and emphasize potential therapeutic benefit from blocking PPIs in hematologic malignancies. We then use these examples to discuss the approaches for successful identification of PPI inhibitors and provide analysis of the protein-protein interfaces, with the goal to address ‘druggability’ of new PPIs relevant to hematology. We discuss lessons learned to improve the success of targeting new protein-protein interactions and evaluate prospects and limits of the research in this field. We conclude that not all PPIs are equally tractable for blocking by small molecules, and detailed analysis of PPI interfaces is critical for selection of those with the highest chance of success. Together, our analysis uncovers patterns that should help to advance drug discovery in hematologic malignancies by successful targeting of new protein-protein interactions. PMID:25510283

  5. Techniques for the Analysis of Protein-Protein Interactions in Vivo1[OPEN

    PubMed Central

    Xing, Shuping; Wallmeroth, Niklas; Berendzen, Kenneth W.

    2016-01-01

    Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique. PMID:27208310

  6. Molecular tweezers modulate 14-3-3 protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Bier, David; Rose, Rolf; Bravo-Rodriguez, Kenny; Bartel, Maria; Ramirez-Anguita, Juan Manuel; Dutt, Som; Wilch, Constanze; Klärner, Frank-Gerrit; Sanchez-Garcia, Elsa; Schrader, Thomas; Ottmann, Christian

    2013-03-01

    Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins—a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)—in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions.

  7. A General System for Studying Protein-Protein Interactions in Gram-Negative Bacteria

    SciTech Connect

    Pelletier, Dale A; Auberry, Deanna L; Buchanan, Michelle V; Cannon, Bill; Daly, Don S.; Doktycz, Mitchel John; Foote, Linda J; Hervey, IV, William Judson; Hooker, Brian; Hurst, Gregory {Greg} B; Kennel, Steve J; Lankford, Patricia K; Larimer, Frank W; Lu, Tse-Yuan S; McDonald, W Hayes; McKeown, Catherine K; Morrell-Falvey, Jennifer L; Owens, Elizabeth T; Schmoyer, Denise D; Shah, Manesh B; Wiley, Steven; Wang, Yisong; Gilmore, Jason

    2008-01-01

    Abstract One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged bait proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

  8. Dynamic imaging of protein-protein interactions by MP-FLIM

    NASA Astrophysics Data System (ADS)

    Ameer-Beg, Simon M.; Peter, Marion; Keppler, Melanie D.; Prag, Soren; Barber, Paul R.; Ng, Tony C.; Vojnovic, Borivoj

    2005-03-01

    The spatio-temporal localization of molecular interactions within cells in situ is of great importance in elucidating the key mechanisms in regulation of fundamental process within the cell. Measurements of such near-field localization of protein complexes may be achieved by the detection of fluorescence (or Forster) resonance energy transfer (FRET) between protein-conjugated fluorophores. We demonstrate the applicability of time-correlated single photon counting multiphoton microscopy to the spatio-temporal localization of protein-protein interactions in live and fixed cell populations. Intramolecular interactions between protein hetero-dimers are investigated using green fluorescent protein variants. We present an improved monomeric form of the red fluorescent protein, mRFP1, as the acceptor in biological fluorescence resonance energy transfer (FRET) experiments using the enhanced green fluorescent protein as donor. We find particular advantage in using this fluorophore pair for quantitative measurements of FRET. The technique was exploited to demonstrate a novel receptor-kinase interaction between the chemokine receptor (CXCR4) and protein kinase C (PKC) α in carcinoma cells for both live and fixed cell experiments.

  9. Effective cell-free drug screening protocol for protein-protein interaction.

    PubMed

    Ashkenazi, Shaked; Plotnikov, Alexander; Bahat, Anat; Dikstein, Rivka

    2017-09-01

    Specific protein-protein interaction (PPI) is an essential feature of many cellular processes however, targeting these interactions by small molecules is highly challenging due to the nature of the interaction interface. Thus, screening for PPI inhibitors requires enormous number of compounds. Here we describe a simple and improved protocol designed for a search of direct PPI inhibitors. We engineered a bacterial expression system for the split-Renilla luciferase (RL) complementation assay that monitors PPI. This enables production of large quantities of the RL fusion proteins in a simple and cost effective manner that is suitable for very large screens. Subsequently, inhibitory compounds are analyzed in a similar complementation assay in living cultured mammalian cells to select for those that can penetrate cells. We applied this method to NF-κB, a family of dimeric transcription factors that plays central roles in immune responses, cell survival and aging, and its dysregulation is linked to many pathological states. This strategy led to the identification of several direct NF-κB inhibitors. As the described protocol is very straightforward and robust it may be suitable for many pairs of interacting proteins. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. A protein-protein interaction map of yeast RNA polymerase III.

    PubMed

    Flores, A; Briand, J F; Gadal, O; Andrau, J C; Rubbi, L; Van Mullem, V; Boschiero, C; Goussot, M; Marck, C; Carles, C; Thuriaux, P; Sentenac, A; Werner, M

    1999-07-06

    The structure of the yeast RNA polymerase (pol) III was investigated by exhaustive two-hybrid screening using a library of random genomic fragments fused to the Gal4 activation domain. This procedure allowed us to identify contacts between individual polypeptides, localize the contact domains, and deduce a protein-protein interaction map of the multisubunit enzyme. In all but one case, pol III subunits were able to interact in vivo with one or sometimes two partner subunits of the enzyme or with subunits of TFIIIC. Four subunits that are common to pol I, II, and III (ABC27, ABC14.5, ABC10alpha, and ABC10beta), two that are common to pol I and III (AC40 and AC19), and one pol III-specific subunit (C11) can associate with defined regions of the two large subunits. These regions overlapped with highly conserved domains. C53, a pol III-specific subunit, interacted with a 37-kDa polypeptide that copurifies with the enzyme and therefore appears to be a unique pol III subunit (C37). Together with parallel interaction studies based on dosage-dependent suppression of conditional mutants, our data suggest a model of the pol III preinitiation complex.

  11. Molecular tweezers modulate 14-3-3 protein-protein interactions.

    PubMed

    Bier, David; Rose, Rolf; Bravo-Rodriguez, Kenny; Bartel, Maria; Ramirez-Anguita, Juan Manuel; Dutt, Som; Wilch, Constanze; Klärner, Frank-Gerrit; Sanchez-Garcia, Elsa; Schrader, Thomas; Ottmann, Christian

    2013-03-01

    Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins--a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)--in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions.

  12. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    PubMed

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  13. Elucidating glycosaminoglycan-protein-protein interactions using carbohydrate microarray and computational approaches.

    PubMed

    Rogers, Claude J; Clark, Peter M; Tully, Sarah E; Abrol, Ravinder; Garcia, K Christopher; Goddard, William A; Hsieh-Wilson, Linda C

    2011-06-14

    Glycosaminoglycan polysaccharides play critical roles in many cellular processes, ranging from viral invasion and angiogenesis to spinal cord injury. Their diverse biological activities are derived from an ability to regulate a remarkable number of proteins. However, few methods exist for the rapid identification of glycosaminoglycan-protein interactions and for studying the potential of glycosaminoglycans to assemble multimeric protein complexes. Here, we report a multidisciplinary approach that combines new carbohydrate microarray and computational modeling methodologies to elucidate glycosaminoglycan-protein interactions. The approach was validated through the study of known protein partners for heparan and chondroitin sulfate, including fibroblast growth factor 2 (FGF2) and its receptor FGFR1, the malarial protein VAR2CSA, and tumor necrosis factor-α (TNF-α). We also applied the approach to identify previously undescribed interactions between a specific sulfated epitope on chondroitin sulfate, CS-E, and the neurotrophins, a critical family of growth factors involved in the development, maintenance, and survival of the vertebrate nervous system. Our studies show for the first time that CS is capable of assembling multimeric signaling complexes and modulating neurotrophin signaling pathways. In addition, we identify a contiguous CS-E-binding site by computational modeling that suggests a potential mechanism to explain how CS may promote neurotrophin-tyrosine receptor kinase (Trk) complex formation and neurotrophin signaling. Together, our combined microarray and computational modeling methodologies provide a general, facile means to identify new glycosaminoglycan-protein-protein interactions, as well as a molecular-level understanding of those complexes.

  14. Transcription Factor Functional Protein-Protein Interactions in Plant Defense Responses

    PubMed Central

    Alves, Murilo S.; Dadalto, Silvana P.; Gonçalves, Amanda B.; de Souza, Gilza B.; Barros, Vanessa A.; Fietto, Luciano G.

    2014-01-01

    Responses to biotic stress in plants lead to dramatic reprogramming of gene expression, favoring stress responses at the expense of normal cellular functions. Transcription factors are master regulators of gene expression at the transcriptional level, and controlling the activity of these factors alters the transcriptome of the plant, leading to metabolic and phenotypic changes in response to stress. The functional analysis of interactions between transcription factors and other proteins is very important for elucidating the role of these transcriptional regulators in different signaling cascades. In this review, we present an overview of protein-protein interactions for the six major families of transcription factors involved in plant defense: basic leucine zipper containing domain proteins (bZIP), amino-acid sequence WRKYGQK (WRKY), myelocytomatosis related proteins (MYC), myeloblastosis related proteins (MYB), APETALA2/ ETHYLENE-RESPONSIVE ELEMENT BINDING FACTORS (AP2/EREBP) and no apical meristem (NAM), Arabidopsis transcription activation factor (ATAF), and cup-shaped cotyledon (CUC) (NAC). We describe the interaction partners of these transcription factors as molecular responses during pathogen attack and the key components of signal transduction pathways that take place during plant defense responses. These interactions determine the activation or repression of response pathways and are crucial to understanding the regulatory networks that modulate plant defense responses. PMID:28250372

  15. Development of a Capillary Electrophoresis Platform for Identifying Inhibitors of Protein-Protein Interactions

    PubMed Central

    Rauch, Jennifer N.; Nie, Jing; Buchholz, Tonia J.; Gestwicki, Jason E.; Kennedy, Robert T.

