Sample records for aids protein-protein interaction

  1. Stapled Voltage-Gated Calcium Channel (CaV) α-Interaction Domain (AID) Peptides Act As Selective Protein-Protein Interaction Inhibitors of CaV Function.

    PubMed

    Findeisen, Felix; Campiglio, Marta; Jo, Hyunil; Abderemane-Ali, Fayal; Rumpf, Christine H; Pope, Lianne; Rossen, Nathan D; Flucher, Bernhard E; DeGrado, William F; Minor, Daniel L

    2017-06-21

    For many voltage-gated ion channels (VGICs), creation of a properly functioning ion channel requires the formation of specific protein-protein interactions between the transmembrane pore-forming subunits and cystoplasmic accessory subunits. Despite the importance of such protein-protein interactions in VGIC function and assembly, their potential as sites for VGIC modulator development has been largely overlooked. Here, we develop meta-xylyl (m-xylyl) stapled peptides that target a prototypic VGIC high affinity protein-protein interaction, the interaction between the voltage-gated calcium channel (Ca V ) pore-forming subunit α-interaction domain (AID) and cytoplasmic β-subunit (Ca V β). We show using circular dichroism spectroscopy, X-ray crystallography, and isothermal titration calorimetry that the m-xylyl staples enhance AID helix formation are structurally compatible with native-like AID:Ca V β interactions and reduce the entropic penalty associated with AID binding to Ca V β. Importantly, electrophysiological studies reveal that stapled AID peptides act as effective inhibitors of the Ca V α 1 :Ca V β interaction that modulate Ca V function in an Ca V β isoform-selective manner. Together, our studies provide a proof-of-concept demonstration of the use of protein-protein interaction inhibitors to control VGIC function and point to strategies for improved AID-based Ca V modulator design.

  2. Stapled Voltage-Gated Calcium Channel (CaV) α-Interaction Domain (AID) Peptides Act As Selective Protein–Protein Interaction Inhibitors of CaV Function

    PubMed Central

    2017-01-01

    For many voltage-gated ion channels (VGICs), creation of a properly functioning ion channel requires the formation of specific protein–protein interactions between the transmembrane pore-forming subunits and cystoplasmic accessory subunits. Despite the importance of such protein–protein interactions in VGIC function and assembly, their potential as sites for VGIC modulator development has been largely overlooked. Here, we develop meta-xylyl (m-xylyl) stapled peptides that target a prototypic VGIC high affinity protein–protein interaction, the interaction between the voltage-gated calcium channel (CaV) pore-forming subunit α-interaction domain (AID) and cytoplasmic β-subunit (CaVβ). We show using circular dichroism spectroscopy, X-ray crystallography, and isothermal titration calorimetry that the m-xylyl staples enhance AID helix formation are structurally compatible with native-like AID:CaVβ interactions and reduce the entropic penalty associated with AID binding to CaVβ. Importantly, electrophysiological studies reveal that stapled AID peptides act as effective inhibitors of the CaVα1:CaVβ interaction that modulate CaV function in an CaVβ isoform-selective manner. Together, our studies provide a proof-of-concept demonstration of the use of protein–protein interaction inhibitors to control VGIC function and point to strategies for improved AID-based CaV modulator design. PMID:28278376

  3. 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.

  4. Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

    DOE PAGES

    Venkatraman, S.; Doktycz, M. J.; Qi, H.; ...

    2006-01-01

    The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

  5. 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.

  6. 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.

  7. Specificity of molecular interactions in transient protein-protein interaction interfaces.

    PubMed

    Cho, Kyu-il; Lee, KiYoung; Lee, Kwang H; Kim, Dongsup; Lee, Doheon

    2006-11-15

    In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of

  8. Interaction entropy for protein-protein binding.

    PubMed

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

    2017-03-28

    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 interactionentropy 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 interactionentropy 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.

  9. Folding superfunnel to describe cooperative folding of interacting proteins.

    PubMed

    Smeller, László

    2016-07-01

    This paper proposes a generalization of the well-known folding funnel concept of proteins. In the funnel model the polypeptide chain is treated as an individual object not interacting with other proteins. Since biological systems are considerably crowded, protein-protein interaction is a fundamental feature during the life cycle of proteins. The folding superfunnel proposed here describes the folding process of interacting proteins in various situations. The first example discussed is the folding of the freshly synthesized protein with the aid of chaperones. Another important aspect of protein-protein interactions is the folding of the recently characterized intrinsically disordered proteins, where binding to target proteins plays a crucial role in the completion of the folding process. The third scenario where the folding superfunnel is used is the formation of aggregates from destabilized proteins, which is an important factor in case of several conformational diseases. The folding superfunnel constructed here with the minimal assumption about the interaction potential explains all three cases mentioned above. Proteins 2016; 84:1009-1016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  10. Non-interacting surface solvation and dynamics in protein-protein interactions.

    PubMed

    Visscher, Koen M; Kastritis, Panagiotis L; Bonvin, Alexandre M J J

    2015-03-01

    Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein-protein binding, even for simple lock-and-key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein-protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein-protein surfaces. We compare properties of the interface, rim, and non-interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non-interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non-interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein-protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein-protein complexes from unintended protein-protein interactions. © 2014 Wiley Periodicals, Inc.

  11. 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

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

    PubMed Central

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

    2011-01-01

    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. PMID:21666777

  13. 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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:25704442

  15. Construction of reliable protein-protein interaction networks with a new interaction generality measure.

    PubMed

    Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

    2003-04-12

    Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

  16. 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.

  17. Molecular simulations of lipid-mediated protein-protein interactions.

    PubMed

    de Meyer, Frédérick Jean-Marie; Venturoli, Maddalena; Smit, Berend

    2008-08-01

    Recent experimental results revealed that lipid-mediated interactions due to hydrophobic forces may be important in determining the protein topology after insertion in the membrane, in regulating the protein activity, in protein aggregation and in signal transduction. To gain insight into the lipid-mediated interactions between two intrinsic membrane proteins, we developed a mesoscopic model of a lipid bilayer with embedded proteins, which we studied with dissipative particle dynamics. Our calculations of the potential of mean force between transmembrane proteins show that hydrophobic forces drive long-range protein-protein interactions and that the nature of these interactions depends on the length of the protein hydrophobic segment, on the three-dimensional structure of the protein and on the properties of the lipid bilayer. To understand the nature of the computed potentials of mean force, the concept of hydrophilic shielding is introduced. The observed protein interactions are interpreted as resulting from the dynamic reorganization of the system to maintain an optimal hydrophilic shielding of the protein and lipid hydrophobic parts, within the constraint of the flexibility of the components. Our results could lead to a better understanding of several membrane processes in which protein interactions are involved.

  18. 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.

  19. Protein-protein interactions: an application of Tus-Ter mediated protein microarray system.

    PubMed

    Sitaraman, Kalavathy; Chatterjee, Deb K

    2011-01-01

    In this chapter, we present a novel, cost-effective microarray strategy that utilizes expression-ready plasmid DNAs to generate protein arrays on-demand and its use to validate protein-protein interactions. These expression plasmids were constructed in such a way so as to serve a dual purpose of synthesizing the protein of interest as well as capturing the synthesized protein. The microarray system is based on the high affinity binding of Escherichia coli "Tus" protein to "Ter," a 20 bp DNA sequence involved in the regulation of DNA replication. The protein expression is carried out in a cell-free protein synthesis system, with rabbit reticulocyte lysates, and the target proteins are detected either by labeled incorporated tag specific or by gene-specific antibodies. This microarray system has been successfully used for the detection of protein-protein interaction because both the target protein and the query protein can be transcribed and translated simultaneously in the microarray slides. The utility of this system for detecting protein-protein interaction is demonstrated by a few well-known examples: Jun/Fos, FRB/FKBP12, p53/MDM2, and CDK4/p16. In all these cases, the presence of protein complexes resulted in the localization of fluorophores at the specific sites of the immobilized target plasmids. Interestingly, during our interactions studies we also detected a previously unknown interaction between CDK2 and p16. Thus, this Tus-Ter based system of protein microarray can be used for the validation of known protein interactions as well as for identifying new protein-protein interactions. In addition, it can be used to examine and identify targets of nucleic acid-protein, ligand-receptor, enzyme-substrate, and drug-protein interactions.

  20. 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

  1. Predicting protein-protein interactions from protein domains using a set cover approach.

    PubMed

    Huang, Chengbang; Morcos, Faruck; Kanaan, Simon P; Wuchty, Stefan; Chen, Danny Z; Izaguirre, Jesús A

    2007-01-01

    One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, Maximum Specificity Set Cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the Maximum Likelihood Estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi.cse.nd.edu.

  2. Prediction of physical protein protein interactions

    NASA Astrophysics Data System (ADS)

    Szilágyi, András; Grimm, Vera; Arakaki, Adrián K.; Skolnick, Jeffrey

    2005-06-01

    Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

  3. 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

  4. Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

    PubMed

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

    Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

  5. NOXclass: prediction of protein-protein interaction types.

    PubMed

    Zhu, Hongbo; Domingues, Francisco S; Sommer, Ingolf; Lengauer, Thomas

    2006-01-19

    Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

  6. Quantifying protein-protein interactions in high throughput using protein domain microarrays.

    PubMed

    Kaushansky, Alexis; Allen, John E; Gordus, Andrew; Stiffler, Michael A; Karp, Ethan S; Chang, Bryan H; MacBeath, Gavin

    2010-04-01

    Protein microarrays provide an efficient way to identify and quantify protein-protein interactions in high throughput. One drawback of this technique is that proteins show a broad range of physicochemical properties and are often difficult to produce recombinantly. To circumvent these problems, we have focused on families of protein interaction domains. Here we provide protocols for constructing microarrays of protein interaction domains in individual wells of 96-well microtiter plates, and for quantifying domain-peptide interactions in high throughput using fluorescently labeled synthetic peptides. As specific examples, we will describe the construction of microarrays of virtually every human Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain, as well as microarrays of mouse PDZ domains, all produced recombinantly in Escherichia coli. For domains that mediate high-affinity interactions, such as SH2 and PTB domains, equilibrium dissociation constants (K(D)s) for their peptide ligands can be measured directly on arrays by obtaining saturation binding curves. For weaker binding domains, such as PDZ domains, arrays are best used to identify candidate interactions, which are then retested and quantified by fluorescence polarization. Overall, protein domain microarrays provide the ability to rapidly identify and quantify protein-ligand interactions with minimal sample consumption. Because entire domain families can be interrogated simultaneously, they provide a powerful way to assess binding selectivity on a proteome-wide scale and provide an unbiased perspective on the connectivity of protein-protein interaction networks.

  7. Building protein-protein interaction networks for Leishmania species through protein structural information.

    PubMed

    Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

    2018-03-06

    Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

  8. Modeling disordered protein interactions from biophysical principles

    PubMed Central

    Christoffer, Charles; Terashi, Genki

    2017-01-01

    Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs. PMID:28394890

  9. 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

  10. Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

    PubMed Central

    Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

    2009-01-01

    Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains. PMID:19936254

  11. Assessment of the reliability of protein-protein interactions and protein function prediction.

    PubMed

    Deng, Minghua; Sun, Fengzhu; Chen, Ting

    2003-01-01

    As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex

  12. Coevolution study of mitochondria respiratory chain proteins: toward the understanding of protein--protein interaction.

    PubMed

    Yang, Ming; Ge, Yan; Wu, Jiayan; Xiao, Jingfa; Yu, Jun

    2011-05-20

    Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein--protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using "Mirror tree" method on the respiratory chain (RC) proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions (Wilcoxon test, p-value = 4.4 × 10(-6)). Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein--protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study. Copyright © 2011. Published by Elsevier Ltd.

  13. A protein interaction network analysis for yeast integral membrane protein.

    PubMed

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

    2008-01-01

    Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.

  14. Computational prediction of host-pathogen protein-protein interactions.

    PubMed

    Dyer, Matthew D; Murali, T M; Sobral, Bruno W

    2007-07-01

    Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. Supplementary data are available at Bioinformatics online.

  15. 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.

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

    PubMed

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

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

  17. 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.

  18. 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

  19. 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

  20. Detection of protein-protein interactions by ribosome display and protein in situ immobilisation.

    PubMed

    He, Mingyue; Liu, Hong; Turner, Martin; Taussig, Michael J

    2009-12-31

    We describe a method for identification of protein-protein interactions by combining two cell-free protein technologies, namely ribosome display and protein in situ immobilisation. The method requires only PCR fragments as the starting material, the target proteins being made through cell-free protein synthesis, either associated with their encoding mRNA as ribosome complexes or immobilised on a solid surface. The use of ribosome complexes allows identification of interacting protein partners from their attached coding mRNA. To demonstrate the procedures, we have employed the lymphocyte signalling proteins Vav1 and Grb2 and confirmed the interaction between Grb2 and the N-terminal SH3 domain of Vav1. The method has promise for library screening of pairwise protein interactions, down to the analytical level of individual domain or motif mapping.

  1. Integrated web visualizations for protein-protein interaction databases.

    PubMed

    Jeanquartier, Fleur; Jean-Quartier, Claire; Holzinger, Andreas

    2015-06-16

    Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

  2. A Discontinuous Potential Model for Protein-Protein Interactions.

    PubMed

    Shao, Qing; Hall, Carol K

    2016-01-01

    Protein-protein interactions play an important role in many biologic and industrial processes. In this work, we develop a two-bead-per-residue model that enables us to account for protein-protein interactions in a multi-protein system using discontinuous molecular dynamics simulations. This model deploys discontinuous potentials to describe the non-bonded interactions and virtual bonds to keep proteins in their native state. The geometric and energetic parameters are derived from the potentials of mean force between sidechain-sidechain, sidechain-backbone, and backbone-backbone pairs. The energetic parameters are scaled with the aim of matching the second virial coefficient of lysozyme reported in experiment. We also investigate the performance of several bond-building strategies.

  3. 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.