    2013-01-01

    Methods for identifying chemical inhibitors of protein-protein interactions (PPIs) are often prone to discovery of false positives, particularly those caused by molecules that induce protein aggregation. Thus, there is interest in developing new platforms that might allow earlier identification of these problematic compounds. Capillary electrophoresis (CE) has been evaluated as a method to screen for PPI inhibitors using the challenging system of Hsp70 interacting with its co-chaperone Bag3. In the method, Hsp70 is labeled with a fluorophore, mixed with Bag3, and the resulting bound and free Hsp70 separated and detected by CE with laser-induced fluorescence detection. The method used a chemically modified CE capillary to prevent protein adsorption. Inhibitors of the Hsp70-Bag3 interaction were detected by observing a reduction in the bound to free ratio. The method was used to screen a library of 3,443 compounds and results compared to those from a flow cytometry protein interaction assay. CE was found to produce a lower hit rate with more compounds that reconfirmed in subsequent testing suggesting greater specificity. This finding was attributed to use of electropherograms to detect artifacts such as aggregators and to differences in protein modifications required to perform the different assays. Increases in throughput are required to make the CE method suitable for primary screens but at the current stage of development it is attractive as a secondary screen to test hits found by higher throughput methods. PMID:24060167

  16. Development of a capillary electrophoresis platform for identifying inhibitors of protein-protein interactions.

    PubMed

    Rauch, Jennifer N; Nie, Jing; Buchholz, Tonia J; Gestwicki, Jason E; Kennedy, Robert T

    2013-10-15

    Methods for identifying chemical inhibitors of protein-protein interactions (PPIs) are often prone to discovery of false positives, particularly those caused by molecules that induce protein aggregation. Thus, there is interest in developing new platforms that might allow earlier identification of these problematic compounds. Capillary electrophoresis (CE) has been evaluated as a method to screen for PPI inhibitors using the challenging system of Hsp70 interacting with its co-chaperone Bag3. In the method, Hsp70 is labeled with a fluorophore, mixed with Bag3, and the resulting bound and free Hsp70 are separated and detected by CE with laser-induced fluorescence detection. The method used a chemically modified CE capillary to prevent protein adsorption. Inhibitors of the Hsp70-Bag3 interaction were detected by observing a reduction in the bound-to-free ratio. The method was used to screen a library of 3443 compounds, and the results were compared to those from a flow cytometry protein interaction assay. CE was found to produce a lower hit rate with more compounds that were reconfirmed in subsequent testing, suggesting greater specificity. This finding was attributed to the use of electropherograms to detect artifacts such as aggregators and to differences in protein modifications required to perform the different assays. Increases in throughput are required to make the CE method suitable for primary screens, but at the current stage of development it is attractive as a secondary screen to test hits found by higher-throughput methods.

  17. Virtual screening and experimental validation reveal novel small-molecule inhibitors of 14-3-3 protein-protein interactions.

    PubMed

    Thiel, Philipp; Röglin, Lars; Meissner, Nicole; Hennig, Sven; Kohlbacher, Oliver; Ottmann, Christian

    2013-10-04

    We report first non-covalent and exclusively extracellular inhibitors of 14-3-3 protein-protein interactions identified by virtual screening. Optimization by crystal structure analysis and in vitro binding assays yielded compounds capable of disrupting the interaction of 14-3-3σ with aminopeptidase N in a cellular assay.

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

    PubMed Central

    Lin, Peng-Lin; Yu, Ya-Wen

    2016-01-01

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

  19. Defining subdomains of the K domain important for protein-protein interactions of plant MADS proteins.

    PubMed

    Yang, Yingzhen; Jack, Thomas

    2004-05-01

    The MADS proteins APETALA3 (AP3), PISTILLATA (PI), SEPALLATAI (SEPI), SEP2, SEP3, AGAMOUS, and APETALA are required for proper floral organ identity in Arabidopsis flowers. All of these floral MADS proteins conserve two domains: the MADS domain that mediates DNA binding and dimerization, and the K domain that mediates protein protein interaction. The K domain is postulated to form a several amphipathic c-helices referred to as K1, K2, and K3. The K1 and K2 helicies are located entirely within the K domain while the K3 helix spans the K domain-C domain boundary. Here we report on our studies on the interactions of the B class MADS proteins AP3 and PI with the E class MADS proteins SEP1, SEP2, and SEP3. A comparative analysis of mutants in the K domain reveals that the subdomains mediating the PI/AP3 interaction are different from the subdomains mediating the PI/SEP3 (or PI/SEP1) interaction. The strong PI/SEP3 (or PI/SEP1) interaction requires K2, part of K3, and the interhelical region between K1 and K2. By contrast, K1, K2 and the region between K1 and K2 are important for strong AP3/PI interaction. Most of the K3 helix does not appear to be important for either the PI/AP3 or the PI/SEP3 (or PI/SEP1) interaction. Conserved hydrophobic positions are most important for the strength of both PI/AP3 and PI/SEP3 dimerization, though ionic and/or polar interactions appear to play a secondary role.

  20. Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

    DOE PAGES

    Shatsky, Maxim; Dong, Ming; Liu, Haichuan; ...

    2016-04-20

    Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification ofmore » endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.« less

  1. Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions*

    PubMed Central

    Shatsky, Maxim; Dong, Ming; Liu, Haichuan; Yang, Lee Lisheng; Choi, Megan; Singer, Mary E.; Geller, Jil T.; Fisher, Susan J.; Hall, Steven C.; Hazen, Terry C.; Brenner, Steven E.; Butland, Gareth; Jin, Jian; Witkowska, H. Ewa; Chandonia, John-Marc; Biggin, Mark D.

    2016-01-01

    Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR. PMID:27099342

  2. Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

    SciTech Connect

    Shatsky, Maxim; Dong, Ming; Liu, Haichuan; Yang, Lee Lisheng; Choi, Megan; Singer, Mary; Geller, Jil; Fisher, Susan; Hall, Steven; Hazen, Terry C.; Brenner, Steven; Butland, Gareth; Jin, Jian; Witkowska, H. Ewa; Chandonia, John-Marc; Biggin, Mark D.

    2016-04-20

    Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

  3. Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia

    PubMed Central

    Yuan, Xuye; Chen, Jiajia; Lin, Yuxin; Li, Yin; Xu, Lihua; Chen, Luonan; Hua, Haiying; Shen, Bairong

    2017-01-01

    Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia. PMID:28243332

  4. Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

    PubMed

    Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

    2008-10-01

    Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

  5. An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology.

    PubMed

    Jain, Shobhit; Bader, Gary D

    2010-11-15

    Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.

  6. An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

    PubMed Central

    2010-01-01

    Background Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity. Results We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs. Conclusions The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations. PMID:21078182

  7. Efficient extraction of protein-protein interactions from full-text articles.

    PubMed

    Hakenberg, Jörg; Leaman, Robert; Vo, Nguyen Ha; Jonnalagadda, Siddhartha; Sullivan, Ryan; Miller, Christopher; Tari, Luis; Baral, Chitta; Gonzalez, Graciela

    2010-01-01

    Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for protein-named entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions, and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that

  8. Molecular modeling of the inhibition of protein-protein interactions with small molecules: The IL2-IL2Rα case

    NASA Astrophysics Data System (ADS)

    Pieraccini, Stefano; De Gonda, Riccardo; Sironi, Maurizio

    2011-12-01

    Developing drug like molecules targeting protein-protein interactions is one of the main goals of current medicinal chemistry. To drive the design process it is fundamental to locate those sites on the protein-protein contact surface that are more critical for protein binding, which are the most eligible targets to affect the protein complex formation. In this work we show how computational alanine scanning can be used to identify such critical sites and evaluate their interactions with small molecules designed to inhibit the complex formation. Complex of protein IL2 with IL2Rα and with some small molecule inhibitors are used as an example.

  9. Lanthanide-based imaging of protein-protein interactions in live cells.

    PubMed

    Rajendran, Megha; Yapici, Engin; Miller, Lawrence W

    2014-02-17

    In order to deduce the molecular mechanisms of biological function, it is necessary to monitor changes in the subcellular location, activation, and interaction of proteins within living cells in real time. Förster resonance energy-transfer (FRET)-based biosensors that incorporate genetically encoded, fluorescent proteins permit high spatial resolution imaging of protein-protein interactions or protein conformational dynamics. However, a nonspecific fluorescence background often obscures small FRET signal changes, and intensity-based biosensor measurements require careful interpretation and several control experiments. These problems can be overcome by using lanthanide [Tb(III) or Eu(III)] complexes as donors and green fluorescent protein (GFP) or other conventional fluorophores as acceptors. Essential features of this approach are the long-lifetime (approximately milliseconds) luminescence of Tb(III) complexes and time-gated luminescence microscopy. This allows pulsed excitation, followed by a brief delay, which eliminates nonspecific fluorescence before the detection of Tb(III)-to-GFP emission. The challenges of intracellular delivery, selective protein labeling, and time-gated imaging of lanthanide luminescence are presented, and recent efforts to investigate the cellular uptake of lanthanide probes are reviewed. Data are presented showing that conjugation to arginine-rich, cell-penetrating peptides (CPPs) can be used as a general strategy for the cellular delivery of membrane-impermeable lanthanide complexes. A heterodimer of a luminescent Tb(III) complex, Lumi4, linked to trimethoprim and conjugated to nonaarginine via a reducible disulfide linker rapidly (∼10 min) translocates into the cytoplasm of Maden Darby canine kidney cells from the culture medium. With this reagent, the intracellular interaction between GFP fused to FK506 binding protein 12 (GFP-FKBP12) and the rapamycin binding domain of mTOR fused to Escherichia coli dihydrofolate reductase (FRB

  10. Transient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity

    PubMed Central

    Bhattacharyya, Sanchari; Bershtein, Shimon; Yan, Jin; Argun, Tijda; Gilson, Amy I; Trauger, Sunia A; Shakhnovich, Eugene I

    2016-01-01

    Gene dosage toxicity (GDT) is an important factor that determines optimal levels of protein abundances, yet its molecular underpinnings remain unknown. Here, we demonstrate that overexpression of DHFR in E. coli causes a toxic metabolic imbalance triggered by interactions with several functionally related enzymes. Though deleterious in the overexpression regime, surprisingly, these interactions are beneficial at physiological concentrations, implying their functional significance in vivo. Moreover, we found that overexpression of orthologous DHFR proteins had minimal effect on all levels of cellular organization – molecular, systems, and phenotypic, in sharp contrast to E. coli DHFR. Dramatic difference of GDT between ‘E. coli’s self’ and ‘foreign’ proteins suggests the crucial role of evolutionary selection in shaping protein-protein interaction (PPI) networks at the whole proteome level. This study shows how protein overexpression perturbs a dynamic metabolon of weak yet potentially functional PPI, with consequences for the metabolic state of cells and their fitness. DOI: http://dx.doi.org/10.7554/eLife.20309.001 PMID:27938662

  11. Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study

    PubMed Central

    Barman, Ranjan Kumar; Jana, Tanmoy; Das, Santasabuj; Saha, Sudipto

    2015-01-01

    Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/. PMID:26717407

  12. Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study.