  4. Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

    DTIC Science & Technology

    2009-06-05

    the interaction data sets we determined, via comparisons with strict randomized simulations , the propensity for essential proteins to selectively...and analysis of high- quality PPI data sets. Materials and Methods We analyzed protein interaction networks for yeast and E. coli determined from Y2H...we reinvestigated the centrality-lethality rule, which implies that proteins having more interactions are more likely to be essential. From analysis

  5. 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.

  6. Hot-spot analysis for drug discovery targeting protein-protein interactions.

    PubMed

    Rosell, Mireia; Fernández-Recio, Juan

    2018-04-01

    Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

  7. Protein-protein interactions and cancer: targeting the central dogma.

    PubMed

    Garner, Amanda L; Janda, Kim D

    2011-01-01

    Between 40,000 and 200,000 protein-protein interactions have been predicted to exist within the human interactome. As these interactions are of a critical nature in many important cellular functions and their dysregulation is causal of disease, the modulation of these binding events has emerged as a leading, yet difficult therapeutic arena. In particular, the targeting of protein-protein interactions relevant to cancer is of fundamental importance as the tumor-promoting function of several aberrantly expressed proteins in the cancerous state is directly resultant of its ability to interact with a protein-binding partner. Of significance, these protein complexes play a crucial role in each of the steps of the central dogma of molecular biology, the fundamental processes of genetic transmission. With the many important discoveries being made regarding the mechanisms of these genetic process, the identification of new chemical probes are needed to better understand and validate the druggability of protein-protein interactions related to the central dogma. In this review, we provide an overview of current small molecule-based protein-protein interaction inhibitors for each stage of the central dogma: transcription, mRNA splicing and translation. Importantly, through our analysis we have uncovered a lack of necessary probes targeting mRNA splicing and translation, thus, opening up the possibility for expansion of these fields.

  8. 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.

  9. 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.

  10. Quality control methodology for high-throughput protein-protein interaction screening.

    PubMed

    Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

    2011-01-01

    Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

  11. 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

  12. Quantifying the molecular origins of opposite solvent effects on protein-protein interactions.

    PubMed

    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.

  13. Identification of Protein-Protein Interactions with Glutathione-S-Transferase (GST) Fusion Proteins.

    PubMed

    Einarson, Margret B; Pugacheva, Elena N; Orlinick, Jason R

    2007-08-01

    INTRODUCTIONGlutathione-S-transferase (GST) fusion proteins have had a wide range of applications since their introduction as tools for synthesis of recombinant proteins in bacteria. GST was originally selected as a fusion moiety because of several desirable properties. First and foremost, when expressed in bacteria alone, or as a fusion, GST is not sequestered in inclusion bodies (in contrast to previous fusion protein systems). Second, GST can be affinity-purified without denaturation because it binds to immobilized glutathione, which provides the basis for simple purification. Consequently, GST fusion proteins are routinely used for antibody generation and purification, protein-protein interaction studies, and biochemical analysis. This article describes the use of GST fusion proteins as probes for the identification of protein-protein interactions.

  14. Monitoring protein-protein interactions using split synthetic renilla luciferase protein-fragment-assisted complementation.

    PubMed

    Paulmurugan, R; Gambhir, S S

    2003-04-01

    In this study we developed an inducible synthetic renilla luciferase protein-fragment-assisted complementation-based bioluminescence assay to quantitatively measure real time protein-protein interactions in mammalian cells. We identified suitable sites to generate fragments of N and C portions of the protein that yield significant recovered activity through complementation. We validate complementation-based activation of split synthetic renilla luciferase protein driven by the interaction of two strongly interacting proteins, MyoD and Id, in five different cell lines utilizing transient transfection studies. The expression level of the system was also modulated by tumor necrosis factor alpha through NFkappaB-promoter/enhancer elements used to drive expression of the N portion of synthetic renilla luciferase reporter gene. This new system should help in studying protein-protein interactions and when used with other split reporters (e.g., split firefly luciferase) should help to monitor different components of an intracellular network.

  15. [Detection of protein-protein interactions by FRET and BRET methods].

    PubMed

    Matoulková, E; Vojtěšek, B

    2014-01-01

    Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

  16. Selection of peptides interfering with protein-protein interaction.

    PubMed

    Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

    2009-01-01

    Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

  17. NMR studies of protein-nucleic acid interactions.

    PubMed

    Varani, Gabriele; Chen, Yu; Leeper, Thomas C

    2004-01-01

    Protein-DNA and protein-RNA complexes play key functional roles in every living organism. Therefore, the elucidation of their structure and dynamics is an important goal of structural and molecular biology. Nuclear magnetic resonance (NMR) studies of protein and nucleic acid complexes have common features with studies of protein-protein complexes: the interaction surfaces between the molecules must be carefully delineated, the relative orientation of the two species needs to be accurately and precisely determined, and close intermolecular contacts defined by nuclear Overhauser effects (NOEs) must be obtained. However, differences in NMR properties (e.g., chemical shifts) and biosynthetic pathways for sample productions generate important differences. Chemical shift differences between the protein and nucleic acid resonances can aid the NMR structure determination process; however, the relatively limited dispersion of the RNA ribose resonances makes the process of assigning intermolecular NOEs more difficult. The analysis of the resulting structures requires computational tools unique to nucleic acid interactions. This chapter summarizes the most important elements of the structure determination by NMR of protein-nucleic acid complexes and their analysis. The main emphasis is on recent developments (e.g., residual dipolar couplings and new Web-based analysis tools) that have facilitated NMR studies of these complexes and expanded the type of biological problems to which NMR techniques of structural elucidation can now be applied.

  18. 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.

  19. The Role of Shape Complementarity in the Protein-Protein Interactions

    NASA Astrophysics Data System (ADS)

    Li, Ye; Zhang, Xianren; Cao, Dapeng

    2013-11-01

    We use a dissipative particle dynamic simulation to investigate the effects of shape complementarity on the protein-protein interactions. By monitoring different kinds of protein shape-complementarity modes, we gave a clear mechanism to reveal the role of the shape complementarity in the protein-protein interactions, i.e., when the two proteins with shape complementarity approach each other, the conformation of lipid chains between two proteins would be restricted significantly. The lipid molecules tend to leave the gap formed by two proteins to maximize the configuration entropy, and therefore yield an effective entropy-induced protein-protein attraction, which enhances the protein aggregation. In short, this work provides an insight into understanding the importance of the shape complementarity in the protein-protein interactions especially for protein aggregation and antibody-antigen complexes. Definitely, the shape complementarity is the third key factor affecting protein aggregation and complex, besides the electrostatic-complementarity and hydrophobic complementarity.

  20. Dynamic Fluctuations of Protein-Carbohydrate Interactions Promote Protein Aggregation

    PubMed Central

    Voynov, Vladimir; Chennamsetty, Naresh; Kayser, Veysel; Helk, Bernhard; Forrer, Kurt; Zhang, Heidi; Fritsch, Cornelius; Heine, Holger; Trout, Bernhardt L.

    2009-01-01

    Protein-carbohydrate interactions are important for glycoprotein structure and function. Antibodies of the IgG class, with increasing significance as therapeutics, are glycosylated at a conserved site in the constant Fc region. We hypothesized that disruption of protein-carbohydrate interactions in the glycosylated domain of antibodies leads to the exposure of aggregation-prone motifs. Aggregation is one of the main problems in protein-based therapeutics because of immunogenicity concerns and decreased efficacy. To explore the significance of intramolecular interactions between aromatic amino acids and carbohydrates in the IgG glycosylated domain, we utilized computer simulations, fluorescence analysis, and site-directed mutagenesis. We find that the surface exposure of one aromatic amino acid increases due to dynamic fluctuations. Moreover, protein-carbohydrate interactions decrease upon stress, while protein-protein and carbohydrate-carbohydrate interactions increase. Substitution of the carbohydrate-interacting aromatic amino acids with non-aromatic residues leads to a significantly lower stability than wild type, and to compromised binding to Fc receptors. Our results support a mechanism for antibody aggregation via decreased protein-carbohydrate interactions, leading to the exposure of aggregation-prone regions, and to aggregation. PMID:20037630

  1. 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.

  2. 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

    2017-10-03

    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.

  3. 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.

  4. The Role of Shape Complementarity in the Protein-Protein Interactions

    PubMed Central

    Li, Ye; Zhang, Xianren; Cao, Dapeng

    2013-01-01

    We use a dissipative particle dynamic simulation to investigate the effects of shape complementarity on the protein-protein interactions. By monitoring different kinds of protein shape-complementarity modes, we gave a clear mechanism to reveal the role of the shape complementarity in the protein-protein interactions, i.e., when the two proteins with shape complementarity approach each other, the conformation of lipid chains between two proteins would be restricted significantly. The lipid molecules tend to leave the gap formed by two proteins to maximize the configuration entropy, and therefore yield an effective entropy-induced protein-protein attraction, which enhances the protein aggregation. In short, this work provides an insight into understanding the importance of the shape complementarity in the protein-protein interactions especially for protein aggregation and antibody–antigen complexes. Definitely, the shape complementarity is the third key factor affecting protein aggregation and complex, besides the electrostatic-complementarity and hydrophobic complementarity. PMID:24253561

  5. Polyamines: naturally occurring small molecule modulators of electrostatic protein-protein interactions.

    PubMed

    Berwanger, Anja; Eyrisch, Susanne; Schuster, Inge; Helms, Volkhard; Bernhardt, Rita

    2010-02-01

    Modulations of protein-protein interactions are a key step in regulating protein function, especially in networks. Modulators of these interactions are supposed to be candidates for the development of novel drugs. Here, we describe the role of the small, polycationic and highly abundant natural polyamines that could efficiently bind to charged spots at protein interfaces as modulators of such protein-protein interactions. Using the mitochondrial cytochrome P45011A1 (CYP11A1) electron transfer system as a model, we have analyzed the capability of putrescine, spermidine, and spermine at physiologically relevant concentrations to affect the protein-protein interactions between adrenodoxin reductase (AdR), adrenodoxin (Adx), and CYP11A1. The actions of polyamines on the individual components, on their association/dissociation, on electron transfer, and on substrate conversion were examined. These studies revealed modulating effects of polyamines on distinct interactions and on the entire system in a complex way. Modulation via changed protein-protein interactions appeared plausible from docking experiments that suggested favourable high-affinity binding sites of polyamines (spermine>spermidine>putrescine) at the AdR-Adx interface. Our findings imply for the first time that small endogenous compounds are capable of interfering with distinct components of transient protein complexes and might control protein functions by modulating electrostatic protein-protein interactions.

  6. Protein-protein recognition control by modulating electrostatic interactions.

    PubMed

    Han, Song; Yin, Shijin; Yi, Hong; Mouhat, Stéphanie; Qiu, Su; Cao, Zhijian; Sabatier, Jean-Marc; Wu, Yingliang; Li, Wenxin

    2010-06-04

    Protein-protein control recognition remains a huge challenge, and its development depends on understanding the chemical and biological mechanisms by which these interactions occur. Here we describe a protein-protein control recognition technique based on the dominant electrostatic interactions occurring between the proteins. We designed a potassium channel inhibitor, BmP05-T, that was 90.32% identical to wild-type BmP05. Negatively charged residues were translocated from the nonbinding interface to the binding interface of BmP05 inhibitor, such that BmP05-T now used BmP05 nonbinding interface as the binding interface. This switch demonstrated that nonbinding interfaces were able to control the orientation of protein binding interfaces in the process of protein-protein recognition. The novel function findings of BmP05-T peptide suggested that the control recognition technique described here had the potential for use in designing and utilizing functional proteins in many biological scenarios.

  7. Druggable orthosteric and allosteric hot spots to target protein-protein interactions.

    PubMed

    Ma, Buyong; Nussinov, Ruth

    2014-01-01

    Drug designing targeting protein-protein interactions is challenging. Because structural elucidation and computational analysis have revealed the importance of hot spot residues in stabilizing these interactions, there have been on-going efforts to develop drugs which bind the hot spots and out-compete the native protein partners. The question arises as to what are the key 'druggable' properties of hot spots in protein-protein interactions and whether these mimic the general hot spot definition. Identification of orthosteric (at the protein- protein interaction site) and allosteric (elsewhere) druggable hot spots is expected to help in discovering compounds that can more effectively modulate protein-protein interactions. For example, are there any other significant features beyond their location in pockets in the interface? The interactions of protein-protein hot spots are coupled with conformational dynamics of protein complexes. Currently increasing efforts focus on the allosteric drug discovery. Allosteric drugs bind away from the native binding site and can modulate the native interactions. We propose that identification of allosteric hot spots could similarly help in more effective allosteric drug discovery. While detection of allosteric hot spots is challenging, targeting drugs to these residues has the potential of greatly increasing the hot spot and protein druggability.

  8. In silico prediction of protein-protein interactions in human macrophages

    PubMed Central

    2014-01-01

    Background Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level. PMID:24636261

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

    PubMed Central

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

    2014-01-01

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

  10. Light-scattering studies of protein solutions: role of hydration in weak protein-protein interactions.

    PubMed

    Paliwal, A; Asthagiri, D; Abras, D; Lenhoff, A M; Paulaitis, M E

    2005-09-01

    We model the hydration contribution to short-range electrostatic/dispersion protein interactions embodied in the osmotic second virial coefficient, B(2), by adopting a quasi-chemical description in which water molecules associated with the protein are identified through explicit molecular dynamics simulations. These water molecules reduce the surface complementarity of highly favorable short-range interactions, and therefore can play an important role in mediating protein-protein interactions. Here we examine this quasi-chemical view of hydration by predicting the interaction part of B(2) and comparing our results with those derived from light-scattering measurements of B(2) for staphylococcal nuclease, lysozyme, and chymotrypsinogen at 25 degrees C as a function of solution pH and ionic strength. We find that short-range protein interactions are influenced by water molecules strongly associated with a relatively small fraction of the protein surface. However, the effect of these strongly associated water molecules on the surface complementarity of short-range protein interactions is significant, and must be taken into account for an accurate description of B(2). We also observe remarkably similar hydration behavior for these proteins despite substantial differences in their three-dimensional structures and spatial charge distributions, suggesting a general characterization of protein hydration.