    PubMed

    Barman, Ranjan Kumar; Jana, Tanmoy; Das, Santasabuj; Saha, Sudipto

    2015-01-01

    Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.

  13. Protein-Protein Interaction Network and Subcellular Localization of the Arabidopsis Thaliana ESCRT Machinery.

    PubMed

    Richardson, Lynn G L; Howard, Alexander S M; Khuu, Nicholas; Gidda, Satinder K; McCartney, Andrew; Morphy, Brett J; Mullen, Robert T

    2011-01-01

    The endosomal sorting complex required for transport (ESCRT) consists of several multi-protein subcomplexes which assemble sequentially at the endosomal surface and function in multivesicular body (MVB) biogenesis. While ESCRT has been relatively well characterized in yeasts and mammals, comparably little is known about ESCRT in plants. Here we explored the yeast two-hybrid protein interaction network and subcellular localization of the Arabidopsis thaliana ESCRT machinery. We show that the Arabidopsis ESCRT interactome possesses a number of protein-protein interactions that are either conserved in yeasts and mammals or distinct to plants. We show also that most of the Arabidopsis ESCRT proteins examined at least partially localize to MVBs in plant cells when ectopically expressed on their own or co-expressed with other interacting ESCRT proteins, and some also induce abnormal MVB phenotypes, consistent with their proposed functional role(s) as part of the ESCRT machinery in Arabidopsis. Overall, our results help define the plant ESCRT machinery by highlighting both conserved and unique features when compared to ESCRT in other evolutionarily diverse organisms, providing a foundation for further exploration of ESCRT in plants.

  14. Anticancer osmium complex inhibitors of the HIF-1α and p300 protein-protein interaction

    PubMed Central

    Yang, Chao; Wang, Wanhe; Li, Guo-Dong; Zhong, Hai-Jing; Dong, Zhen-Zhen; Wong, Chun-Yuen; Kwong, Daniel W. J.; Ma, Dik-Lung; Leung, Chung-Hang

    2017-01-01

    The hypoxia inducible factor (HIF) pathway has been considered to be an attractive anti-cancer target. One strategy to inhibit HIF activity is through the disruption of the HIF-1α–p300 protein-protein interaction. We report herein the identification of an osmium(II) complex as the first metal-based inhibitor of the HIF-1α–p300 interaction. We evaluated the effect of complex 1 on HIF-1α signaling pathway in vitro and in cellulo by using the dual luciferase reporter assay, co-immunoprecipitation assay, and immunoblot assay. Complex 1 exhibited a dose-dependent inhibition of HRE-driven luciferase activity, with an IC50 value of 1.22 μM. Complex 1 interfered with the HIF-1α–p300 interaction as revealed by a dose-dependent reduction of p300 co-precipitated with HIF-1α as the concentration of complex 1 was increased. Complex 1 repressed the phosphorylation of SRC, AKT and STAT3, and had no discernible effect on the activity of NF-κB. We anticipate that complex 1 could be utilized as a promising scaffold for the further development of more potent HIF-1α inhibitors for anti-cancer treatment. PMID:28225008

  15. Kinetic and affinity predictions of a protein-protein interaction using multivariate experimental design.

    PubMed

    De Genst, Erwin; Areskoug, Daphne; Decanniere, Klaas; Muyldermans, Serge; Andersson, Karl

    2002-08-16

    We measured the influence of 14 mutations and 5 environmental variables (buffer perturbation) on the association and dissociation rate of a camel single domain antibody (cAb-Lys3) interacting with hen egg white lysozyme using a surface plasmon resonance-based biosensor. Based on this data set, we constructed quantitative predictive models for both kinetic (k(a) and k(d)) constants as for the affinity constant (K(d)). Mutations, after parameterization by quantitative descriptors, and buffers were selected using multivariate experimental design. These models were able to predict the corresponding parameters of four new variants of cAb-Lys3. Moreover, the models provide insights to the important chemical aspects of the interacting residues, which are difficult to deduce from the crystal structure. Our approach provides useful physicochemical information of protein-protein interactions in general. The information obtained from this kind of analysis complements and goes beyond that of conventional methods like alanine scanning and substitution by closely related amino acids. The mathematical modeling may contribute to a rational approach in the optimization of bio-molecules of biotechnological interest.

  16. Protein-Protein Interaction Investigated by Steered Molecular Dynamics: The TCR-pMHC Complex

    PubMed Central

    Cuendet, Michel A.; Michielin, Olivier

    2008-01-01

    We present a novel steered molecular dynamics scheme to induce the dissociation of large protein-protein complexes. We apply this scheme to study the interaction of a T cell receptor (TCR) with a major histocompatibility complex (MHC) presenting a peptide (p). Two TCR-pMHC complexes are considered, which only differ by the mutation of a single amino acid on the peptide; one is a strong agonist that produces T cell activation in vivo, while the other is an antagonist. We investigate the interaction mechanism from a large number of unbinding trajectories by analyzing van der Waals and electrostatic interactions and by computing energy changes in proteins and solvent. In addition, dissociation potentials of mean force are calculated with the Jarzynski identity, using an averaging method developed for our steering scheme. We analyze the convergence of the Jarzynski exponential average, which is hampered by the large amount of dissipative work involved and the complexity of the system. The resulting dissociation free energies largely underestimate experimental values, but the simulations are able to clearly differentiate between wild-type and mutated TCR-pMHC and give insights into the dissociation mechanism. PMID:18621828

  17. Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions.

    PubMed

    Morris, John H; Knudsen, Giselle M; Verschueren, Erik; Johnson, Jeffrey R; Cimermancic, Peter; Greninger, Alexander L; Pico, Alexander R

    2014-11-01

    By determining protein-protein interactions in normal, diseased and infected cells, we can improve our understanding of cellular systems and their reaction to various perturbations. In this protocol, we discuss how to use data obtained in affinity purification-mass spectrometry (AP-MS) experiments to generate meaningful interaction networks and effective figures. We begin with an overview of common epitope tagging, expression and AP practices, followed by liquid chromatography-MS (LC-MS) data collection. We then provide a detailed procedure covering a pipeline approach to (i) pre-processing the data by filtering against contaminant lists such as the Contaminant Repository for Affinity Purification (CRAPome) and normalization using the spectral index (SIN) or normalized spectral abundance factor (NSAF); (ii) scoring via methods such as MiST, SAInt and CompPASS; and (iii) testing the resulting scores. Data formats familiar to MS practitioners are then transformed to those most useful for network-based analyses. The protocol also explores methods available in Cytoscape to visualize and analyze these types of interaction data. The scoring pipeline can take anywhere from 1 d to 1 week, depending on one's familiarity with the tools and data peculiarities. Similarly, the network analysis and visualization protocol in Cytoscape takes 2-4 h to complete with the provided sample data, but we recommend taking days or even weeks to explore one's data and find the right questions.

  18. Targeting a dynamic protein-protein interaction: fragment screening against the malaria myosin A motor complex.

    PubMed

    Douse, Christopher H; Vrielink, Nina; Wenlin, Zhang; Cota, Ernesto; Tate, Edward W

    2015-01-01

    Motility is a vital feature of the complex life cycle of Plasmodium falciparum, the apicomplexan parasite that causes human malaria. Processes such as host cell invasion are thought to be powered by a conserved actomyosin motor (containing myosin A or myoA), correct localization of which is dependent on a tight interaction with myosin A tail domain interacting protein (MTIP) at the inner membrane of the parasite. Although disruption of this protein-protein interaction represents an attractive means to investigate the putative roles of myoA-based motility and to inhibit the parasitic life cycle, no small molecules have been identified that bind to MTIP. Furthermore, it has not been possible to obtain a crystal structure of the free protein, which is highly dynamic and unstable in the absence of its natural myoA tail partner. Herein we report the de novo identification of the first molecules that bind to and stabilize MTIP via a fragment-based, integrated biophysical approach and structural investigations to examine the binding modes of hit compounds. The challenges of targeting such a dynamic system with traditional fragment screening workflows are addressed throughout.

  19. Predicted networks of protein-protein interactions in Stegodyphus mimosarum by cross-species comparisons.

    PubMed

    Wang, Xiu; Jin, Yongfeng

    2017-09-11

    Stegodyphus mimosarum is a candidate model organism belonging to the class Arachnida in the phylum Arthropoda. Studies on the biology of S. mimosarum over the past several decades have consisted of behavioral research and comparison of gene sequences based on the assembled genome sequence. Given the lack of systematic protein analyses and the rich source of information in the genome, we predicted the relationships of proteins in S. mimosarum by bioinformatics comparison with genome-wide proteins from select model organisms using gene mapping. The protein-protein interactions (PPIs) of 11 organisms were integrated from four databases (BioGrid, InAct, MINT, and DIP). Here, we present comprehensive prediction and analysis of 3810 proteins in S. mimosarum with regard to interactions between proteins using PPI data of organisms. Interestingly, a portion of the protein interactions conserved among Saccharomyces cerevisiae, Homo sapiens, Arabidopsis thaliana, and Drosophila melanogaster were found to be associated with RNA splicing. In addition, overlap of predicted PPIs in reference organisms, Gene Ontology, and topology models in S. mimosarum are also reported. Addition of Stegodyphus, a spider representative of interactomic research, provides the possibility of obtaining deeper insights into the evolution of PPI networks among different animal species. This work largely supports the utility of the "stratus clouds" model for predicted PPIs, providing a roadmap for integrative systems biology in S. mimosarum.

  20. Regulation of dopamine transporter function by protein-protein interactions: new discoveries and methodological challenges.

    PubMed

    Eriksen, Jacob; Jørgensen, Trine Nygaard; Gether, Ulrik

    2010-04-01

    The dopamine transporter (DAT) plays a key role in regulating dopaminergic signalling in the brain by mediating rapid clearance of dopamine from the synaptic clefts. The psychostimulatory actions of cocaine and amphetamine are primarily the result of a direct interaction of these compounds with DAT leading to attenuated dopamine clearance and for amphetamine even increased dopamine release. In the last decade, intensive efforts have been directed towards understanding the molecular and cellular mechanisms governing the activity and availability of DAT in the plasma membrane of the pre-synaptic neurons. This has led to the identification of a plethora of different kinases, receptors and scaffolding proteins that interact with DAT and hereby either modulate the catalytic activity of the transporter or regulate its trafficking and degradation. Several new tools for studying DAT regulation in live cells have also recently become available such as fluorescently tagged cocaine analogues and fluorescent substrates. Here we review the current knowledge about the role of protein-protein interactions in DAT regulation as well as we describe the most recent methodological developments that have been established to overcome the challenges associated with the study of DAT in endogenous systems.

  1. Mining topological structures of protein-protein interaction networks for human brain-specific genes.

    PubMed

    Cui, W J; Gong, X J; Yu, H; Zhang, X C

    2015-10-16

    Compared to other placental mammals, humans have unique thinking and cognitive abilities because of their developed cerebral cortex composed of billions of neurons and synaptic connections. As the primary effectors of the mechanisms of life, proteins and their interactions form the basis of cellular and molecular functions in the living body. In this paper, we developed a pipeline for mining topological structures, identifying functional modules, and analyzing their functions from publically available datasets. A human brain-specific protein-protein interaction network with 1482 nodes and 3105 edges was built using a MapReduce based shortest path algorithm. Within this, 7 functional cliques were identified using a network clustering method, 98 hub proteins were obtained by the calculation of betweenness and connectivity, and 5 closest relationship to clique connector proteins were recognized by the combination scores of topological distance and gene ontology similarity. Furthermore, we discovered functional modules interacting with TP53 protein, which involves several fragmented research study conclusions and might be an important clue for further in vivo or in silico experiments to confirm these associations.

  2. Interplay between binding affinity and kinetics in protein-protein interactions.

    PubMed

    Cao, Huaiqing; Huang, Yongqi; Liu, Zhirong

    2016-07-01

    To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  3. Protein Cross-Linking Capillary Electrophoresis for Protein-Protein Interaction Analysis.

    PubMed

    Ouimet, Claire M; Shao, Hao; Rauch, Jennifer N; Dawod, Mohamed; Nordhues, Bryce; Dickey, Chad A; Gestwicki, Jason E; Kennedy, Robert T

    2016-08-16

    Capillary electrophoresis (CE) has been identified as a useful platform for detecting, quantifying, and screening for modulators of protein-protein interactions (PPIs). In this method, one protein binding partner is labeled with a fluorophore, the protein binding partners are mixed, and then, the complex is separated from free protein to allow direct determination of bound to free ratios. Although it possesses many advantages for PPI studies, the method is limited by the need to have separation conditions that both prevent protein adsorption to capillary and maintain protein interactions during the separation. In this work, we use protein cross-linking capillary electrophoresis (PXCE) to overcome this limitation. In PXCE, the proteins are cross-linked under binding conditions and then separated. This approach eliminates the need to maintain noncovalent interactions during electrophoresis and facilitates method development. We report PXCE methods for an antibody-antigen interaction and heterodimer and homodimer heat shock protein complexes. Complexes are cross-linked by short treatments with formaldehyde after reaching binding equilibrium. Cross-linked complexes are separated by electrophoretic mobility using free solution CE or by size using sieving electrophoresis of SDS complexes. The method gives good quantitative results; e.g., a lysozyme-antibody interaction was found to have Kd = 24 ± 3 nM by PXCE and Kd = 17 ± 2 nM using isothermal calorimetry (ITC). Heat shock protein 70 (Hsp70) in complex with bcl2 associated athanogene 3 (Bag3) was found to have Kd = 25 ± 5 nM by PXCE which agrees with Kd values reported without cross-linking. Hsp70-Bag3 binding site mutants and small molecule inhibitors of Hsp70-Bag3 were characterized by PXCE with good agreement to inhibitory constants and IC50 values obtained by a bead-based flow cytometry protein interaction assay (FCPIA). PXCE allows rapid method development for quantitative analysis of PPIs.

  4. Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure.

    PubMed

    Ansari-Pour, Naser; Razaghi-Moghadam, Zahra; Barneh, Farnaz; Jafari, Mohieddin

    2016-03-04

    Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.

  5. Specific anion effects on the pressure dependence of the protein-protein interaction potential.

    PubMed

    Möller, Johannes; Grobelny, Sebastian; Schulze, Julian; Steffen, Andre; Bieder, Steffen; Paulus, Michael; Tolan, Metin; Winter, Roland

    2014-04-28

    We present a study on ion specific effects on the intermolecular interaction potential V(r) of dense protein solutions under high hydrostatic pressure conditions. Small-angle X-ray scattering in combination with a liquid-state theoretical approach was used to determine the effect of structure breaking/making salt anions (Cl(-), SO4(2-), PO4(3-)) on the intermolecular interaction of lysozyme molecules. It was found that besides the Debye-Hückel charge screening effect, reducing the repulsiveness of the interaction potential V(r) at low salt concentrations, a specific ion effect is observed at high salt concentrations for the multivalent kosmotropic anions, which modulates also the pressure dependence of the protein-protein interaction potential. Whereas sulfate and phosphate strongly influence the pressure dependence of V(r), chloride anions do not. The strong structure-making effect of the multivalent anions, dominating for the triply charged PO4(3-), renders the solution structure less bulk-water-like at high salt concentrations, which leads to an altered behavior of the pressure dependence of V(r). Hence, the particular structural properties of the salt solutions are able to influence the spatial organization and the intermolecular interactions of the proteins, in particular upon compression. These results are of interest for exploring the combined effects of ionic strength, temperature and pressure on the phase behavior of protein solutions, but may also be of relevance for understanding pressure effects on the hydration behavior of biological matter under extreme environmental conditions.

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

    PubMed Central

    2014-01-01

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

  7. Introduction of inflammatory bowel disease biomarkers panel using protein-protein interaction (PPI) network analysis.

    PubMed

    Asadzadeh-Aghdaee, Hamid; Shahrokh, Shabnam; Norouzinia, Mohsen; Hosseini, Mostafa; Keramatinia, Aliasghar; Jamalan, Mostafa; Naghibzadeh, Bijan; Sadeghi, Ali; Jahani Sherafat, Somayeh; Zali, Mohammad Reza

    2016-12-01

    In the present study, a protein-protein interaction network construction is conducted for IBD. Inflammatory bowel diseases as serious chronic gastrointestinal disorders attracted many molecular investigations. Diverse molecular information is present for IBD. However, these molecular findings are not highlighted based on interactome analysis. On the other hand, PPI network analysis is a powerful method for study of molecular interactions in the protein level that provide useful information for highlighting the desired key proteins. Cytoscape is the used software with its plug-ins for detailed analysis. Two centrality parameters including degree and betweenness are determined and the crucial proteins based on these parameters are introduced. The 75 proteins among 100 initial proteins are included in the network of IBD. Seventy-five nodes and 260 edges constructed the network as a scale free network. The findings indicate that there are seven hub-bottleneck proteins in the IBD network. More examination revealed the essential roles of these key proteins in the integrity of the network. Finally, the indicator panel including NFKB1, CD40, TNFA, TYK2, NOD2, IL23R, and STAT3 is presented as a possible molecular index for IBD.

  8. Construction and analysis of the protein-protein interaction network related to essential hypertension

    PubMed Central

    2013-01-01

    Background Essential hypertension (EH) is a complex disease as a consequence of interaction between environmental factors and genetic background, but the pathogenesis of EH remains elusive. The emerging tools of network medicine offer a platform to explore a complex disease at system level. In this study, we aimed to identify the key proteins and the biological regulatory pathways involving in EH and further to explore the molecular connectivities between these pathways by the topological analysis of the Protein-protein interaction (PPI) network. Result The extended network including one giant network consisted of 535 nodes connected via 2572 edges and two separated small networks. 27 proteins with high BC and 28 proteins with large degree have been identified. NOS3 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 blood pressure (BP) and the crosstalk between them. Finally, the robustness of NOS3 as central protein and accuracy of backbone were validated by 287 test networks. Conclusion Our finding suggests that blood pressure variation is orchestrated by an integrated PPI network centered on NOS3. PMID:23587307

  9. A coclustering approach for mining large protein-protein interaction networks.

    PubMed

    Pizzuti, Clara; Rombo, Simona E

    2012-01-01

    Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.

  10. A Comparison of Two-Hybrid Approaches for Detecting Protein-Protein Interactions.

    PubMed

    Mehla, J; Caufield, J H; Sakhawalkar, N; Uetz, P

    2017-01-01

    Two-hybrid systems are one of the most popular, preferred, cost effective, and scalable in vivo genetic approaches for screening protein-protein interactions. A number of variants of yeast and bacterial two-hybrid systems exist, rendering them ideal for modern, flexible proteomics-driven studies. For mapping protein interactions at genome scales (that is, constructing an interactome), the yeast two-hybrid system has been extensively tested and is preferred over bacterial two-hybrid systems, given that users have created more resources such as a variety of vectors and other modifications. Each system has its own advantages and limitations and thus needs to be compared directly. For instance, the bacterial two-hybrid method seems a better fit than the yeast two-hybrid system to screen membrane-associated proteins. In this chapter, we provide detailed protocols for yeast and bacterial two-hybrid systems as well as a comparison of outcomes for each approach using our own and published data. © 2017 Elsevier Inc. All rights reserved.

  11. Structural Insights into Protein-Protein Interactions Involved in Bacterial Cell Wall Biogenesis.

    PubMed

    Laddomada, Federica; Miyachiro, Mayara M; Dessen, Andréa

    2016-04-28

    The bacterial cell wall is essential for survival, and proteins that participate in its biosynthesis have been the targets of antibiotic development efforts for decades. The biosynthesis of its main component, the peptidoglycan, involves the coordinated action of proteins that are involved in multi-member complexes which are essential for cell division (the "divisome") and/or cell wall elongation (the "elongasome"), in the case of rod-shaped cells. Our knowledge regarding these interactions has greatly benefitted from the visualization of different aspects of the bacterial cell wall and its cytoskeleton by cryoelectron microscopy and tomography, as well as genetic and biochemical screens that have complemented information from high resolution crystal structures of protein complexes involved in divisome or elongasome formation. This review summarizes structural and functional aspects of protein complexes involved in the cytoplasmic and membrane-related steps of peptidoglycan biosynthesis, with a particular focus on protein-protein interactions whereby disruption could lead to the development of novel antibacterial strategies.