  11. 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

  12. 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. Copyright © 2012 Wiley Periodicals, Inc.

  13. Biospecific protein immobilization for rapid analysis of weak protein interactions using self-interaction nanoparticle spectroscopy.

    PubMed

    Bengali, Aditya N; Tessier, Peter M

    2009-10-01

    "Reversible" protein interactions govern diverse biological behavior ranging from intracellular transport and toxic protein aggregation to protein crystallization and inactivation of protein therapeutics. Much less is known about weak protein interactions than their stronger counterparts since they are difficult to characterize, especially in a parallel format (in contrast to a sequential format) necessary for high-throughput screening. We have recently introduced a highly efficient approach of characterizing protein self-association, namely self-interaction nanoparticle spectroscopy (SINS; Tessier et al., 2008; J Am Chem Soc 130:3106-3112). This approach exploits the separation-dependent optical properties of gold nanoparticles to detect weak self-interactions between proteins immobilized on nanoparticles. A limitation of our previous work is that differences in the sequence and structure of proteins can lead to significant differences in their affinity to adsorb to nanoparticle surfaces, which complicates analysis of the corresponding protein self-association behavior. In this work we demonstrate a highly specific approach for coating nanoparticles with proteins using biotin-avidin interactions to generate protein-nanoparticle conjugates that report protein self-interactions through changes in their optical properties. Using lysozyme as a model protein that is refractory to characterization by conventional SINS, we demonstrate that surface Plasmon wavelengths for gold-avidin-lysozyme conjugates over a range of solution conditions (i.e., pH and ionic strength) are well correlated with lysozyme osmotic second virial coefficient measurements. Since SINS requires orders of magnitude less protein and time than conventional methods (e.g., static light scattering), we envision this approach will find application in large screens of protein self-association aimed at either preventing (e.g., protein aggregation) or promoting (e.g., protein crystallization) these

  14. 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.

  15. The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

    PubMed Central

    Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

    2009-01-01

    We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

  16. Revealing protein functions based on relationships of interacting proteins and GO terms.

    PubMed

    Teng, Zhixia; Guo, Maozu; Liu, Xiaoyan; Tian, Zhen; Che, Kai

    2017-09-20

    In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they interact with each other. However, it is reported by recent studies that the functions of two interacting proteins may be just related. It will mislead the prediction of protein function. Therefore, there is a need for investigating the functional relationship between interacting proteins. In this paper, the functional relationship between interacting proteins is studied and a novel method, called as GoDIN, is advanced to annotate functions of interacting proteins in Gene Ontology (GO) context. It is assumed that the functional difference between interacting proteins can be expressed by semantic difference between GO term and its relatives. Thus, the method uses GO term and its relatives to annotate the interacting proteins separately according to their functional roles in the PPI network. The method is validated by a series of experiments and compared with the concerned method. The experimental results confirm the assumption and suggest that GoDIN is effective on predicting functions of protein. This study demonstrates that: (1) interacting proteins are not equal in the PPI network, and their function may be same or similar, or just related; (2) functional difference between interacting proteins can be measured by their degrees in the PPI network; (3) functional relationship between interacting proteins can be expressed by relationship between GO term and its relatives.

  17. Potential Interference of Protein-Protein Interactions by Graphyne.

    PubMed

    Luan, Binquan; Huynh, Tien; Zhou, Ruhong

    2016-03-10

    Graphyne has attracted tremendous attention recently due to its many potentially superior properties relative to those of graphene. Although extensive efforts have been devoted to explore the applicability of graphyne as an alternative nanomaterial for state-of-the-art nanotechnology (including biomedical applications), knowledge regarding its possible adverse effects to biological cells is still lacking. Here, using large-scale all-atom molecular dynamics simulations, we investigate the potential toxicity of graphyne by interfering a protein-protein interaction (ppI). We found that graphyne could indeed disrupt the ppIs by cutting through the protein-protein interface and separating the protein complex into noncontacting ones, due to graphyne's dispersive and hydrophobic interaction with the hydrophobic residues residing at the dimer interface. Our results help to elucidate the mechanism of interaction between graphyne and ppI networks within a biological cell and provide insights for its hazard reduction.

  18. Molecular simulation of the effect of cholesterol on lipid-mediated protein-protein interactions.

    PubMed

    de Meyer, Frédérick J-M; Rodgers, Jocelyn M; Willems, Thomas F; Smit, Berend

    2010-12-01

    Experiments and molecular simulations have shown that the hydrophobic mismatch between proteins and membranes contributes significantly to lipid-mediated protein-protein interactions. In this article, we discuss the effect of cholesterol on lipid-mediated protein-protein interactions as function of hydrophobic mismatch, protein diameter and protein cluster size, lipid tail length, and temperature. To do so, we study a mesoscopic model of a hydrated bilayer containing lipids and cholesterol in which proteins are embedded, with a hybrid dissipative particle dynamics-Monte Carlo method. We propose a mechanism by which cholesterol affects protein interactions: protein-induced, cholesterol-enriched, or cholesterol-depleted lipid shells surrounding the proteins affect the lipid-mediated protein-protein interactions. Our calculations of the potential of mean force between proteins and protein clusters show that the addition of cholesterol dramatically reduces repulsive lipid-mediated interactions between proteins (protein clusters) with positive mismatch, but does not affect attractive interactions between proteins with negative mismatch. Cholesterol has only a modest effect on the repulsive interactions between proteins with different mismatch. Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  19. Manipulating Protein-Protein Interactions in Nonribosomal Peptide Synthetase Type II Peptidyl Carrier Proteins.

    PubMed

    Jaremko, Matt J; Lee, D John; Patel, Ashay; Winslow, Victoria; Opella, Stanley J; McCammon, J Andrew; Burkart, Michael D

    2017-10-10

    In an effort to elucidate and engineer interactions in type II nonribosomal peptide synthetases, we analyzed biomolecular recognition between the essential peptidyl carrier proteins and adenylation domains using nuclear magnetic resonance (NMR) spectroscopy, molecular dynamics, and mutational studies. Three peptidyl carrier proteins, PigG, PltL, and RedO, in addition to their cognate adenylation domains, PigI, PltF, and RedM, were investigated for their cross-species activity. Of the three peptidyl carrier proteins, only PigG showed substantial cross-pathway activity. Characterization of the novel NMR solution structure of holo-PigG and molecular dynamics simulations of holo-PltL and holo-PigG revealed differences in structures and dynamics of these carrier proteins. NMR titration experiments revealed perturbations of the chemical shifts of the loop 1 residues of these peptidyl carrier proteins upon their interaction with the adenylation domain. These experiments revealed a key region for the protein-protein interaction. Mutational studies supported the role of loop 1 in molecular recognition, as mutations to this region of the peptidyl carrier proteins significantly modulated their activities.

  20. InterPred: A pipeline to identify and model protein-protein interactions.

    PubMed

    Mirabello, Claudio; Wallner, Björn

    2017-06-01

    Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred Proteins 2017; 85:1159-1170. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

    PubMed

    Wuchty, Stefan

    2006-05-23

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

  2. Computational Prediction of Protein-Protein Interactions

    PubMed Central

    Ehrenberger, Tobias; Cantley, Lewis C.; Yaffe, Michael B.

    2015-01-01

    The prediction of protein-protein interactions and kinase-specific phosphorylation sites on individual proteins is critical for correctly placing proteins within signaling pathways and networks. The importance of this type of annotation continues to increase with the continued explosion of genomic and proteomic data, particularly with emerging data categorizing posttranslational modifications on a large scale. A variety of computational tools are available for this purpose. In this chapter, we review the general methodologies for these types of computational predictions and present a detailed user-focused tutorial of one such method and computational tool, Scansite, which is freely available to the entire scientific community over the Internet. PMID:25859943

  3. [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.

  4. The Ser/Thr Protein Kinase Protein-Protein Interaction Map of M. tuberculosis.

    PubMed

    Wu, Fan-Lin; Liu, Yin; Jiang, He-Wei; Luan, Yi-Zhao; Zhang, Hai-Nan; He, Xiang; Xu, Zhao-Wei; Hou, Jing-Li; Ji, Li-Yun; Xie, Zhi; Czajkowsky, Daniel M; Yan, Wei; Deng, Jiao-Yu; Bi, Li-Jun; Zhang, Xian-En; Tao, Sheng-Ce

    2017-08-01

    Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, e.g. MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  5. 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

  6. Mapping protein-protein interactions using yeast two-hybrid assays.

    PubMed

    Mehla, Jitender; Caufield, J Harry; Uetz, Peter

    2015-05-01

    Yeast two-hybrid (Y2H) screens are an efficient system for mapping protein-protein interactions and whole interactomes. The screens can be performed using random libraries or collections of defined open reading frames (ORFs) called ORFeomes. This protocol describes both library and array-based Y2H screening, with an emphasis on array-based assays. Array-based Y2H is commonly used to test a number of "prey" proteins for interactions with a single "bait" (target) protein or pool of proteins. The advantage of this approach is the direct identification of interacting protein pairs without further downstream experiments: The identity of the preys is known and does not require further confirmation. In contrast, constructing and screening a random prey library requires identification of individual prey clones and systematic retesting. Retesting is typically performed in an array format. © 2015 Cold Spring Harbor Laboratory Press.

  7. The Modular Organization of Protein Interactions in Escherichia coli

    PubMed Central

    Peregrín-Alvarez, José M.; Xiong, Xuejian; Su, Chong; Parkinson, John

    2009-01-01

    Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks. PMID:19798435

  8. Modifications in nanoparticle-protein interactions by varying the protein conformation

    NASA Astrophysics Data System (ADS)

    Kumar, Sugam; Yadav, I.; Aswal, V. K.; Kohlbrecher, J.

    2017-05-01

    Small-angle neutron scattering has been used to study the interaction of silica nanoparticle with Bovine Serum Albumin (BSA) protein without and with a protein denaturing agent urea. The measurements have been carried out at pH 7 where both the components (nanoparticle and protein) are similarly charged. We show that the interactions in nanoparticle-protein system can be modified by changing the conformation of protein through the presence of urea. In the absence of urea, the strong electrostatic repulsion between the nanoparticle and protein prevents protein adsorption on nanoparticle surface. This non-adsorption, in turn gives rise to depletion attraction between nanoparticles. However, with addition of urea the depletion attraction is completely suppressed. Urea driven denaturation of protein is utilized to expose the positively charged patched of the BSA molecules which eventually leads to adsorption of BSA on nanoparticles eliminating the depletion interaction.

  9. iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence

    PubMed Central

    Turner, Brian; Razick, Sabry; Turinsky, Andrei L.; Vlasblom, James; Crowdy, Edgard K.; Cho, Emerson; Morrison, Kyle; Wodak, Shoshana J.

    2010-01-01

    We present iRefWeb, a web interface to protein interaction data consolidated from 10 public databases: BIND, BioGRID, CORUM, DIP, IntAct, HPRD, MINT, MPact, MPPI and OPHID. iRefWeb enables users to examine aggregated interactions for a protein of interest, and presents various statistical summaries of the data across databases, such as the number of organism-specific interactions, proteins and cited publications. Through links to source databases and supporting evidence, researchers may gauge the reliability of an interaction using simple criteria, such as the detection methods, the scale of the study (high- or low-throughput) or the number of cited publications. Furthermore, iRefWeb compares the information extracted from the same publication by different databases, and offers means to follow-up possible inconsistencies. We provide an overview of the consolidated protein–protein interaction landscape and show how it can be automatically cropped to aid the generation of meaningful organism-specific interactomes. iRefWeb can be accessed at: http://wodaklab.org/iRefWeb. Database URL: http://wodaklab.org/iRefWeb/ PMID:20940177

  10. Scoring functions for protein-protein interactions.

    PubMed

    Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

    2013-12-01

    The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. 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

  12. 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.

  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. Bacteriophage Protein–Protein Interactions

    PubMed Central

    Häuser, Roman; Blasche, Sonja; Dokland, Terje; Haggård-Ljungquist, Elisabeth; von Brunn, Albrecht; Salas, Margarita; Casjens, Sherwood; Molineux, Ian

    2012-01-01

    Bacteriophages T7, λ, P22, and P2/P4 (from Escherichia coli), as well as ϕ29 (from Bacillus subtilis), are among the best-studied bacterial viruses. This chapter summarizes published protein interaction data of intraviral protein interactions, as well as known phage–host protein interactions of these phages retrieved from the literature. We also review the published results of comprehensive protein interaction analyses of Pneumococcus phages Dp-1 and Cp-1, as well as coliphages λ and T7. For example, the ≈55 proteins encoded by the T7 genome are connected by ≈43 interactions with another ≈15 between the phage and its host. The chapter compiles published interactions for the well-studied phages λ (33 intra-phage/22 phage-host), P22 (38/9), P2/P4 (14/3), and ϕ29 (20/2). We discuss whether different interaction patterns reflect different phage lifestyles or whether they may be artifacts of sampling. Phages that infect the same host can interact with different host target proteins, as exemplified by E. coli phage λ and T7. Despite decades of intensive investigation, only a fraction of these phage interactomes are known. Technical limitations and a lack of depth in many studies explain the gaps in our knowledge. Strategies to complete current interactome maps are described. Although limited space precludes detailed overviews of phage molecular biology, this compilation will allow future studies to put interaction data into the context of phage biology. PMID:22748812

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Beers, Eric; Brunner, Amy; Helm, Richard

    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 themore » 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

  16. 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.

  17. Identification of Modules in Protein-Protein Interaction Networks

    NASA Astrophysics Data System (ADS)

    Erten, Sinan; Koyutürk, Mehmet

    In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

  18. Bifunctional fusion proteins of calmodulin and protein A as affinity ligands in protein purification and in the study of protein-protein interactions.