  12. Structural Insights into Protein-Protein Interactions Involved in Bacterial Cell Wall Biogenesis

    PubMed Central

    Laddomada, Federica; Miyachiro, Mayara M.; Dessen, Andréa

    2016-01-01

    The bacterial cell wall is essential for survival, and proteins that participate in its biosynthesis have been the targets of antibiotic development efforts for decades. The biosynthesis of its main component, the peptidoglycan, involves the coordinated action of proteins that are involved in multi-member complexes which are essential for cell division (the “divisome”) and/or cell wall elongation (the “elongasome”), in the case of rod-shaped cells. Our knowledge regarding these interactions has greatly benefitted from the visualization of different aspects of the bacterial cell wall and its cytoskeleton by cryoelectron microscopy and tomography, as well as genetic and biochemical screens that have complemented information from high resolution crystal structures of protein complexes involved in divisome or elongasome formation. This review summarizes structural and functional aspects of protein complexes involved in the cytoplasmic and membrane-related steps of peptidoglycan biosynthesis, with a particular focus on protein-protein interactions whereby disruption could lead to the development of novel antibacterial strategies. PMID:27136593

  13. Protein-protein interactions among components of the Drosophila primary sex determination signal.

    PubMed

    Liu, Y; Belote, J M

    1995-07-28

    Sex determination in Drosophila melanogaster is initiated in the early embryo by a signal provided by three types of genes: (1) X-linked numerator elements [e.g., sisterless-a (sis-a) and sisterless-b (sis-b)], (2) autosomally linked denominator elements [e.g., deadpan (dpn)], and (3) maternal factors [e.g., daughterless (da)]. This signal acts to stimulate transcription from an embryo-specific promoter of the master regulatory gene Sex-lethal (Sxl) in embryos that have two X chromosomes (females), while it fails to activate Sxl in those with only one X (males). It has been previously proposed that competitive dimerizations among the components of this signal might provide the molecular basis for this sex specificity. Here, we use the yeast two-hybrid system to demonstrate specific protein-protein interactions among the above-mentioned factors, and to delimit their interacting domains. These results support and extend the model of the molecular basis of the X/A ratio signal.

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

    PubMed Central

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

    2016-01-01

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

  15. PIE: an online prediction system for protein-protein interactions from text.

    PubMed

    Kim, Sun; Shin, Soo-Yong; Lee, In-Hee; Kim, Soo-Jin; Sriram, Ram; Zhang, Byoung-Tak

    2008-07-01

    Protein-protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/.

  16. Stabilization of collagen through bioconversion: An insight in protein-protein interaction.

    PubMed

    Usharani, Nagarajan; Jayakumar, Gladstone Christopher; Kanth, Swarna Vinodh; Rao, Jonnalagadda Raghava

    2014-08-01

    Collagen is a natural protein, which is used as a vital biomaterial in tissue engineering. The major concern about native collagen is lack of its thermal stability and weak resistance to proteolytic degradation. In this scenario, the crosslinking compounds used for stabilization of collagen are mostly of chemical nature and exhibit toxicity. The enzyme mediated crosslinking of collagen provides a novel alternative, nontoxic method for stabilization. In this study, aldehyde forming enzyme (AFE) is used in the bioconversion of hydroxylmethyl groups of collagen to formyl groups that results in the formation of peptidyl aldehyde. The resulted peptidyl aldehyde interacts with bipolar ions of basic amino acid residues of collagen. Further interaction leads to the formation of conjugated double bonds (aldol condensation involving the aldehyde group of peptidyl aldehyde) within the collagen. The enzyme modified collagen matrices have shown an increase in the denaturation temperature, when compared with native collagen. Enzyme modified collagen membranes exhibit resistance toward collagenolytic activity. Moreover, they exhibited a nontoxic nature. The catalytic activity of AFE on collagen as a substrate establishes an efficient modification, which enhances the structural stability of collagen. This finds new avenues in the context of protein-protein stabilization and discovers paramount application in tissue engineering.

  17. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    PubMed

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  18. Targeting Plant Ethylene Responses by Controlling Essential Protein-Protein Interactions in the Ethylene Pathway.

    PubMed

    Bisson, Melanie M A; Groth, Georg

    2015-08-01

    The gaseous plant hormone ethylene regulates many processes of high agronomic relevance throughout the life span of plants. A central element in ethylene signaling is the endoplasmic reticulum (ER)-localized membrane protein ethylene insensitive2 (EIN2). Recent studies indicate that in response to ethylene, the extra-membranous C-terminal end of EIN2 is proteolytically processed and translocated from the ER to the nucleus. Here, we report that the conserved nuclear localization signal (NLS) mediating nuclear import of the EIN2 C-terminus provides an important domain for complex formation with ethylene receptor ethylene response1 (ETR1). EIN2 lacking the NLS domain shows strongly reduced affinity for the receptor. Interaction of EIN2 and ETR1 is also blocked by a synthetic peptide of the NLS motif. The corresponding peptide substantially reduces ethylene responses in planta. Our results uncover a novel mechanism and type of inhibitor interfering with ethylene signal transduction and ethylene responses in plants. Disruption of essential protein-protein interactions in the ethylene signaling pathway as shown in our study for the EIN2-ETR1 complex has the potential to guide the development of innovative ethylene antagonists for modern agriculture and horticulture.

  19. Introduction of inflammatory bowel disease biomarkers panel using protein-protein interaction (PPI) network analysis

    PubMed Central

    Asadzadeh-Aghdaee, Hamid; Shahrokh, Shabnam; Norouzinia, Mohsen; Hosseini, Mostafa; Keramatinia, Aliasghar; Jamalan, Mostafa; Naghibzadeh, Bijan; Sadeghi, Ali; Jahani Sherafat, Somayeh; Zali, Mohammad Reza

    2016-01-01

    Aim: In the present study, a protein-protein interaction network construction is conducted for IBD. Background: Inflammatory bowel diseases as serious chronic gastrointestinal disorders attracted many molecular investigations. Diverse molecular information is present for IBD. However, these molecular findings are not highlighted based on interactome analysis. On the other hand, PPI network analysis is a powerful method for study of molecular interactions in the protein level that provide useful information for highlighting the desired key proteins. Methods: Cytoscape is the used software with its plug-ins for detailed analysis. Two centrality parameters including degree and betweenness are determined and the crucial proteins based on these parameters are introduced. Results: The 75 proteins among 100 initial proteins are included in the network of IBD. Seventy-five nodes and 260 edges constructed the network as a scale free network. The findings indicate that there are seven hub-bottleneck proteins in the IBD network. Conclusion: More examination revealed the essential roles of these key proteins in the integrity of the network. Finally, the indicator panel including NFKB1, CD40, TNFA, TYK2, NOD2, IL23R, and STAT3 is presented as a possible molecular index for IBD. PMID:28224022

  20. Analysis of protein-protein interaction network in chronic obstructive pulmonary disease.

    PubMed

    Yuan, Y P; Shi, Y H; Gu, W C

    2014-10-31

    Chronic obstructive pulmonary disease (COPD) is a growing cause of morbidity and mortality throughout the world. The purpose of our study was to uncover biomarkers and explore its pathogenic mechanisms at the molecular level. The gene expression profiles of COPD samples and normal controls were downloaded from Gene Expression Omnibus. Matlab was used for data preprocessing and SAM4.0 was applied to determine the differentially expressed genes (DEGs). Furthermore, a protein-protein interaction (PPI) network was constructed by mapping the DEGs into PPI data, and functional analysis of the network was conducted with BiNGO. A total of 348 DEGs and 765 interactive genes were identified. The hub genes were mainly involved in metabolic processes and ribosome biogenesis. Several genes related to COPD in the PPI network were found, including CAMK1D, ALB, KIT, and DDX3Y. In conclusion, CAMK1D, ALB, KIT, and DDX3Y were chosen as candidate genes, which have the potential to be biomarkers or candidate target molecules to apply in clinical diagnosis and treatment of COPD.

  1. Protein-Protein Interaction for the De Novo Design of Cyclin-Dependent Kinase Peptide Inhibitors.

    PubMed

    Arumugasamy, Karthiga; Tripathi, Sunil Kumar; Singh, Poonam; Singh, Sanjeev Kumar

    2016-01-01

    The homology of the inhibitor binding site regions on the surface of cyclin-dependent kinases (CDKs) makes actual CDK inhibitors unable to bind specifically to their molecular targets. Most of them are ATP competitive inhibitors with low specificity that also affect the phosphorylation mechanisms of other nontarget kinases giving rise to harmful side effects. So, the search of specific and potent inhibitors able to bind to the desired CDK target is still a pending issue. Structure based drug design minimized the erroneous binding and increased the affinity of the inhibitor interaction. In the case of CDKs their activation and regulation mechanisms mainly depend on protein-protein interactions (PPIs). The design of drugs targeting these PPIs makes feasible and promising towards the discovery of new and specific CDK inhibitors. Development of peptide inhibitors for a target protein is an emerging approach in computer aided drug designing. This chapter describes in detail methodology for use of the VitAL-Viterbi algorithm for de novo peptide design of CDK2 inhibitors.

  2. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

    PubMed

    Teng, W J; Zhou, C; Liu, L J; Cao, X J; Zhuang, J; Liu, G X; Sun, C G

    2016-05-23

    Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

  3. Small molecule inhibitors of PSD95-nNOS protein-protein interactions as novel analgesics

    PubMed Central

    Lee, Wan-Hung; Xu, Zhili; Ashpole, Nicole M.; Hudmon, Andy; Kulkarni, Pushkar M.; Thakur, Ganesh A.; Lai, Yvonne Y.; Hohmann, Andrea G.

    2015-01-01

    Aberrant increases in NMDA receptor (NMDAR) signaling contributes to central nervous system sensitization and chronic pain by activating neuronal nitric oxide synthase (nNOS) and generating nitric oxide (NO). Because the scaffolding protein postsynaptic density 95kDA (PSD95) tethers nNOS to NMDARs, the PSD95-nNOS complex represents a therapeutic target. Small molecule inhibitors IC87201 (EC5O: 23.94 µM) and ZL006 (EC50: 12.88 µM) directly inhibited binding of purified PSD95 and nNOS proteins in AlphaScreen without altering binding of PSD95 to ErbB4. Both PSD95-nNOS inhibitors suppressed glutamate-induced cell death with efficacy comparable to MK-801. IC87201 and ZL006 preferentially suppressed phase 2A pain behavior in the formalin test and suppressed allodynia induced by intraplantar complete Freund’s adjuvant administration. IC87201 and ZL006 suppressed mechanical and cold allodynia induced by the chemotherapeutic agent paclitaxel (ED50s: 2.47 and 0.93 mg/kg i.p. for IC87201 and ZL006, respectively). Efficacy of PSD95-nNOS disruptors was similar to MK-801. Motor ataxic effects were induced by MK-801 but not by ZL006 or IC87201. Finally, MK-801 produced hyperalgesia in the tail-flick test whereas IC87201 and ZL006 did not alter basal nociceptive thresholds. Our studies establish the utility of using AlphaScreen and purified protein pairs to establish and quantify disruption of protein-protein interactions. Our results demonstrate previously unrecognized antinociceptive efficacy of ZL006 and establish, using two small molecules, a broad application for PSD95-nNOS inhibitors in treating neuropathic and inflammatory pain. Collectively, our results demonstrate that disrupting PSD95-nNOS protein-protein interactions is effective in attenuating pathological pain without producing unwanted side effects (i.e. motor ataxia) associated with NMDAR antagonists. PMID:26071110

  4. PPI-IRO: a two-stage method for protein-protein interaction extraction based on interaction relation ontology.

    PubMed

    Li, Chuan-Xi; Chen, Peng; Wang, Ru-Jing; Wang, Xiu-Jie; Su, Ya-Ru; Li, Jinyan

    2014-01-01

    Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identification of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifies and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At first, IRO is applied in a binary classifier to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the significant performance of IRO on relation sentences classification and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and BioInfer, respectively, which are superior to most existing extraction methods.

  5. Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments.

    PubMed

    Meng, Fanchi; Na, Insung; Kurgan, Lukasz; Uversky, Vladimir N

    2015-12-25

    The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA) and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore) are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions.

  6. Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments

    PubMed Central

    Meng, Fanchi; Na, Insung; Kurgan, Lukasz; Uversky, Vladimir N.

    2015-01-01

    The cell nucleus contains a number of membrane-less organelles or intra-nuclear compartments. These compartments are dynamic structures representing liquid-droplet phases which are only slightly denser than the bulk intra-nuclear fluid. They possess different functions, have diverse morphologies, and are typically composed of RNA (or, in some cases, DNA) and proteins. We analyzed 3005 mouse proteins localized in specific intra-nuclear organelles, such as nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinuclear compartment and compared them with ~29,863 non-nuclear proteins from mouse proteome. Our analysis revealed that intrinsic disorder is enriched in the majority of intra-nuclear compartments, except for the nuclear pore and lamina. These compartments are depleted in proteins that lack disordered domains and enriched in proteins that have multiple disordered domains. Moonlighting proteins found in multiple intra-nuclear compartments are more likely to have multiple disordered domains. Protein-protein interaction networks in the intra-nuclear compartments are denser and include more hubs compared to the non-nuclear proteins. Hubs in the intra-nuclear compartments (except for the nuclear pore) are enriched in disorder compared with non-nuclear hubs and non-nuclear proteins. Therefore, our work provides support to the idea of the functional importance of intrinsic disorder in the cell nucleus and shows that many proteins associated with sub-nuclear organelles in nuclei of mouse cells are enriched in disorder. This high level of disorder in the mouse nuclear proteins defines their ability to serve as very promiscuous binders, possessing both large quantities of potential disorder-based interaction sites and the ability of a single such site to be involved in a large number of interactions. PMID:26712748

  7. Prediction of protein-protein interaction sites using an ensemble method

    PubMed Central

    2009-01-01

    Background Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved. Results In this paper, an ensemble method is proposed, which combines bootstrap resampling technique, SVM-based fusion classifiers and weighted voting strategy, to overcome the imbalanced problem and effectively utilize a wide variety of features. We evaluate the ensemble classifier using a dataset extracted from 99 polypeptide chains with 10-fold cross validation, and get a AUC score of 0.86, with a sensitivity of 0.76 and a specificity of 0.78, which are better than that of the existing methods. To improve the usefulness of the proposed method, two special ensemble classifiers are designed to handle the cases of missing homologues and structural information respectively, and the performance is still encouraging. The robustness of the ensemble method is also evaluated by effectively classifying interaction sites from surface residues as well as from all residues in proteins. Moreover, we demonstrate the applicability of the proposed method to identify interaction sites from the non-structural proteins (NS) of the influenza A virus, which may be utilized as potential drug target sites. Conclusion Our experimental results show that the ensemble classifiers are quite effective in predicting protein interaction sites. The Sub-EnClassifiers with resampling technique can alleviate the imbalanced problem and the combination of Sub-EnClassifiers with a wide variety of feature groups can significantly improve prediction performance. PMID:20015386

  8. Lighting the Way to Protein-Protein Interactions: Recommendations on Best Practices for Bimolecular Fluorescence Complementation Analyses[OPEN

    PubMed Central

    Kudla, Jörg

    2016-01-01

    Techniques to detect and verify interactions between proteins in vivo have become invaluable tools in functional genomic research. While many of the initially developed interaction assays (e.g., yeast two-hybrid system and split-ubiquitin assay) usually are conducted in heterologous systems, assays relying on bimolecular fluorescence complementation (BiFC; also referred to as split-YFP assays) are applicable to the analysis of protein-protein interactions in most native systems, including plant cells. Like all protein-protein interaction assays, BiFC can produce false positive and false negative results. The purpose of this commentary is to (1) highlight shortcomings of and potential pitfalls in BiFC assays, (2) provide guidelines for avoiding artifactual interactions, and (3) suggest suitable approaches to scrutinize potential interactions and validate them by independent methods. PMID:27099259

  9. Protein-Protein Interactions Suggest Novel Activities of Human Cytomegalovirus Tegument Protein pUL103

    PubMed Central

    Ortiz, Daniel A.; Glassbrook, James E.

    2016-01-01

    cellular protein-protein interactions to identify and understand the range of pUL103 functions. We found that pUL103 interacts with cellular antiviral defense systems and proteins involved in organelle biogenesis and transport of cytoplasmic vesicles and is present in infected cell nuclei. These results expand our understanding of the functional repertoire of pUL103 to include activities that extend from the earliest stages of infection through virion assembly and egress. PMID:27334581

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

    PubMed Central

    2012-01-01

    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/). PMID:22230699

  11. Emory University: High-Throughput Protein-Protein Interaction Analysis for Hippo Pathway Profiling | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory University used high-throughput protein-protein interaction (PPI) mapping for Hippo signaling pathway profiling to rapidly unveil promising PPIs as potential therapeutic targets and advance functional understanding of signaling circuitry in cells. Read the abstract.

  12. Utilizing Mechanistic Cross-Linking Technology to Study Protein-Protein Interactions: An Experiment Designed for an Undergraduate Biochemistry Lab

    ERIC Educational Resources Information Center

    Finzel, Kara; Beld, Joris; Burkart, Michael D.; Charkoudian, Louise K.

    2017-01-01

    Over the past decade, mechanistic cross-linking probes have been used to study protein-protein interactions in natural product biosynthetic pathways. This approach is highly interdisciplinary, combining elements of protein biochemistry, organic chemistry, and computational docking. Herein, we described the development of an experiment to engage…

  13. Prediction of biological protein-protein interactions using atom-type and amino acid properties.

    PubMed

    Aziz, Md Mominul; Maleki, Mina; Rueda, Luis; Raza, Mohammad; Banerjee, Sridip

    2011-10-01

    Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks.

    PubMed

    Zhu, Lin; You, Zhu-Hong; Huang, De-Shuang; Wang, Bing

    2013-01-01

    Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.

  15. Clustered microRNAs' coordination in regulating protein-protein interaction network

    PubMed Central

    Yuan, Xiongying; Liu, Changning; Yang, Pengcheng; He, Shunmin; Liao, Qi; Kang, Shuli; Zhao, Yi

    2009-01-01

    Background MicroRNAs (miRNAs), a growing class of small RNAs with crucial regulatory roles at the post-transcriptional level, are usually found to be clustered on chromosomes. However, with the exception of a few individual cases, so far little is known about the functional consequence of this conserved clustering of miRNA loci. In animal genomes such clusters often contain non-homologous miRNA genes. One hypothesis to explain this heterogeneity suggests that clustered miRNAs are functionally related by virtue of co-targeting downstream pathways. Results Integrating of miRNA cluster information with protein protein interaction (PPI) network data, our research supports the hypothesis of the functional coordination of clustered miRNAs and links it to the topological features of miRNAs' targets in PPI network. Specifically, our results demonstrate that clustered miRNAs jointly regulate proteins in close proximity of the PPI network. The possibility that two proteins yield to this coordinated regulation is negatively correlated with their distance in PPI network. Guided by the knowledge of this preference, we found several network communities enriched with target genes of miRNA clusters. In addition, our results demonstrate that the variance of this propensity can also partly be explained by protein's connectivity and miRNA's conservation. Conclusion In summary, this work supports the hypothesis of intra-cluster coordination and investigates the extent of this coordination. PMID:19558649

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

    PubMed

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

    2013-01-01

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

  17. Therapeutic design of peptide modulators of protein-protein interactions in membranes.

    PubMed

    Stone, Tracy A; Deber, Charles M

    2017-04-01

    Membrane proteins play the central roles in a variety of cellular processes, ranging from nutrient uptake and signalling, to cell-cell communication. Their biological functions are directly related to how they fold and assemble; defects often lead to disease. Protein-protein interactions (PPIs) within the membrane are therefore of great interest as therapeutic targets. Here we review the progress in the application of membrane-insertable peptides for the disruption or stabilization of membrane-based PPIs. We describe the design and preparation of transmembrane peptide mimics; and of several categories of peptidomimetics used for study, including d-enantiomers, non-natural amino acids, peptoids, and β-peptides. Further aspects of the review describe modifications to membrane-insertable peptides, including lipidation and cyclization via hydrocarbon stapling. These approaches provide a pathway toward the development of metabolically stable, non-toxic, and efficacious peptide modulators of membrane-based PPIs. This article is part of a Special Issue entitled: Lipid order/lipid defects and lipid-control of protein activity edited by Dirk Schneider.

  18. Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors

    NASA Astrophysics Data System (ADS)

    Kuenemann, Mélaine A.; Labbé, Céline M.; Cerdan, Adrien H.; Sperandio, Olivier

    2016-04-01

    Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.