    PubMed

    Hentz, N G; Daunert, S

    1996-11-15

    An affinity chromatography system is described that incorporates a genetically designed bifunctional affinity ligand. The utility of the system in protein purification and in the study of protein-protein interactions is demonstrated by using the interaction between protein A and the heat shock protein DnaK as a model system. The bifunctional affinity ligand was developed by genetically fusing calmodulin (CaM) to protein A (ProtA). The dual functionality of protein A-calmodulin (ProtA-CaM) stems from the molecular recognition properties of the two components of the fusion protein. In particular, CaM serves as the anchoring component by virtue of its binding properties toward phenothiazine. Thus, the ProtA-CaM can be immobilized on a solid support containing phenothiazine from the C-terminal domain of the fusion protein. Protein A is at the N-terminal domain of the fusion protein and serves as the affinity site for DnaK. While DnaK binds specifically to the protein A domain of the bifunctional ligand, it is released upon addition of ATP and under very mild conditions (pH 7.0). In addition to obtaining highly purified DnaK, this system is very rugged in terms of its performance. The proteinaceous bifunctional affinity ligand can be easily removed by addition of EGTA, and fresh ProtA-CaM can be easily reloaded onto the column. This allows for a facile regeneration of the affinity column because the phenothiazine-silica support matrix is stable for long periods of time under a variety of conditions. This study also demonstrates that calmodulin fusions can provide a new approach to study protein-protein interactions. Indeed, the ProtA-CaM fusion protein identified DnaK as a cellular component that interacts with protein A from among the thousands of proteins present in Escherichia coli.

  19. New paradigm in ankyrin repeats: Beyond protein-protein interaction module.

    PubMed

    Islam, Zeyaul; Nagampalli, Raghavendra Sashi Krishna; Fatima, Munazza Tamkeen; Ashraf, Ghulam Md

    2018-04-01

    Classically, ankyrin repeat (ANK) proteins are built from tandems of two or more repeats and form curved solenoid structures that are associated with protein-protein interactions. These are short, widespread structural motif of around 33 amino acids repeats in tandem, having a canonical helix-loop-helix fold, found individually or in combination with other domains. The multiplicity of structural pattern enables it to form assemblies of diverse sizes, required for their abilities to confer multiple binding and structural roles of proteins. Three-dimensional structures of these repeats determined to date reveal a degree of structural variability that translates into the considerable functional versatility of this protein superfamily. Recent work on the ANK has proposed novel structural information, especially protein-lipid, protein-sugar and protein-protein interaction. Self-assembly of these repeats was also shown to prevent the associated protein in forming filaments. In this review, we summarize the latest findings and how the new structural information has increased our understanding of the structural determinants of ANK proteins. We discussed latest findings on how these proteins participate in various interactions to diversify the ANK roles in numerous biological processes, and explored the emerging and evolving field of designer ankyrins and its framework for protein engineering emphasizing on biotechnological applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. 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

  1. Identification of host cellular proteins that interact with the M protein of a highly pathogenic porcine reproductive and respiratory syndrome virus vaccine strain.

    PubMed

    Wang, Qian; Li, Yanwei; Dong, Hong; Wang, Li; Peng, Jinmei; An, Tongqing; Yang, Xufu; Tian, Zhijun; Cai, Xuehui

    2017-02-22

    The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) continues to pose one of the greatest threats to the swine industry. M protein is the most conserved and important structural protein of PRRSV. However, information about the host cellular proteins that interact with M protein remains limited. Host cellular proteins that interact with the M protein of HP-PRRSV were immunoprecipitated from MARC-145 cells infected with PRRSV HuN4-F112 using the M monoclonal antibody (mAb). The differentially expressed proteins were identified by LC-MS/MS. The screened proteins were used for bioinformatics analysis including Gene Ontology, the interaction network, and the enriched KEGG pathways. Some interested cellular proteins were validated to interact with M protein by CO-IP. The PRRSV HuN4-F112 infection group had 10 bands compared with the control group. The bands included 219 non-redundant cellular proteins that interact with M protein, which were identified by LC-MS/MS with high confidence. The gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway bioinformatic analyses indicated that the identified proteins could be assigned to several different subcellular locations and functional classes. Functional analysis of the interactome profile highlighted cellular pathways associated with protein translation, infectious disease, and signal transduction. Two interested cellular proteins-nuclear factor of activated T cells 45 kDa (NF45) and proliferating cell nuclear antigen (PCNA)-that could interact with M protein were validated by Co-IP and confocal analyses. The interactome data between PRRSV M protein and cellular proteins were identified and contribute to the understanding of the roles of M protein in the replication and pathogenesis of PRRSV. The interactome of M protein will aid studies of virus/host interactions and provide means to decrease the threat of PRRSV to the swine industry in the future.

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

    PubMed

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

    2014-09-07

    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.

  3. Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

    PubMed Central

    Ruan, Peiying; Hayashida, Morihiro; Maruyama, Osamu; Akutsu, Tatsuya

    2013-01-01

    Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes. PMID:23776458

  4. Phthalic acid chemical probes synthesized for protein-protein interaction analysis.

    PubMed

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

    2013-06-24

    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.

  5. 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.

  6. Conservation of hot regions in protein-protein interaction in evolution.

    PubMed

    Hu, Jing; Li, Jiarui; Chen, Nansheng; Zhang, Xiaolong

    2016-11-01

    The hot regions of protein-protein interactions refer to the active area which formed by those most important residues to protein combination process. With the research development on protein interactions, lots of predicted hot regions can be discovered efficiently by intelligent computing methods, while performing biology experiments to verify each every prediction is hardly to be done due to the time-cost and the complexity of the experiment. This study based on the research of hot spot residue conservations, the proposed method is used to verify authenticity of predicted hot regions that using machine learning algorithm combined with protein's biological features and sequence conservation, though multiple sequence alignment, module substitute matrix and sequence similarity to create conservation scoring algorithm, and then using threshold module to verify the conservation tendency of hot regions in evolution. This research work gives an effective method to verify predicted hot regions in protein-protein interactions, which also provides a useful way to deeply investigate the functional activities of protein hot regions. Copyright © 2016. Published by Elsevier Inc.

  7. 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.

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

    PubMed

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

    2016-06-14

    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.

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Feng, Mei; Kang, Hongsuk; Luan, Binquan

    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 andmore » 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.« less

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

    PubMed Central

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

    2000-01-01

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

  11. 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

  12. Predicting Protein-Protein Interactions by Combing Various Sequence-Derived.

    PubMed

    Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

    2011-09-20

    Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

  13. Predicting Physical Interactions between Protein Complexes*

    PubMed Central

    Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

    2013-01-01

    Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

  14. Targeting protein-protein interaction between MLL1 and reciprocal proteins for leukemia therapy.

    PubMed

    Wang, Zhi-Hui; Li, Dong-Dong; Chen, Wei-Lin; You, Qi-Dong; Guo, Xiao-Ke

    2018-01-15

    The mixed lineage leukemia protein-1 (MLL1), as a lysine methyltransferase, predominantly regulates the methylation of histone H3 lysine 4 (H3K4) and functions in hematopoietic stem cell (HSC) self-renewal. MLL1 gene fuses with partner genes that results in the generation of MLL1 fusion proteins (MLL1-FPs), which are frequently detected in acute leukemia. In the progress of leukemogenesis, a great deal of proteins cooperate with MLL1 to form multiprotein complexes serving for the dysregulation of H3K4 methylation, the overexpression of homeobox (HOX) cluster genes, and the consequent generation of leukemia. Hence, disrupting the interactions between MLL1 and the reciprocal proteins has been considered to be a new treatment strategy for leukemia. Here, we reviewed potential protein-protein interactions (PPIs) between MLL1 and its reciprocal proteins, and summarized the inhibitors to target MLL1 PPIs. The druggability of MLL1 PPIs for leukemia were also discussed. Copyright © 2017. Published by Elsevier Ltd.

  15. A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

    PubMed

    Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

    2018-06-01

    Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

  16. Measuring protein-protein and protein-nucleic Acid interactions by biolayer interferometry.

    PubMed

    Sultana, Azmiri; Lee, Jeffrey E

    2015-02-02

    Biolayer interferometry (BLI) is a simple, optical dip-and-read system useful for measuring interactions between proteins, peptides, nucleic acids, small molecules, and/or lipids in real time. In BLI, a biomolecular bait is immobilized on a matrix at the tip of a fiber-optic sensor. The binding between the immobilized ligand and another molecule in an analyte solution produces a change in optical thickness at the tip and results in a wavelength shift proportional to binding. BLI provides direct binding affinities and rates of association and dissociation. This unit describes an efficient approach using streptavidin-based BLI to analyze DNA-protein and protein-protein interactions. A quantitative set of equilibrium binding affinities (K(d)) and rates of association and dissociation (k(a)/k(d)) can be measured in minutes using nanomole quantities of sample. Copyright © 2015 John Wiley & Sons, Inc.

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

    PubMed Central

    Peri, Claudio; Morra, Giulia; Colombo, Giorgio

    2016-01-01

    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. PMID:27050828

  18. PodNet, a protein-protein interaction network of the podocyte.

    PubMed

    Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

    2013-07-01

    Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

  19. Relevance of protein-protein interactions on the biological identity of nanoparticles.

    PubMed

    Vasti, Cecilia; Bonnet, Laura V; Galiano, Mauricio R; Rojas, Ricardo; Giacomelli, Carla E

    2018-06-01

    Considering that the use of nanoparticles (NPs) as carriers of therapeutic or theranostic agents has increased in the last years, it is mandatory to understand the interaction between NPs and living systems. In contact with biological fluids, the NPs (synthetic identity) are covered with biomolecules that form a protein corona, which defines the biological identity. It is well known that the protein corona formation is mediated by non-specific physical interactions, but protein-protein interactions (PPI), involving specific recognition sites of the polypeptides, are also involved. This work explores the relationship between the synthetic and biological identities of layered double hydroxides nanoparticles (LDH-NPs) and the effect of the protein corona on the cellular response. With such a purpose, the synthetic identity was modified by coating LDH-NPs with either a single protein or a complex mixture of them, followed by the characterization of the protein corona formed in a commonly used cell culture medium. A proteomic approach was used to identify the protein corona molecules and the PPI network was constructed with a novel bioinformatic tool. The coating on LDH-NPs defines the biological identity in such a way that the composition of the protein corona as well as PPI are changed. Electrostatic interactions appear not to be the only driving force regulating the interactions between NPs, proteins and cells since the specific recognition also play a fundamental role. However, the biological identity of LDH-NPs does not affect the interactions with cells that shows negligible cytotoxicity and high internalization levels. Copyright © 2018 Elsevier B.V. All rights reserved.

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

    PubMed

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

    2017-01-01

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

  1. Cross-regulatory protein-protein interactions between Hox and Pax transcription factors.

    PubMed

    Plaza, Serge; Prince, Frederic; Adachi, Yoshitsugu; Punzo, Claudio; Cribbs, David L; Gehring, Walter J

    2008-09-09

    Homeotic Hox selector genes encode highly conserved transcriptional regulators involved in the differentiation of multicellular organisms. Ectopic expression of the Antennapedia (ANTP) homeodomain protein in Drosophila imaginal discs induces distinct phenotypes, including an antenna-to-leg transformation and eye reduction. We have proposed that the eye loss phenotype is a consequence of a negative posttranslational control mechanism because of direct protein-protein interactions between ANTP and Eyeless (EY). In the present work, we analyzed the effect of various ANTP homeodomain mutations for their interaction with EY and for head development. Contrasting with the eye loss phenotype, we provide evidence that the antenna-to-leg transformation involves ANTP DNA-binding activity. In a complementary genetic screen performed in yeast, we isolated mutations located in the N terminus of the ANTP homeodomain that inhibit direct interactions with EY without abolishing DNA binding in vitro and in vivo. In a bimolecular fluorescence complementation assay, we detected the ANTP-EY interaction in vivo, these interactions occurring through the paired domain and/or the homeodomain of EY. These results demonstrate that the homeodomain supports multiple molecular regulatory functions in addition to protein-DNA and protein-RNA interactions; it is also involved in protein-protein interactions.

  2. 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.

  3. Molecular imaging of drug-modulated protein-protein interactions in living subjects.

    PubMed

    Paulmurugan, Ramasamy; Massoud, Tarik F; Huang, Jing; Gambhir, Sanjiv S

    2004-03-15

    Networks of protein interactions mediate cellular responses to environmental stimuli and direct the execution of many different cellular functional pathways. Small molecules synthesized within cells or recruited from the external environment mediate many protein interactions. The study of small molecule-mediated interactions of proteins is important to understand abnormal signal transduction pathways in cancer and in drug development and validation. In this study, we used split synthetic renilla luciferase (hRLUC) protein fragment-assisted complementation to evaluate heterodimerization of the human proteins FRB and FKBP12 mediated by the small molecule rapamycin. The concentration of rapamycin required for efficient dimerization and that of its competitive binder ascomycin required for dimerization inhibition were studied in cell lines. The system was dually modulated in cell culture at the transcription level, by controlling nuclear factor kappaB promoter/enhancer elements using tumor necrosis factor alpha, and at the interaction level, by controlling the concentration of the dimerizer rapamycin. The rapamycin-mediated dimerization of FRB and FKBP12 also was studied in living mice by locating, quantifying, and timing the hRLUC complementation-based bioluminescence imaging signal using a cooled charged coupled device camera. This split reporter system can be used to efficiently screen small molecule drugs that modulate protein-protein interactions and also to assess drugs in living animals. Both are essential steps in the preclinical evaluation of candidate pharmaceutical agents targeting protein-protein interactions, including signaling pathways in cancer cells.

  4. Visualizing an ultra-weak protein-protein interaction in phosphorylation signaling.

    PubMed

    Xing, Qiong; Huang, Peng; Yang, Ju; Sun, Jian-Qiang; Gong, Zhou; Dong, Xu; Guo, Da-Chuan; Chen, Shao-Min; Yang, Yu-Hong; Wang, Yan; Yang, Ming-Hui; Yi, Ming; Ding, Yi-Ming; Liu, Mai-Li; Zhang, Wei-Ping; Tang, Chun

    2014-10-20

    Proteins interact with each other to fulfill their functions. The importance of weak protein-protein interactions has been increasingly recognized. However, owing to technical difficulties, ultra-weak interactions remain to be characterized. Phosphorylation can take place via a K(D)≈25 mM interaction between two bacterial enzymes. Using paramagnetic NMR spectroscopy and with the introduction of a novel Gd(III)-based probe, we determined the structure of the resulting complex to atomic resolution. The structure accounts for the mechanism of phosphoryl transfer between the two enzymes and demonstrates the physical basis for their ultra-weak interaction. Further, molecular dynamics (MD) simulations suggest that the complex has a lifetime in the micro- to millisecond regimen. Hence such interaction is termed a fleeting interaction. From mathematical modeling, we propose that an ultra-weak fleeting interaction enables rapid flux of phosphoryl signal, providing a high effective protein concentration. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Multiple protein-protein interactions converging on the Prp38 protein during activation of the human spliceosome.

    PubMed

    Schütze, Tonio; Ulrich, Alexander K C; Apelt, Luise; Will, Cindy L; Bartlick, Natascha; Seeger, Martin; Weber, Gert; Lührmann, Reinhard; Stelzl, Ulrich; Wahl, Markus C

    2016-02-01

    Spliceosomal Prp38 proteins contain a conserved amino-terminal domain, but only higher eukaryotic orthologs also harbor a carboxy-terminal RS domain, a hallmark of splicing regulatory SR proteins. We show by crystal structure analysis that the amino-terminal domain of human Prp38 is organized around three pairs of antiparallel α-helices and lacks similarities to RNA-binding domains found in canonical SR proteins. Instead, yeast two-hybrid analyses suggest that the amino-terminal domain is a versatile protein-protein interaction hub that possibly binds 12 other spliceosomal proteins, most of which are recruited at the same stage as Prp38. By quantitative, alanine surface-scanning two-hybrid screens and biochemical analyses we delineated four distinct interfaces on the Prp38 amino-terminal domain. In vitro interaction assays using recombinant proteins showed that Prp38 can bind at least two proteins simultaneously via two different interfaces. Addition of excess Prp38 amino-terminal domain to in vitro splicing assays, but not of an interaction-deficient mutant, stalled splicing at a precatalytic stage. Our results show that human Prp38 is an unusual SR protein, whose amino-terminal domain is a multi-interface protein-protein interaction platform that might organize the relative positioning of other proteins during splicing. © 2016 Schütze et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  6. On the role of electrostatics in protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Zhang, Zhe; Witham, Shawn; Alexov, Emil

    2011-06-01

    The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which 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 compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.

  7. Protein-protein interactions within late pre-40S ribosomes.

    PubMed

    Campbell, Melody G; Karbstein, Katrin

    2011-01-20

    Ribosome assembly in eukaryotic organisms requires more than 200 assembly factors to facilitate and coordinate rRNA transcription, processing, and folding with the binding of the ribosomal proteins. Many of these assembly factors bind and dissociate at defined times giving rise to discrete assembly intermediates, some of which have been partially characterized with regards to their protein and RNA composition. Here, we have analyzed the protein-protein interactions between the seven assembly factors bound to late cytoplasmic pre-40S ribosomes using recombinant proteins in binding assays. Our data show that these factors form two modules: one comprising Enp1 and the export adaptor Ltv1 near the beak structure, and the second comprising the kinase Rio2, the nuclease Nob1, and a regulatory RNA binding protein Dim2/Pno1 on the front of the head. The GTPase-like Tsr1 and the universally conserved methylase Dim1 are also peripherally connected to this second module. Additionally, in an effort to further define the locations for these essential proteins, we have analyzed the interactions between these assembly factors and six ribosomal proteins: Rps0, Rps3, Rps5, Rps14, Rps15 and Rps29. Together, these results and previous RNA-protein crosslinking data allow us to propose a model for the binding sites of these seven assembly factors. Furthermore, our data show that the essential kinase Rio2 is located at the center of the pre-ribosomal particle and interacts, directly or indirectly, with every other assembly factor, as well as three ribosomal proteins required for cytoplasmic 40S maturation. These data suggest that Rio2 could play a central role in regulating cytoplasmic maturation steps.

  8. A selection that reports on protein-protein interactions within a thermophilic bacterium.

    PubMed

    Nguyen, Peter Q; Silberg, Jonathan J

    2010-07-01

    Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

  9. iview: an interactive WebGL visualizer for protein-ligand complex.

    PubMed

    Li, Hongjian; Leung, Kwong-Sak; Nakane, Takanori; Wong, Man-Hon

    2014-02-25

    Visualization of protein-ligand complex plays an important role in elaborating protein-ligand interactions and aiding novel drug design. Most existing web visualizers either rely on slow software rendering, or lack virtual reality support. The vital feature of macromolecular surface construction is also unavailable. We have developed iview, an easy-to-use interactive WebGL visualizer of protein-ligand complex. It exploits hardware acceleration rather than software rendering. It features three special effects in virtual reality settings, namely anaglyph, parallax barrier and oculus rift, resulting in visually appealing identification of intermolecular interactions. It supports four surface representations including Van der Waals surface, solvent excluded surface, solvent accessible surface and molecular surface. Moreover, based on the feature-rich version of iview, we have also developed a neat and tailor-made version specifically for our istar web platform for protein-ligand docking purpose. This demonstrates the excellent portability of iview. Using innovative 3D techniques, we provide a user friendly visualizer that is not intended to compete with professional visualizers, but to enable easy accessibility and platform independence.

  10. Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

    PubMed Central

    Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

    2015-01-01

    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. PMID:26490033

  11. Moonlighting Proteins and Protein–Protein Interactions as Neurotherapeutic Targets in the G Protein-Coupled Receptor Field

    PubMed Central

    Fuxe, Kjell; Borroto-Escuela, Dasiel O; Romero-Fernandez, Wilber; Palkovits, Miklós; Tarakanov, Alexander O; Ciruela, Francisco; Agnati, Luigi F

    2014-01-01

    There is serious interest in understanding the dynamics of the receptor–receptor and receptor–protein interactions in space and time and their integration in GPCR heteroreceptor complexes of the CNS. Moonlighting proteins are special multifunctional proteins because they perform multiple autonomous, often unrelated, functions without partitioning into different protein domains. Moonlighting through receptor oligomerization can be operationally defined as an allosteric receptor–receptor interaction, which leads to novel functions of at least one receptor protomer. GPCR-mediated signaling is a more complicated process than previously described as every GPCR and GPCR heteroreceptor complex requires a set of G protein interacting proteins, which interacts with the receptor in an orchestrated spatio-temporal fashion. GPCR heteroreceptor complexes with allosteric receptor–receptor interactions operating through the receptor interface have become major integrative centers at the molecular level and their receptor protomers act as moonlighting proteins. The GPCR heteroreceptor complexes in the CNS have become exciting new targets for neurotherapeutics in Parkinson's disease, schizophrenia, drug addiction, and anxiety and depression opening a new field in neuropsychopharmacology. PMID:24105074

  12. 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).

  13. Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

    PubMed

    Suratanee, Apichat; Plaimas, Kitiporn

    2017-01-01

    The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

  14. A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions

    PubMed Central

    Hostetler, Jessica B.; Sharma, Sumana; Bartholdson, S. Josefin; Wright, Gavin J.; Fairhurst, Rick M.; Rayner, Julian C.

    2015-01-01

    Background A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP) and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC), and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion. Methodology/Principal Findings We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further

  15. 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

  16. Blocking the interaction between S100A9 protein and RAGE V domain using S100A12 protein.

    PubMed

    Katte, Revansiddha; Yu, Chin

    2018-01-01

    The proteins S100A9 and S100A12 are associated with the human S100 calcium-binding protein family. These proteins promote interaction with target proteins and alter their conformation when they bind to calcium ions in EF-hand motifs. The V domain of RAGE (Receptor for Advanced Glycation End products) is crucial for S100A9 binding. The binding of RAGE with S100 family proteins aids in cell proliferation. In this report, we demonstrate that S100A12 protein hinders the binding of S100A9 with the RAGE V-domain. We used fluorescence and NMR spectroscopy to analyze the interaction of S100A9 with S100A12. The binary complex models of S100A9-S100A12 were developed using data obtained from 1H-15N HSQC NMR titrations and the HADDOCK program. We overlaid the complex models of S100A9-S100A12 with the same orientation of S100A9 and the RAGE V-domain. This complex showed that S100A12 protein blocks the interaction between S100A9 and the RAGE V-domain. It means S100A12 may be used as an antagonist for S100A9. The results could be favorable for developing anti-cancer drugs based on S100 family proteins.

  17. Identification of structural protein-protein interactions of herpes simplex virus type 1.

    PubMed

    Lee, Jin H; Vittone, Valerio; Diefenbach, Eve; Cunningham, Anthony L; Diefenbach, Russell J

    2008-09-01

    In this study we have defined protein-protein interactions between the structural proteins of herpes simplex virus type 1 (HSV-1) using a LexA yeast two-hybrid system. The majority of the capsid, tegument and envelope proteins of HSV-1 were screened in a matrix approach. A total of 40 binary interactions were detected including 9 out of 10 previously identified tegument-tegument interactions (Vittone, V., Diefenbach, E., Triffett, D., Douglas, M.W., Cunningham, A.L., and Diefenbach, R.J., 2005. Determination of interactions between tegument proteins of herpes simplex virus type 1. J. Virol. 79, 9566-9571). A total of 12 interactions involving the capsid protein pUL35 (VP26) and 11 interactions involving the tegument protein pUL46 (VP11/12) were identified. The most significant novel interactions detected in this study, which are likely to play a role in viral assembly, include pUL35-pUL37 (capsid-tegument), pUL46-pUL37 (tegument-tegument) and pUL49 (VP22)-pUS9 (tegument-envelope). This information will provide further insights into the pathways of HSV-1 assembly and the identified interactions are potential targets for new antiviral drugs.

  18. 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/ .

  19. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    PubMed

    Rezende, Antonio M; Folador, Edson L; Resende, Daniela de M; Ruiz, Jeronimo C

    2012-01-01

    The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree

  20. The protein interaction map of bacteriophage lambda

    PubMed Central

    2011-01-01

    Background Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage. Results In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome") into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%). We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda. Conclusions Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins. PMID:21943085

  1. Protein-Protein Interactions within Late Pre-40S Ribosomes

    PubMed Central

    Campbell, Melody G.; Karbstein, Katrin

    2011-01-01

    Ribosome assembly in eukaryotic organisms requires more than 200 assembly factors to facilitate and coordinate rRNA transcription, processing, and folding with the binding of the ribosomal proteins. Many of these assembly factors bind and dissociate at defined times giving rise to discrete assembly intermediates, some of which have been partially characterized with regards to their protein and RNA composition. Here, we have analyzed the protein-protein interactions between the seven assembly factors bound to late cytoplasmic pre-40S ribosomes using recombinant proteins in binding assays. Our data show that these factors form two modules: one comprising Enp1 and the export adaptor Ltv1 near the beak structure, and the second comprising the kinase Rio2, the nuclease Nob1, and a regulatory RNA binding protein Dim2/Pno1 on the front of the head. The GTPase-like Tsr1 and the universally conserved methylase Dim1 are also peripherally connected to this second module. Additionally, in an effort to further define the locations for these essential proteins, we have analyzed the interactions between these assembly factors and six ribosomal proteins: Rps0, Rps3, Rps5, Rps14, Rps15 and Rps29. Together, these results and previous RNA-protein crosslinking data allow us to propose a model for the binding sites of these seven assembly factors. Furthermore, our data show that the essential kinase Rio2 is located at the center of the pre-ribosomal particle and interacts, directly or indirectly, with every other assembly factor, as well as three ribosomal proteins required for cytoplasmic 40S maturation. These data suggest that Rio2 could play a central role in regulating cytoplasmic maturation steps. PMID:21283762

  2. Functional requirements of AID's higher order structures and their interaction with RNA-binding proteins.

    PubMed

    Mondal, Samiran; Begum, Nasim A; Hu, Wenjun; Honjo, Tasuku

    2016-03-15

    Activation-induced cytidine deaminase (AID) is essential for the somatic hypermutation (SHM) and class-switch recombination (CSR) of Ig genes. Although both the N and C termini of AID have unique functions in DNA cleavage and recombination, respectively, during SHM and CSR, their molecular mechanisms are poorly understood. Using a bimolecular fluorescence complementation (BiFC) assay combined with glycerol gradient fractionation, we revealed that the AID C terminus is required for a stable dimer formation. Furthermore, AID monomers and dimers form complexes with distinct heterogeneous nuclear ribonucleoproteins (hnRNPs). AID monomers associate with DNA cleavage cofactor hnRNP K whereas AID dimers associate with recombination cofactors hnRNP L, hnRNP U, and Serpine mRNA-binding protein 1. All of these AID/ribonucleoprotein associations are RNA-dependent. We propose that AID's structure-specific cofactor complex formations differentially contribute to its DNA-cleavage and recombination functions.

  3. Hexahistidine (6xHis) fusion-based assays for protein-protein interactions.

    PubMed

    Puckett, Mary C

    2015-01-01

    Fusion-protein tags provide a useful method to study protein-protein interactions. One widely used fusion tag is hexahistidine (6xHis). This tag has unique advantages over others due to its small size and the relatively low abundance of naturally occurring consecutive histidine repeats. 6xHis tags can interact with immobilized metal cations to provide for the capture of proteins and protein complexes of interest. In this chapter, a description of the benefits and uses of 6xHis-fusion proteins as well as a detailed method for performing a 6xHis-pulldown assay are described.

  4. Free energy decomposition of protein-protein interactions.

    PubMed

    Noskov, S Y; Lim, C

    2001-08-01

    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.