  19. Dissecting the Human Protein-Protein Interaction Network via Phylogenetic Decomposition

    PubMed Central

    Chen, Cho-Yi; Ho, Andy; Huang, Hsin-Yuan; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

    2014-01-01

    The protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, the intricacy of network complexity complicates comprehensive analysis. Here, we adopted a phylogenic grouping method combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Second, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and pathway engagements, which could correspond to their evolutionary roles in eukaryotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organization in a living cell. PMID:25412639

  20. The EED protein-protein interaction inhibitor A-395 inactivates the PRC2 complex.

    PubMed

    He, Yupeng; Selvaraju, Sujatha; Curtin, Michael L; Jakob, Clarissa G; Zhu, Haizhong; Comess, Kenneth M; Shaw, Bailin; The, Juliana; Lima-Fernandes, Evelyne; Szewczyk, Magdalena M; Cheng, Dong; Klinge, Kelly L; Li, Huan-Qiu; Pliushchev, Marina; Algire, Mikkel A; Maag, David; Guo, Jun; Dietrich, Justin; Panchal, Sanjay C; Petros, Andrew M; Sweis, Ramzi F; Torrent, Maricel; Bigelow, Lance J; Senisterra, Guillermo; Li, Fengling; Kennedy, Steven; Wu, Qin; Osterling, Donald J; Lindley, David J; Gao, Wenqing; Galasinski, Scott; Barsyte-Lovejoy, Dalia; Vedadi, Masoud; Buchanan, Fritz G; Arrowsmith, Cheryl H; Chiang, Gary G; Sun, Chaohong; Pappano, William N

    2017-04-01

    Polycomb repressive complex 2 (PRC2) is a regulator of epigenetic states required for development and homeostasis. PRC2 trimethylates histone H3 at lysine 27 (H3K27me3), which leads to gene silencing, and is dysregulated in many cancers. The embryonic ectoderm development (EED) protein is an essential subunit of PRC2 that has both a scaffolding function and an H3K27me3-binding function. Here we report the identification of A-395, a potent antagonist of the H3K27me3 binding functions of EED. Structural studies demonstrate that A-395 binds to EED in the H3K27me3-binding pocket, thereby preventing allosteric activation of the catalytic activity of PRC2. Phenotypic effects observed in vitro and in vivo are similar to those of known PRC2 enzymatic inhibitors; however, A-395 retains potent activity against cell lines resistant to the catalytic inhibitors. A-395 represents a first-in-class antagonist of PRC2 protein-protein interactions (PPI) for use as a chemical probe to investigate the roles of EED-containing protein complexes.

  1. Stabilization of Protein-Protein Interactions in chemical biology and drug discovery.

    PubMed

    Bier, David; Thiel, Philipp; Briels, Jeroen; Ottmann, Christian

    2015-10-01

    More than 300,000 Protein-Protein Interactions (PPIs) can be found in human cells. This number is significantly larger than the number of single proteins, which are the classical targets for pharmacological intervention. Hence, specific and potent modulation of PPIs by small, drug-like molecules would tremendously enlarge the "druggable genome" enabling novel ways of drug discovery for essentially every human disease. This strategy is especially promising in diseases with difficult targets like intrinsically disordered proteins or transcription factors, for example neurodegeneration or metabolic diseases. Whereas the potential of PPI modulation has been recognized in terms of the development of inhibitors that disrupt or prevent a binary protein complex, the opposite (or complementary) strategy to stabilize PPIs has not yet been realized in a systematic manner. This fact is rather surprising given the number of impressive natural product examples that confer their activity by stabilizing specific PPIs. In addition, in recent years more and more examples of synthetic molecules are being published that work as PPI stabilizers, despite the fact that in the majority they initially have not been designed as such. Here, we describe examples from both the natural products as well as the synthetic molecules advocating for a stronger consideration of the PPI stabilization approach in chemical biology and drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction.

    PubMed

    Wang, Yongcui; Ren, Xianwen; Zhang, Chunhua; Deng, Naiyang; Zhang, Xiangsun

    2012-12-21

    The past decades witnessed extensive efforts to study the relationship among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. High-throughput experimental methodologies make many model organism's PPIs known, which allows us to apply machine learning methods to learn understandable rules from the available PPIs. Under the machine learning framework, the composition vectors are usually applied to encode proteins as real-value vectors. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vectors. Thus formulation to estimate the noise induced by the background distribution of amino acids may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which were successfully used in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and human PPIs datasets, surprisingly, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that the noise in phylogenetic tree construction may be valuable information in PPIs prediction.

  3. Self protein-protein interactions are involved in TPPP/p25 mediated microtubule bundling

    PubMed Central

    DeBonis, Salvatore; Neumann, Emmanuelle; Skoufias, Dimitrios A.

    2015-01-01

    TPPP/p25 is a microtubule-associated protein, detected in protein inclusions associated with various neurodegenerative diseases. Deletion analysis data show that TPPP/p25 has two microtubule binding sites, both located in intrinsically disordered domains, one at the N-terminal and the other in the C-terminal domain. In copolymerization assays the full-length protein exhibits microtubule stimulation and bundling activity. In contrast, at the same ratio relative to tubulin, truncated forms of TPPP/p25 exhibit either lower or no microtubule stimulation and no bundling activity, suggesting a cooperative phenomenon which is enhanced by the presence of the two binding sites. The binding characteristics of the N- and C-terminally truncated proteins to taxol-stabilized microtubules are similar to the full-length protein. However, the C-terminally truncated TPPP/p25 shows a lower Bmax for microtubule binding, suggesting that it may bind to a site of tubulin that is masked in microtubules. Bimolecular fluorescent complementation assays in cells expressing combinations of various TPPP/p25 fragments, but not that of the central folded domain, resulted in the generation of a fluorescence signal colocalized with perinuclear microtubule bundles insensitive to microtubule inhibitors. The data suggest that the central folded domain of TPPP/p25 following binding to microtubules can drive s homotypic protein-protein interactions leading to bundled microtubules. PMID:26289831

  4. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    PubMed

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  5. Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history.

    PubMed

    Yu, Wei; He, Li-Ran; Zhao, Yan-Chao; Chan, Man-Him; Zhang, Meng; He, Miao

    2013-02-01

    Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases. However, how lung cancer develops in patients with smoking history remains unclear. Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods. We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs. By defining expression variance (EV), we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database, and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history. We also determined the primary functions of each subnetwork: signal transduction, apoptosis, and cell migration and adhesion for subnetwork A; cell-sustained angiogenesis for subnetwork B; apoptosis for subnetwork C; and, finally, signal transduction and cell replication and proliferation for subnetworks D-G. The probability distribution of the degree of dynamic protein and static protein differed, clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins. There were high correlations among the dynamic proteins, suggesting that the dynamic proteins tend to form specific dynamic modules. We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

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

    PubMed

    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.

  7. Features of Protein-Protein Interactions that Translate into Potent Inhibitors: Topology, Surface Area and Affinity

    PubMed Central

    Smith, Matthew C.; Gestwicki, Jason E.

    2013-01-01

    Protein-protein interactions (PPIs) control the assembly of multi-protein complexes and, thus, these contacts have enormous potential as drug targets. However, the field has produced a mix of both exciting success stories and frustrating challenges. Here, we review known examples and explore how the physical features of a PPI, such as its affinity, hotspots, off-rates, buried surface area and topology, may influence the chances of success in finding inhibitors. This analysis suggests that concise, tight binding PPIs are most amenable to inhibition. However, it is also clear that emerging technical methods are expanding the repertoire of “druggable” protein contacts and increasing the odds against difficult targets. In particular, natural product-like compound libraries, high throughput screens specifically designed for PPIs and approaches that favor discovery of allosteric inhibitors appear to be attractive routes. The first group of PPI inhibitors has entered clinical trials, further motivating the need to understand the challenges and opportunities in pursuing these types of targets. PMID:22831787

  8. Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

    PubMed

    Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

    2015-10-22

    Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and "interologs" in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria.

  9. Recent coselection in human populations revealed by protein-protein interaction network.

    PubMed

    Qian, Wei; Zhou, Hang; Tang, Kun

    2014-12-21

    Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein-protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  10. Small-Molecule Stabilization of 14-3-3 Protein-Protein Interactions Stimulates Axon Regeneration.

    PubMed

    Kaplan, Andrew; Morquette, Barbara; Kroner, Antje; Leong, SooYuen; Madwar, Carolin; Sanz, Ricardo; Banerjee, Sara L; Antel, Jack; Bisson, Nicolas; David, Samuel; Fournier, Alyson E

    2017-03-08

    Damaged central nervous system (CNS) neurons have a poor ability to spontaneously regenerate, causing persistent functional deficits after injury. Therapies that stimulate axon growth are needed to repair CNS damage. 14-3-3 adaptors are hub proteins that are attractive targets to manipulate cell signaling. We identify a positive role for 14-3-3s in axon growth and uncover a developmental regulation of the phosphorylation and function of 14-3-3s. We show that fusicoccin-A (FC-A), a small-molecule stabilizer of 14-3-3 protein-protein interactions, stimulates axon growth in vitro and regeneration in vivo. We show that FC-A stabilizes a complex between 14-3-3 and the stress response regulator GCN1, inducing GCN1 turnover and neurite outgrowth. These findings show that 14-3-3 adaptor protein complexes are druggable targets and identify a new class of small molecules that may be further optimized for the repair of CNS damage.