  5. Monitoring Ligand-Activated Protein-Protein Interactions Using Bioluminescent Resonance Energy Transfer (BRET) Assay.

    PubMed

    Coriano, Carlos; Powell, Emily; Xu, Wei

    2016-01-01

    The bioluminescent resonance energy transfer (BRET) assay has been extensively used in cell-based and in vivo imaging systems for detecting protein-protein interactions in the native environment of living cells. These protein-protein interactions are essential for the functional response of many signaling pathways to environmental chemicals. BRET has been used as a toxicological tool for identifying chemicals that either induce or inhibit these protein-protein interactions. This chapter focuses on describing the toxicological applications of BRET and its optimization as a high-throughput detection system in live cells. Here we review the construction of BRET fusion proteins, describe the BRET methodology, and outline strategies to overcome obstacles that may arise. Furthermore, we describe the advantage of BRET over other resonance energy transfer methods for monitoring protein-protein interactions.

  6. Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms.

    PubMed

    Lin, Xiaotong; Liu, Mei; Chen, Xue-wen

    2009-04-29

    Protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. Although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. Since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. Thus, it is essential to assess the quality of the protein interaction data. In this paper, a novel Bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. We develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. Our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. Furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. Applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. Finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. We observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. This study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. The proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. We expect the method to continually improve as more high quality interaction data from more

  7. Protein Charge and Mass Contribute to the Spatio-temporal Dynamics of Protein-Protein Interactions in a Minimal Proteome

    PubMed Central

    Xu, Yu; Wang, Hong; Nussinov, Ruth; Ma, Buyong

    2013-01-01

    We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae. PMID:23420643

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

    PubMed

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

    2013-01-01

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

  9. Prediction of protein-protein interaction sites using electrostatic desolvation profiles.

    PubMed

    Fiorucci, Sébastien; Zacharias, Martin

    2010-05-19

    Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. Copyright (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  10. Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles

    PubMed Central

    Fiorucci, Sébastien; Zacharias, Martin

    2010-01-01

    Abstract Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. PMID:20441756

  11. Role of water mediated interactions in protein-protein recognition landscapes.

    PubMed

    Papoian, Garegin A; Ulander, Johan; Wolynes, Peter G

    2003-07-30

    The energy landscape picture of protein folding and binding is employed to optimize a number of pair potentials for direct and water-mediated interactions in protein complex interfaces. We find that water-mediated interactions greatly complement direct interactions in discriminating against various types of trap interactions that model those present in the cell. We highlight the context dependent nature of knowledge-based binding potentials, as contrasted with the situation for autonomous folding. By performing a Principal Component Analysis (PCA) of the corresponding interaction matrixes, we rationalize the strength of the recognition signal for each combination of the contact type and reference trap states using the differential in the idealized "canonical" amino acid compositions of native and trap layers. The comparison of direct and water-mediated contact potential matrixes emphasizes the importance of partial solvation in stabilizing charged groups in the protein interfaces. Specific water-mediated interresidue interactions are expected to influence significantly the kinetics as well as thermodynamics of protein association.

  12. 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.

  13. 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

  14. 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

  15. Bioluminescence Resonance Energy Transfer System for Measuring Dynamic Protein-Protein Interactions in Bacteria

    PubMed Central

    Cui, Boyu; Wang, Yao; Song, Yunhong; Wang, Tietao; Li, Changfu; Wei, Yahong

    2014-01-01

    ABSTRACT 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. PMID:24846380

  16. 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

  17. The Protein Interactome of Mycobacteriophage Giles Predicts Functions for Unknown Proteins.

    PubMed

    Mehla, Jitender; Dedrick, Rebekah M; Caufield, J Harry; Siefring, Rachel; Mair, Megan; Johnson, Allison; Hatfull, Graham F; Uetz, Peter

    2015-08-01

    Mycobacteriophages are viruses that infect mycobacterial hosts and are prevalent in the environment. Nearly 700 mycobacteriophage genomes have been completely sequenced, revealing considerable diversity and genetic novelty. Here, we have determined the protein complement of mycobacteriophage Giles by mass spectrometry and mapped its genome-wide protein interactome to help elucidate the roles of its 77 predicted proteins, 50% of which have no known function. About 22,000 individual yeast two-hybrid (Y2H) tests with four different Y2H vectors, followed by filtering and retest screens, resulted in 324 reproducible protein-protein interactions, including 171 (136 nonredundant) high-confidence interactions. The complete set of high-confidence interactions among Giles proteins reveals new mechanistic details and predicts functions for unknown proteins. The Giles interactome is the first for any mycobacteriophage and one of just five known phage interactomes so far. Our results will help in understanding mycobacteriophage biology and aid in development of new genetic and therapeutic tools to understand Mycobacterium tuberculosis. Mycobacterium tuberculosis causes over 9 million new cases of tuberculosis each year. Mycobacteriophages, viruses of mycobacterial hosts, hold considerable potential to understand phage diversity, evolution, and mycobacterial biology, aiding in the development of therapeutic tools to control mycobacterial infections. The mycobacteriophage Giles protein-protein interaction network allows us to predict functions for unknown proteins and shed light on major biological processes in phage biology. For example, Giles gp76, a protein of unknown function, is found to associate with phage packaging and maturation. The functions of mycobacteriophage-derived proteins may suggest novel therapeutic approaches for tuberculosis. Our ORFeome clone set of Giles proteins and the interactome data will be useful resources for phage interactomics. Copyright © 2015

  18. Luciferase Complementation Imaging Assay in Nicotiana benthamiana Leaves for Transiently Determining Protein-protein Interaction Dynamics.

    PubMed

    Sun, Kaiwen; Zheng, Yuyu; Zhu, Ziqiang

    2017-11-20

    Protein-protein interactions are fundamental mechanisms for relaying signal transduction in most cellular processes; therefore, identification of novel protein-protein interaction pairs and monitoring protein interaction dynamics are of particular interest for revealing how plants respond to environmental factors and/or developmental signals. A plethora of approaches have been developed to examine protein-protein interactions, either in vitro or in vivo. Among them, the recently established luciferase complementation imaging (LCI) assay is the simplest and fastest method for demonstrating in vivo protein-protein interactions. In this assay, protein A or protein B is fused with the amino-terminal or carboxyl-terminal half of luciferase, respectively. When protein A interacts with protein B, the two halves of luciferase will be reconstituted to form a functional and active luciferase enzyme. Luciferase activity can be recorded with a luminometer or CCD-camera. Compared with other approaches, the LCI assay shows protein-protein interactions both qualitatively and quantitatively. Agrobacterium infiltration in Nicotiana benthamiana leaves is a widely used system for transient protein expression. With the combination of LCI and transient expression, these approaches show that the physical interaction between COP1 and SPA1 was gradually reduced after jasmonate treatment.

  19. A Luciferase-fragment Complementation Assay to Detect Lipid Droplet-associated Protein-Protein Interactions*

    PubMed Central

    Kolkhof, Petra; Werthebach, Michael; van de Venn, Anna; Poschmann, Gereon; Chen, Lili; Welte, Michael; Stühler, Kai; Beller, Mathias

    2017-01-01

    A critical challenge for all organisms is to carefully control the amount of lipids they store. An important node for this regulation is the protein coat present at the surface of lipid droplets (LDs), the intracellular organelles dedicated to lipid storage. Only limited aspects of this regulation are understood so far. For the probably best characterized case, the regulation of lipolysis in mammals, some of the major protein players have been identified, and it has been established that this process crucially depends on an orchestrated set of protein-protein interactions. Proteomic analysis has revealed that LDs are associated with dozens, if not hundreds, of different proteins, most of them poorly characterized, with even fewer data regarding which of them might physically interact. To comprehensively understand the mechanism of lipid storage regulation, it will likely be essential to define the interactome of LD-associated proteins. Previous studies of such interactions were hampered by technical limitations. Therefore, we have developed a split-luciferase based protein-protein interaction assay and test for interactions among 47 proteins from Drosophila and from mouse. We confirmed previously described interactions and identified many new ones. In 1561 complementation tests, we assayed for interactions among 487 protein pairs of which 92 (19%) resulted in a successful luciferase complementation. These results suggest that a prominent fraction of the LD-associated proteome participates in protein-protein interactions. In targeted experiments, we analyzed the two proteins Jabba and CG9186 in greater detail. Jabba mediates the sequestration of histones to LDs. We successfully applied our split luciferase complementation assay to learn more about this function as we were e.g. able to map the interaction between Jabba and histones. For CG9186, expression levels affect the positioning of LDs. Here, we reveal the ubiquitination of CG9186, and link this

  20. Towards Inferring Protein Interactions: Challenges and Solutions

    NASA Astrophysics Data System (ADS)

    Zhang, Ya; Zha, Hongyuan; Chu, Chao-Hsien; Ji, Xiang

    2006-12-01

    Discovering interacting proteins has been an essential part of functional genomics. However, existing experimental techniques only uncover a small portion of any interactome. Furthermore, these data often have a very high false rate. By conceptualizing the interactions at domain level, we provide a more abstract representation of interactome, which also facilitates the discovery of unobserved protein-protein interactions. Although several domain-based approaches have been proposed to predict protein-protein interactions, they usually assume that domain interactions are independent on each other for the convenience of computational modeling. A new framework to predict protein interactions is proposed in this paper, where no assumption is made about domain interactions. Protein interactions may be the result of multiple domain interactions which are dependent on each other. A conjunctive norm form representation is used to capture the relationships between protein interactions and domain interactions. The problem of interaction inference is then modeled as a constraint satisfiability problem and solved via linear programing. Experimental results on a combined yeast data set have demonstrated the robustness and the accuracy of the proposed algorithm. Moreover, we also map some predicted interacting domains to three-dimensional structures of protein complexes to show the validity of our predictions.

  1. Protein interactions and ligand binding: from protein subfamilies to functional specificity.

    PubMed

    Rausell, Antonio; Juan, David; Pazos, Florencio; Valencia, Alfonso

    2010-02-02

    The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as "specificity determining positions" (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.

  2. Computer-aided drug design for AMP-activated protein kinase activators.

    PubMed

    Wang, Zhanli; Huo, Jianxin; Sun, Lidan; Wang, Yongfu; Jin, Hongwei; Yu, Hui; Zhang, Liangren; Zhou, Lishe

    2011-09-01

    AMP-activated protein kinase (AMPK) is an important therapeutic target for the potential treatment of metabolic disorders, cardiovascular disease and cancer. Recently, various classes of compounds that activate AMPK by direct or indirect interactions have been reported. The importance of computer-aided drug design approaches in the search for potent activators of AMPK is now established, including structure-based design, ligand-based design, fragment-based design, as well as structural analysis. This review article highlights the computer-aided drug design approaches utilized to discover of activators targeting AMPK. The principles, advantages or limitation of the different methods are also being discussed together with examples of applications taken from the literatures.

  3. 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

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

    PubMed

    Nahálková, Jarmila; Tomkinson, Birgitta

    2014-12-15

    Tripeptidyl-peptidase II (TPPII) is an aminopeptidase with suggested regulatory effects on cell cycle, apoptosis and senescence. A protein-protein interaction study revealed that TPPII physically interacts with the tumor suppressor MYBBP1A and the cell cycle regulator protein CDK2. Mutual protein-protein interaction was detected between MYBBP1A and CDK2 as well. In situ Proximity Ligation Assay (PLA) using HEK293 cells overexpressing TPPII forming highly enzymatically active oligomeric complexes showed that the cytoplasmic interaction frequency of TPPII with MYBBP1A increased with the protein expression of TPPII and using serum-free cell growth conditions. A specific reversible inhibitor of TPPII, butabindide, suppressed the cytoplasmic interactions of TPPII and MYBBP1A both in control HEK293 and the cells overexpressing murine TPPII. The interaction of MYBBP1A with CDK2 was confirmed by in situ PLA in two different mammalian cell lines. Functional link between TPPII and MYBBP1A has been verified by gene expression study during anoikis, where overexpression of TPP II decreased mRNA expression level of MYBBP1A at the cell detachment conditions. All three interacting proteins TPPII, MYBBP1A and CDK2 have been previously implicated in the research for development of tumor-suppressing agents. This is the first report presenting mutual protein-protein interaction network of these proteins. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. An automated method for finding molecular complexes in large protein interaction networks

    PubMed Central

    Bader, Gary D; Hogue, Christopher WV

    2003-01-01

    Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. Results This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Conclusion Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from . PMID:12525261

  6. 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…

  7. Anomalous Protein-Protein Interactions in Multivalent Salt Solution.

    PubMed

    Pasquier, Coralie; Vazdar, Mario; Forsman, Jan; Jungwirth, Pavel; Lund, Mikael

    2017-04-13

    The stability of aqueous protein solutions is strongly affected by multivalent ions, which induce ion-ion correlations beyond the scope of classical mean-field theory. Using all-atom molecular dynamics (MD) and coarse grained Monte Carlo (MC) simulations, we investigate the interaction between a pair of protein molecules in 3:1 electrolyte solution. In agreement with available experimental findings of "reentrant protein condensation", we observe an anomalous trend in the protein-protein potential of mean force with increasing electrolyte concentration in the order: (i) double-layer repulsion, (ii) ion-ion correlation attraction, (iii) overcharge repulsion, and in excess of 1:1 salt, (iv) non Coulombic attraction. To efficiently sample configurational space we explore hybrid continuum solvent models, applicable to many-protein systems, where weakly coupled ions are treated implicitly, while strongly coupled ones are treated explicitly. Good agreement is found with the primitive model of electrolytes, as well as with atomic models of protein and solvent.

  8. Energy design for protein-protein interactions

    PubMed Central

    Ravikant, D. V. S.; Elber, Ron

    2011-01-01

    Proteins bind to other proteins efficiently and specifically to carry on many cell functions such as signaling, activation, transport, enzymatic reactions, and more. To determine the geometry and strength of binding of a protein pair, an energy function is required. An algorithm to design an optimal energy function, based on empirical data of protein complexes, is proposed and applied. Emphasis is made on negative design in which incorrect geometries are presented to the algorithm that learns to avoid them. For the docking problem the search for plausible geometries can be performed exhaustively. The possible geometries of the complex are generated on a grid with the help of a fast Fourier transform algorithm. A novel formulation of negative design makes it possible to investigate iteratively hundreds of millions of negative examples while monotonically improving the quality of the potential. Experimental structures for 640 protein complexes are used to generate positive and negative examples for learning parameters. The algorithm designed in this work finds the correct binding structure as the lowest energy minimum in 318 cases of the 640 examples. Further benchmarks on independent sets confirm the significant capacity of the scoring function to recognize correct modes of interactions. PMID:21842951

  9. Co-evolutionary Analysis of Domains in Interacting Proteins Reveals Insights into Domain–Domain Interactions Mediating Protein–Protein Interactions

    PubMed Central

    Jothi, Raja; Cherukuri, Praveen F.; Tasneem, Asba; Przytycka, Teresa M.

    2006-01-01

    Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the noninteracting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. PMID:16949097

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

    NASA Technical Reports Server (NTRS)

    Samanta, Manoj Pratim; Liang, Shoudan

    2003-01-01

    Interpreting data from large-scale protein interaction experiments has been a challenging task because of the widespread presence of random false positives. Here, we present a network-based statistical algorithm that overcomes this difficulty and allows us to derive functions of unannotated proteins from large-scale interaction data. Our algorithm uses the insight that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals >2,800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty. Our method is not overly sensitive to the false positives present in the data. Even after adding 50% randomly generated interactions to the measured data set, we are able to recover almost all (approximately 89%) of the original associations.

  11. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. 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

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

    PubMed

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

    2015-06-01

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

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

    PubMed

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

    2011-10-01

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

  15. Mapping protein-DNA and protein-protein interactions of ATP-dependent chromatin remodelers.

    PubMed

    Hota, Swetansu K; Dechassa, Mekonnen Lemma; Prasad, Punit; Bartholomew, Blaine

    2012-01-01

    Chromatin plays a key regulatory role in several DNA-dependent processes as it regulates DNA access to different protein factors. Several multisubunit protein complexes interact, modify, or mobilize nucleosomes: the basic unit of chromatin, from its original location in an ATP-dependent manner to facilitate processes, such as transcription, replication, repair, and recombination. Knowledge of the interactions of chromatin remodelers with nucleosomes is a crucial requirement to understand the mechanism of chromatin remodeling. Here, we describe several methods to analyze the interactions of multisubunit chromatin-remodeling enzymes with nucleosomes.

  16. Building blocks for protein interaction devices

    PubMed Central

    Grünberg, Raik; Ferrar, Tony S.; van der Sloot, Almer M.; Constante, Marco; Serrano, Luis

    2010-01-01

    Here, we propose a framework for the design of synthetic protein networks from modular protein–protein or protein–peptide interactions and provide a starter toolkit of protein building blocks. Our proof of concept experiments outline a general work flow for part–based protein systems engineering. We streamlined the iterative BioBrick cloning protocol and assembled 25 synthetic multidomain proteins each from seven standardized DNA fragments. A systematic screen revealed two main factors controlling protein expression in Escherichia coli: obstruction of translation initiation by mRNA secondary structure or toxicity of individual domains. Eventually, 13 proteins were purified for further characterization. Starting from well-established biotechnological tools, two general–purpose interaction input and two readout devices were built and characterized in vitro. Constitutive interaction input was achieved with a pair of synthetic leucine zippers. The second interaction was drug-controlled utilizing the rapamycin-induced binding of FRB(T2098L) to FKBP12. The interaction kinetics of both devices were analyzed by surface plasmon resonance. Readout was based on Förster resonance energy transfer between fluorescent proteins and was quantified for various combinations of input and output devices. Our results demonstrate the feasibility of parts-based protein synthetic biology. Additionally, we identify future challenges and limitations of modular design along with approaches to address them. PMID:20215443

  17. Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

    PubMed Central

    Laine, Elodie; Carbone, Alessandra

    2015-01-01

    Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2. PMID:26690684

  18. 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.

  19. 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

  20. 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

  1. NMR Studies of Protein Hydration and Protein-Ligand Interactions

    NASA Astrophysics Data System (ADS)

    Chong, Yuan

    Water on the surface of a protein is called hydration water. Hydration water is known to play a crucial role in a variety of biological processes including protein folding, enzymatic activation, and drug binding. Although the significance of hydration water has been recognized, the underlying mechanism remains far from being understood. This dissertation employs a unique in-situ nuclear magnetic resonance (NMR) technique to study the mechanism of protein hydration and the role of hydration in alcohol-protein interactions. Water isotherms in proteins are measured at different temperatures via the in-situ NMR technique. Water is found to interact differently with hydrophilic and hydrophobic groups on the protein. Water adsorption on hydrophilic groups is hardly affected by the temperature, while water adsorption on hydrophobic groups strongly depends on the temperature around 10 C, below which the adsorption is substantially reduced. This effect is induced by the dramatic decrease in the protein flexibility below 10 C. Furthermore, nanosecond to microsecond protein dynamics and the free energy, enthalpy, and entropy of protein hydration are studied as a function of hydration level and temperature. A crossover at 10 C in protein dynamics and thermodynamics is revealed. The effect of water at hydrophilic groups on protein dynamics and thermodynamics shows little temperature dependence, whereas water at hydrophobic groups has stronger effect above 10 C. In addition, I investigate the role of water in alcohol binding to the protein using the in-situ NMR detection. The isotherms of alcohols are first measured on dry proteins, then on proteins with a series of controlled hydration levels. The free energy, enthalpy, and entropy of alcohol binding are also determined. Two distinct types of alcohol binding are identified. On the one hand, alcohols can directly bind to a few specific sites on the protein. This type of binding is independent of temperature and can be

  2. Kinetic Measurements Reveal Enhanced Protein-Protein Interactions at Intercellular Junctions

    PubMed Central

    Shashikanth, Nitesh; Kisting, Meridith A.; Leckband, Deborah E.

    2016-01-01

    The binding properties of adhesion proteins are typically quantified from measurements with soluble fragments, under conditions that differ radically from the confined microenvironment of membrane bound proteins in adhesion zones. Using classical cadherin as a model adhesion protein, we tested the postulate that confinement within quasi two-dimensional intercellular gaps exposes weak protein interactions that are not detected in solution binding assays. Micropipette-based measurements of cadherin-mediated, cell-cell binding kinetics identified a unique kinetic signature that reflects both adhesive (trans) bonds between cadherins on opposing cells and lateral (cis) interactions between cadherins on the same cell. In solution, proposed lateral interactions were not detected, even at high cadherin concentrations. Mutations postulated to disrupt lateral cadherin association altered the kinetic signatures, but did not affect the adhesive (trans) binding affinity. Perturbed kinetics further coincided with altered cadherin distributions at junctions, wound healing dynamics, and paracellular permeability. Intercellular binding kinetics thus revealed cadherin interactions that occur within confined, intermembrane gaps but not in solution. Findings further demonstrate the impact of these revealed interactions on the organization and function of intercellular junctions. PMID:27009566

  3. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803.

    PubMed

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    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. 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. 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 http://synechocystis.org/ or directly at http://bioportal.kobic.kr/SynechoNET/.

  4. Structure and Protein-Protein Interaction Studies on Chlamydia trachomatis Protein CT670 (YscO Homolog)

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lorenzini, Emily; Singer, Alexander; Singh, Bhag

    2010-07-28

    Comparative genomic studies have identified many proteins that are found only in various Chlamydiae species and exhibit no significant sequence similarity to any protein in organisms that do not belong to this group. The CT670 protein of Chlamydia trachomatis is one of the proteins whose genes are in one of the type III secretion gene clusters but whose cellular functions are not known. CT670 shares several characteristics with the YscO protein of Yersinia pestis, including the neighboring genes, size, charge, and secondary structure, but the structures and/or functions of these proteins remain to be determined. Although a BLAST search withmore » CT670 did not identify YscO as a related protein, our analysis indicated that these two proteins exhibit significant sequence similarity. In this paper, we report that the CT670 crystal, solved at a resolution of 2 {angstrom}, consists of a single coiled coil containing just two long helices. Gel filtration and analytical ultracentrifugation studies showed that in solution CT670 exists in both monomeric and dimeric forms and that the monomer predominates at lower protein concentrations. We examined the interaction of CT670 with many type III secretion system-related proteins (viz., CT091, CT665, CT666, CT667, CT668, CT669, CT671, CT672, and CT673) by performing bacterial two-hybrid assays. In these experiments, CT670 was found to interact only with the CT671 protein (YscP homolog), whose gene is immediately downstream of ct670. A specific interaction between CT670 and CT671 was also observed when affinity chromatography pull-down experiments were performed. These results suggest that CT670 and CT671 are putative homologs of the YcoO and YscP proteins, respectively, and that they likely form a chaperone-effector pair.« less

  5. Parallel force assay for protein-protein interactions.

    PubMed

    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.

  6. SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome.

    PubMed

    Li, Yiwei; Ilie, Lucian

    2017-11-15

    Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory. We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory. SPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/ .

  7. Kinome signaling through regulated protein-protein interactions in normal and cancer cells.

    PubMed

    Pawson, Tony; Kofler, Michael

    2009-04-01

    The flow of molecular information through normal and oncogenic signaling pathways frequently depends on protein phosphorylation, mediated by specific kinases, and the selective binding of the resulting phosphorylation sites to interaction domains present on downstream targets. This physical and functional interplay of catalytic and interaction domains can be clearly seen in cytoplasmic tyrosine kinases such as Src, Abl, Fes, and ZAP-70. Although the kinase and SH2 domains of these proteins possess similar intrinsic properties of phosphorylating tyrosine residues or binding phosphotyrosine sites, they also undergo intramolecular interactions when linked together, in a fashion that varies from protein to protein. These cooperative interactions can have diverse effects on substrate recognition and kinase activity, and provide a variety of mechanisms to link the stimulation of catalytic activity to substrate recognition. Taken together, these data have suggested how protein kinases, and the signaling pathways in which they are embedded, can evolve complex properties through the stepwise linkage of domains within single polypeptides or multi-protein assemblies.

  8. Evidence for dynamically organized modularity in the yeast protein-protein interaction network

    NASA Astrophysics Data System (ADS)

    Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

    2004-07-01

    In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

  9. Engineering Aromatic-Aromatic Interactions To Nucleate Folding in Intrinsically Disordered Regions of Proteins.

    PubMed

    Balakrishnan, Swati; Sarma, Siddhartha P

    2017-08-22

    Aromatic interactions are an important force in protein folding as they combine the stability of a hydrophobic interaction with the selectivity of a hydrogen bond. Much of our understanding of aromatic interactions comes from "bioinformatics" based analyses of protein structures and from the contribution of these interactions to stabilizing secondary structure motifs in model peptides. In this study, the structural consequences of aromatic interactions on protein folding have been explored in engineered mutants of the molten globule protein apo-cytochrome b 5 . Structural changes from disorder to order due to aromatic interactions in two variants of the protein, viz., WF-cytb5 and FF-cytb5, result in significant long-range secondary and tertiary structure. The results show that 54 and 52% of the residues in WF-cytb5 and FF-cytb5, respectively, occupy ordered regions versus 26% in apo-cytochrome b 5 . The interactions between the aromatic groups are offset-stacked and edge-to-face for the Trp-Phe and Phe-Phe mutants, respectively. Urea denaturation studies indicate that both mutants have a C m higher than that of apo-cytochrome b 5 and are more stable to chaotropic agents than apo-cytochrome b 5 . The introduction of these aromatic residues also results in "trimer" interactions with existing aromatic groups, reaffirming the selectivity of the aromatic interactions. These studies provide insights into the aromatic interactions that drive disorder-to-order transitions in intrinsically disordered regions of proteins and will aid in de novo protein design beyond small peptide scaffolds.

  10. Dendrimer-protein interactions versus dendrimer-based nanomedicine.

    PubMed

    Shcharbin, Dzmitry; Shcharbina, Natallia; Dzmitruk, Volha; Pedziwiatr-Werbicka, Elzbieta; Ionov, Maksim; Mignani, Serge; de la Mata, F Javier; Gómez, Rafael; Muñoz-Fernández, Maria Angeles; Majoral, Jean-Pierre; Bryszewska, Maria

    2017-04-01

    Dendrimers are hyperbranched polymers belonging to the huge class of nanomedical devices. Their wide application in biology and medicine requires understanding of the fundamental mechanisms of their interactions with biological systems. Summarizing, electrostatic force plays the predominant role in dendrimer-protein interactions, especially with charged dendrimers. Other kinds of interactions have been proven, such as H-bonding, van der Waals forces, and even hydrophobic interactions. These interactions depend on the characteristics of both participants: flexibility and surface charge of a dendrimer, rigidity of protein structure and the localization of charged amino acids at its surface. pH and ionic strength of solutions can significantly modulate interactions. Ligands and cofactors attached to a protein can also change dendrimer-protein interactions. Binding of dendrimers to a protein can change its secondary structure, conformation, intramolecular mobility and functional activity. However, this strongly depends on rigidity versus flexibility of a protein's structure. In addition, the potential applications of dendrimers to nanomedicine are reviwed related to dendrimer-protein interactions. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Rapid discovery of protein interactions by cell-free protein technologies.

    PubMed

    He, M; Taussig, M J

    2007-11-01

    Cell-free transcription and translation provides an open, controllable environment for production of correctly folded, soluble proteins and allows the rapid generation of proteins from DNA without the need for cloning. Thus it is becoming an increasingly attractive alternative to conventional in vivo expression systems, especially when parallel expression of multiple proteins is required. Through novel design and exploitation, powerful cell-free technologies of ribosome display and protein in situ arrays have been developed for in vitro production and isolation of protein-binding molecules from large libraries. These technologies can be combined for rapid detection of protein interactions.