  11. Modulation of nociceptive ion channels and receptors via protein-protein interactions: implications for pain relief

    PubMed Central

    Rouwette, Tom; Avenali, Luca; Sondermann, Julia; Narayanan, Pratibha; Gomez-Varela, David; Schmidt, Manuela

    2015-01-01

    In the last 2 decades biomedical research has provided great insights into the molecular signatures underlying painful conditions. However, chronic pain still imposes substantial challenges to researchers, clinicians and patients alike. Under pathological conditions, pain therapeutics often lack efficacy and exhibit only minimal safety profiles, which can be largely attributed to the targeting of molecules with key physiological functions throughout the body. In light of these difficulties, the identification of molecules and associated protein complexes specifically involved in chronic pain states is of paramount importance for designing selective interventions. Ion channels and receptors represent primary targets, as they critically shape nociceptive signaling from the periphery to the brain. Moreover, their function requires tight control, which is usually implemented by protein-protein interactions (PPIs). Indeed, manipulation of such PPIs entails the modulation of ion channel activity with widespread implications for influencing nociceptive signaling in a more specific way. In this review, we highlight recent advances in modulating ion channels and receptors via their PPI networks in the pursuit of relieving chronic pain. Moreover, we critically discuss the potential of targeting PPIs for developing novel pain therapies exhibiting higher efficacy and improved safety profiles. PMID:26039491

  12. CCharPPI web server: computational characterization of protein-protein interactions from structure.

    PubMed

    Moal, Iain H; Jiménez-García, Brian; Fernández-Recio, Juan

    2015-01-01

    The atomic structures of protein-protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions. The server does not require registration by the user and is freely available for non-commercial academic use at http://life.bsc.es/pid/ccharppi. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Disease Gene Prioritization Based on Topological Similarity in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Erten, Sinan; Bebek, Gurkan; Koyutürk, Mehmet

    In recent years, many algorithms have been developed to narrow down the set of candidate disease genes implicated by genome wide association studies (GWAS), using knowledge on protein-protein interactions (PPIs). All of these algorithms are based on a common principle; functional association between proteins is correlated with their connectivity/proximity in the PPI network. However, recent research also reveals that networks are organized into recurrent network schemes that underlie the mechanisms of cooperation among proteins with different function, as well as the crosstalk between different cellular processes. In this paper, we hypothesize that proteins that are associated with similar diseases may exhibit patterns of "topological similarity" in PPI networks. Motivated by these observations, we introduce the notion of "topological profile", which represents the location of a protein in the network with respect to other proteins. Based on this notion, we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to develop algorithms that prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. Systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance (OMIM) database show that the proposed algorithm, Vavien, clearly outperforms state-of-the-art network based prioritization algorithms. Vavien is available as a web service at http://www.diseasegenes.org .

  14. Expanding the Number of “Druggable” Targets: Non-Enzymes and Protein-Protein Interactions

    PubMed Central

    Makley, Leah N.; Gestwicki, Jason E.

    2012-01-01

    Following sequencing and assembly of the human genome, the preferred methods for identification of new drug targets have changed dramatically. Modern tactics such as genome-wide association studies (GWAS) and deep sequencing are fundamentally different from the pharmacology-guided approaches used previously, in which knowledge of small molecule ligands acting at their cellular targets was the primary discovery engine. A consequence of the “target-first, pharmacology-second” strategy is that many predicted drug targets are non-enzymes, such as scaffolding, regulatory or structural proteins, and their activities are often dependent on protein-protein interactions (PPIs). These types of targets create unique challenges to drug discovery efforts because enzymatic turnover cannot be used as a convenient surrogate for compound potency. Moreover, it is often challenging to predict how ligand binding to non-enzymes might affect changes in protein function and/or pathobiology. Thus, in the post-genomic era, targets might be strongly implicated by molecular biology-based methods, yet they often later earn the designation of “undruggable.” Can the scope of available targets be widened to include these promising, but challenging, non-enzymes? In this review, we discuss advances in high throughput screening technology and chemical library design that are emerging to deal with these challenges. PMID:23253128

  15. Lipid demixing and protein-protein interactions in the adsorption of charged proteins on mixed membranes.

    PubMed Central

    May, S; Harries, D; Ben-Shaul, A

    2000-01-01

    The adsorption free energy of charged proteins on mixed membranes, containing varying amounts of (oppositely) charged lipids, is calculated based on a mean-field free energy expression that accounts explicitly for the ability of the lipids to demix locally, and for lateral interactions between the adsorbed proteins. Minimization of this free energy functional yields the familiar nonlinear Poisson-Boltzmann equation and the boundary condition at the membrane surface that allows for lipid charge rearrangement. These two self-consistent equations are solved simultaneously. The proteins are modeled as uniformly charged spheres and the (bare) membrane as an ideal two-dimensional binary mixture of charged and neutral lipids. Substantial variations in the lipid charge density profiles are found when highly charged proteins adsorb on weakly charged membranes; the lipids, at a certain demixing entropy penalty, adjust their concentration in the vicinity of the adsorbed protein to achieve optimal charge matching. Lateral repulsive interactions between the adsorbed proteins affect the lipid modulation profile and, at high densities, result in substantial lowering of the binding energy. Adsorption isotherms demonstrating the importance of lipid mobility and protein-protein interactions are calculated using an adsorption equation with a coverage-dependent binding constant. Typically, at bulk-surface equilibrium (i.e., when the membrane surface is "saturated" by adsorbed proteins), the membrane charges are "overcompensated" by the protein charges, because only about half of the protein charges (those on the hemispheres facing the membrane) are involved in charge neutralization. Finally, it is argued that the formation of lipid-protein domains may be enhanced by electrostatic adsorption of proteins, but its origin (e.g., elastic deformations associated with lipid demixing) is not purely electrostatic. PMID:11023883

  16. Identification of a protein-protein interaction network downstream of molybdenum cofactor biosynthesis in Arabidopsis thaliana.

    PubMed

    Kaufholdt, David; Baillie, Christin-Kirsty; Meyer, Martin H; Schwich, Oliver D; Timmerer, Ulrike L; Tobias, Lydia; van Thiel, Daniela; Hänsch, Robert; Mendel, Ralf R

    2016-12-01

    The molybdenum cofactor (Moco) is ubiquitously present in all kingdoms of life and vitally important for survival. Among animals, loss of the Moco-containing enzyme (Mo-enzyme) sulphite oxidase is lethal, while for plants the loss of nitrate reductase prohibits nitrogen assimilation. Moco is highly oxygen-sensitive, which obviates a freely diffusible pool and necessitates protein-mediated distribution. During the highly conserved Moco biosynthesis pathway, intermediates are channelled through a multi-protein complex facilitating protected transport. However, the mechanism by which Moco is subsequently transferred to apo-enzymes is still unclear. Moco user enzymes can be divided into two families: the sulphite oxidase (SO) and the xanthine oxidoreductase (XOR) family. The latter requires a final sulphurisation of Moco catalysed via ABA3. To examine Moco transfer towards apo-Mo-enzymes, two different and independent protein-protein interaction assays were performed in vivo: bimolecular fluorescence complementation and split luciferase. The results revealed a direct contact between Moco producer molybdenum insertase CNX1, which represents the last biosynthesis step, and members of the SO family. However, no protein contact was observed between Moco producer CNX1 and apo-enzymes of the XOR family or between CNX1 and the Moco sulphurase ABA3. Instead, the Moco-binding protein MOBP2 was identified as a mediator between CNX1 and ABA3. This interaction was followed by contact between ABA3 and enzymes of the XOR family. These results allow to describe an interaction matrix of proteins beyond Moco biosynthesis and to demonstrate the complexity of transferring a prosthetic group after biosynthesis. Copyright © 2016 Elsevier GmbH. All rights reserved.

  17. Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions.

    PubMed

    Kuppuswamy, Usha; Ananthasubramanian, Seshan; Wang, Yanli; Balakrishnan, Narayanaswamy; Ganapathiraju, Madhavi K

    2014-04-03

    The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably. This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in

  18. Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses.

    PubMed

    Durmuş, Saliha; Ülgen, Kutlu Ö

    2017-01-01

    Viruses are obligatory intracellular pathogens and completely depend on their hosts for survival and reproduction. The strategies adopted by viruses to exploit host cell processes and to evade host immune systems during infections may differ largely with the type of the viral genetic material. An improved understanding of these viral infection mechanisms is only possible through a better understanding of the pathogen-host interactions (PHIs) that enable viruses to enter into the host cells and manipulate the cellular mechanisms to their own advantage. Experimentally-verified protein-protein interaction (PPI) data of pathogen-host systems only became available at large scale within the last decade. In this study, we comparatively analyzed the current PHI networks belonging to DNA and RNA viruses and their human host, to get insights into the infection strategies used by these viral groups. We investigated the functional properties of human proteins in the PHI networks, to observe and compare the attack strategies of DNA and RNA viruses. We observed that DNA viruses are able to attack both human cellular and metabolic processes simultaneously during infections. On the other hand, RNA viruses preferentially interact with human proteins functioning in specific cellular processes as well as in intracellular transport and localization within the cell. Observing virus-targeted human proteins, we propose heterogeneous nuclear ribonucleoproteins and transporter proteins as potential antiviral therapeutic targets. The observed common and specific infection mechanisms in terms of viral strategies to attack human proteins may provide crucial information for further design of broad and specific next-generation antiviral therapeutics.

  19. Revealing the potential pathogenesis of glioma by utilizing a glioma associated protein-protein interaction network.

    PubMed

    Pan, Weiran; Li, Gang; Yang, Xiaoxiao; Miao, Jinming

    2015-04-01

    This study aims to explore the potential mechanism of glioma through bioinformatic approaches. The gene expression profile (GSE4290) of glioma tumor and non-tumor samples was downloaded from Gene Expression Omnibus database. A total of 180 samples were available, including 23 non-tumor and 157 tumor samples. Then the raw data were preprocessed using robust multiarray analysis, and 8,890 differentially expressed genes (DEGs) were identified by using t-test (false discovery rate < 0.0005). Furthermore, 16 known glioma related genes were abstracted from Genetic Association Database. After mapping 8,890 DEGs and 16 known glioma related genes to Human Protein Reference Database, a glioma associated protein-protein interaction network (GAPN) was constructed. In addition, 51 sub-networks in GAPN were screened out through Molecular Complex Detection (score ≥ 1), and sub-network 1 was found to have the closest interaction (score = 3). What' more, for the top 10 sub-networks, Gene Ontology (GO) enrichment analysis (p value < 0.05) was performed, and DEGs involved in sub-network 1 and 2, such as BRMS1L and CCNA1, were predicted to regulate cell growth, cell cycle, and DNA replication via interacting with known glioma related genes. Finally, the overlaps of DEGs and human essential, housekeeping, tissue-specific genes were calculated (p value = 1.0, 1.0, and 0.00014, respectively) and visualized by Venn Diagram package in R. About 61% of human tissue-specific genes were DEGs as well. This research shed new light on the pathogenesis of glioma based on DEGs and GAPN, and our findings might provide potential targets for clinical glioma treatment.

  20. Struct2Net: a web service to predict protein-protein interactions using a structure-based approach.

    PubMed

    Singh, Rohit; Park, Daniel; Xu, Jinbo; Hosur, Raghavendra; Berger, Bonnie

    2010-07-01

    Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein-protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the