  12. RNA-protein interactions in an unstructured context.

    PubMed

    Zagrovic, Bojan; Bartonek, Lukas; Polyansky, Anton A

    2018-05-31

    Despite their importance, our understanding of noncovalent RNA-protein interactions is incomplete. This especially concerns the binding between RNA and unstructured protein regions, a widespread class of such interactions. Here, we review the recent experimental and computational work on RNA-protein interactions in an unstructured context with a particular focus on how such interactions may be shaped by the intrinsic interaction affinities between individual nucleobases and protein side chains. Specifically, we articulate the claim that the universal genetic code reflects the binding specificity between nucleobases and protein side chains and that, in turn, the code may be seen as the Rosetta stone for understanding RNA-protein interactions in general. © 2018 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

  13. Van der Waals Interactions Involving Proteins

    NASA Technical Reports Server (NTRS)

    Roth, Charles M.; Neal, Brian L.; Lenhoff, Abraham M.

    1996-01-01

    Van der Waals (dispersion) forces contribute to interactions of proteins with other molecules or with surfaces, but because of the structural complexity of protein molecules, the magnitude of these effects is usually estimated based on idealized models of the molecular geometry, e.g., spheres or spheroids. The calculations reported here seek to account for both the geometric irregularity of protein molecules and the material properties of the interacting media. Whereas the latter are found to fall in the generally accepted range, the molecular shape is shown to cause the magnitudes of the interactions to differ significantly from those calculated using idealized models. with important consequences. First, the roughness of the molecular surface leads to much lower average interaction energies for both protein-protein and protein-surface cases relative to calculations in which the protein molecule is approximated as a sphere. These results indicate that a form of steric stabilization may be an important effect in protein solutions. Underlying this behavior is appreciable orientational dependence, one reflection of which is that molecules of complementary shape are found to exhibit very strong attractive dispersion interactions. Although this has been widely discussed previously in the context of molecular recognition processes, the broader implications of these phenomena may also be important at larger molecular separations, e.g., in the dynamics of aggregation, precipitation, and crystal growth.

  14. Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein.

    PubMed

    Khamina, Kseniya; Lercher, Alexander; Caldera, Michael; Schliehe, Christopher; Vilagos, Bojan; Sahin, Mehmet; Kosack, Lindsay; Bhattacharya, Anannya; Májek, Peter; Stukalov, Alexey; Sacco, Roberto; James, Leo C; Pinschewer, Daniel D; Bennett, Keiryn L; Menche, Jörg; Bergthaler, Andreas

    2017-12-01

    RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

  15. Force spectroscopy studies on protein-ligand interactions: a single protein mechanics perspective.

    PubMed

    Hu, Xiaotang; Li, Hongbin

    2014-10-01

    Protein-ligand interactions are ubiquitous and play important roles in almost every biological process. The direct elucidation of the thermodynamic, structural and functional consequences of protein-ligand interactions is thus of critical importance to decipher the mechanism underlying these biological processes. A toolbox containing a variety of powerful techniques has been developed to quantitatively study protein-ligand interactions in vitro as well as in living systems. The development of atomic force microscopy-based single molecule force spectroscopy techniques has expanded this toolbox and made it possible to directly probe the mechanical consequence of ligand binding on proteins. Many recent experiments have revealed how ligand binding affects the mechanical stability and mechanical unfolding dynamics of proteins, and provided mechanistic understanding on these effects. The enhancement effect of mechanical stability by ligand binding has been used to help tune the mechanical stability of proteins in a rational manner and develop novel functional binding assays for protein-ligand interactions. Single molecule force spectroscopy studies have started to shed new lights on the structural and functional consequence of ligand binding on proteins that bear force under their biological settings. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

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

    PubMed Central

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

    2016-01-01

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

  17. Protein-Protein Interactions of Azurin Complex by Coarse-Grained Simulations with a Gō-Like Model

    NASA Astrophysics Data System (ADS)

    Rusmerryani, Micke; Takasu, Masako; Kawaguchi, Kazutomo; Saito, Hiroaki; Nagao, Hidemi

    Proteins usually perform their biological functions by forming a complex with other proteins. It is very important to study the protein-protein interactions since these interactions are crucial in many processes of a living organism. In this study, we develop a coarse grained model to simulate protein complex in liquid system. We carry out molecular dynamics simulations with topology-based potential interactions to simulate dynamical properties of Pseudomonas Aeruginosa azurin complex systems. Azurin is known to play an essential role as an anticancer agent and bind many important intracellular molecules. Some physical properties are monitored during simulation time to get a better understanding of the influence of protein-protein interactions to the azurin complex dynamics. These studies will provide valuable insights for further investigation on protein-protein interactions in more realistic system.

  18. Interaction of Proteins Identified in Human Thyroid Cells

    PubMed Central

    Pietsch, Jessica; Riwaldt, Stefan; Bauer, Johann; Sickmann, Albert; Weber, Gerhard; Grosse, Jirka; Infanger, Manfred; Eilles, Christoph; Grimm, Daniela

    2013-01-01

    Influence of gravity forces on the regulation of protein expression by healthy and malignant thyroid cells was studied with the aim to identify protein interactions. Western blot analyses of a limited number of proteins suggested a time-dependent regulation of protein expression by simulated microgravity. After applying free flow isoelectric focusing and mass spectrometry to search for differently expressed proteins by thyroid cells exposed to simulated microgravity for three days, a considerable number of candidates for gravi-sensitive proteins were detected. In order to show how proteins sensitive to microgravity could directly influence other proteins, we investigated all polypeptide chains identified with Mascot scores above 100, looking for groups of interacting proteins. Hence, UniProtKB entry numbers of all detected proteins were entered into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and processed. The program indicated that we had detected various groups of interacting proteins in each of the three cell lines studied. The major groups of interacting proteins play a role in pathways of carbohydrate and protein metabolism, regulation of cell growth and cell membrane structuring. Analyzing these groups, networks of interaction could be established which show how a punctual influence of simulated microgravity may propagate via various members of interaction chains. PMID:23303277

  19. Prediction of Protein Aggregation in High Concentration Protein Solutions Utilizing Protein-Protein Interactions Determined by Low Volume Static Light Scattering.

    PubMed

    Hofmann, Melanie; Winzer, Matthias; Weber, Christian; Gieseler, Henning

    2016-06-01

    The development of highly concentrated protein formulations is more demanding than for conventional concentrations due to an elevated protein aggregation tendency. Predictive protein-protein interaction parameters, such as the second virial coefficient B22 or the interaction parameter kD, have already been used to predict aggregation tendency and optimize protein formulations. However, these parameters can only be determined in diluted solutions, up to 20 mg/mL. And their validity at high concentrations is currently controversially discussed. This work presents a μ-scale screening approach which has been adapted to early industrial project needs. The procedure is based on static light scattering to directly determine protein-protein interactions at concentrations up to 100 mg/mL. Three different therapeutic molecules were formulated, varying in pH, salt content, and addition of excipients (e.g., sugars, amino acids, polysorbates, or other macromolecules). Validity of the predicted aggregation tendency was confirmed by stability data of selected formulations. Based on the results obtained, the new prediction method is a promising screening tool for fast and easy formulation development of highly concentrated protein solutions, consuming only microliter of sample volumes. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  20. Protein Surface Mimetics: Understanding How Ruthenium Tris(Bipyridines) Interact with Proteins.

    PubMed

    Hewitt, Sarah H; Filby, Maria H; Hayes, Ed; Kuhn, Lars T; Kalverda, Arnout P; Webb, Michael E; Wilson, Andrew J

    2017-01-17

    Protein surface mimetics achieve high-affinity binding by exploiting a scaffold to project binding groups over a large area of solvent-exposed protein surface to make multiple cooperative noncovalent interactions. Such recognition is a prerequisite for competitive/orthosteric inhibition of protein-protein interactions (PPIs). This paper describes biophysical and structural studies on ruthenium(II) tris(bipyridine) surface mimetics that recognize cytochrome (cyt) c and inhibit the cyt c/cyt c peroxidase (CCP) PPI. Binding is electrostatically driven, with enhanced affinity achieved through enthalpic contributions thought to arise from the ability of the surface mimetics to make a greater number of noncovalent interactions than CCP with surface-exposed basic residues on cyt c. High-field natural abundance 1 H, 15 N HSQC NMR experiments are consistent with surface mimetics binding to cyt c in similar manner to CCP. This provides a framework for understanding recognition of proteins by supramolecular receptors and informing the design of ligands superior to the protein partners upon which they are inspired. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Protein interaction networks from literature mining

    NASA Astrophysics Data System (ADS)

    Ihara, Sigeo

    2005-03-01

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

  2. Characterizing carbohydrate-protein interactions by NMR

    PubMed Central

    Bewley, Carole A.; Shahzad-ul-Hussan, Syed

    2013-01-01

    Interactions between proteins and soluble carbohydrates and/or surface displayed glycans are central to countless recognition, attachment and signaling events in biology. The physical chemical features associated with these binding events vary considerably, depending on the biological system of interest. For example, carbohydrate-protein interactions can be stoichiometric or multivalent, the protein receptors can be monomeric or oligomeric, and the specificity of recognition can be highly stringent or rather promiscuous. Equilibrium dissociation constants for carbohydrate binding are known to vary from micromolar to millimolar, with weak interactions being far more prevalent; and individual carbohydrate binding sites can be truly symmetrical or merely homologous, and hence, the affinities of individual sites within a single protein can vary, as can the order of binding. Several factors, including the weak affinities with which glycans bind their protein receptors, the dynamic nature of the glycans themselves, and the non-equivalent interactions among oligomeric carbohydrate receptors, have made NMR an especially powerful tool for studying and defining carbohydrate-protein interactions. Here we describe those NMR approaches that have proven to be the most robust in characterizing these systems, and explain what type of information can (or cannot) be obtained from each. Our goal is to provide to the reader the information necessary for selecting the correct experiment or sets of experiments to characterize their carbohydrate-protein interaction of interest. PMID:23784792

  3. Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

    PubMed Central

    Mazloom, Amin R.; Dannenfelser, Ruth; Clark, Neil R.; Grigoryan, Arsen V.; Linder, Kathryn M.; Cardozo, Timothy J.; Bond, Julia C.; Boran, Aislyn D. W.; Iyengar, Ravi; Malovannaya, Anna; Lanz, Rainer B.; Ma'ayan, Avi

    2011-01-01

    Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/. PMID:22219718

  4. In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

    PubMed

    Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques; Ferreira, Rafaela Salgado; Silva, Artur; Gromiha, Michael; Ghosh, Preetam; Barh, Debmalya; Azevedo, Vasco; Röttger, Richard

    2016-11-04

    Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

  5. PCPPI: a comprehensive database for the prediction of Penicillium-crop protein-protein interactions.

    PubMed

    Yue, Junyang; Zhang, Danfeng; Ban, Rongjun; Ma, Xiaojing; Chen, Danyang; Li, Guangwei; Liu, Jia; Wisniewski, Michael; Droby, Samir; Liu, Yongsheng

    2017-01-01

    Penicillium expansum , the causal agent of blue mold, is one of the most prevalent post-harvest pathogens, infecting a wide range of crops after harvest. In response, crops have evolved various defense systems to protect themselves against this and other pathogens. Penicillium -crop interaction is a multifaceted process and mediated by pathogen- and host-derived proteins. Identification and characterization of the inter-species protein-protein interactions (PPIs) are fundamental to elucidating the molecular mechanisms underlying infection processes between P. expansum and plant crops. Here, we have developed PCPPI, the Penicillium -Crop Protein-Protein Interactions database, which is constructed based on the experimentally determined orthologous interactions in pathogen-plant systems and available domain-domain interactions (DDIs) in each PPI. Thus far, it stores information on 9911 proteins, 439 904 interactions and seven host species, including apple, kiwifruit, maize, pear, rice, strawberry and tomato. Further analysis through the gene ontology (GO) annotation indicated that proteins with more interacting partners tend to execute the essential function. Significantly, semantic statistics of the GO terms also provided strong support for the accuracy of our predicted interactions in PCPPI. We believe that all the PCPPI datasets are helpful to facilitate the study of pathogen-crop interactions and freely available to the research community. : http://bdg.hfut.edu.cn/pcppi/index.html. © The Author(s) 2017. Published by Oxford University Press.

  6. 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.

  7. 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

  8. Protein profile and protein interaction network of Moniliophthora perniciosa basidiospores.

    PubMed

    Mares, Joise Hander; Gramacho, Karina Peres; Dos Santos, Everton Cruz; Santiago, André da Silva; Silva, Edson Mário de Andrade; Alvim, Fátima Cerqueira; Pirovani, Carlos Priminho

    2016-06-24

    Witches' broom, a disease caused by the basidiomycete Moniliophthora perniciosa, is considered to be the most important disease of the cocoa crop in Bahia, an area in the Brazilian Amazon, and also in the other countries where it is found. M. perniciosa germ tubes may penetrate into the host through intact or natural openings in the cuticle surface, in epidermis cell junctions, at the base of trichomes, or through the stomata. Despite its relevance to the fungal life cycle, basidiospore biology has not been extensively investigated. In this study, our goal was to optimize techniques for producing basidiospores for protein extraction, and to produce the first proteomics analysis map of ungerminated basidiospores. We then presented a protein interaction network by using Ustilago maydis as a model. The average pileus area ranged from 17.35 to 211.24 mm(2). The minimum and maximum productivity were 23,200 and 6,666,667 basidiospores per basidiome, respectively. The protein yield in micrograms per million basidiospores were approximately 0.161; 2.307, and 3.582 for germination times of 0, 2, and 4 h after germination, respectively. A total of 178 proteins were identified through mass spectrometry. These proteins were classified according to their molecular function and their involvement in biological processes such as cellular energy production, oxidative metabolism, stress, protein synthesis, and protein folding. Furthermore, to better understand the expression pattern, signaling, and interaction events of spore proteins, we presented an interaction network using orthologous proteins from Ustilago maydis as a model. Most of the orthologous proteins that were identified in this study were not clustered in the network, but several of them play a very important role in hypha development and branching. The quantities of basidiospores 7 × 10(9); 5.2 × 10(8), and 6.7 × 10(8) were sufficient to obtain enough protein mass for the three 2D-PAGE replicates, for

  9. A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces.

    PubMed

    Xu, David; Si, Yubing; Meroueh, Samy O

    2017-09-25

    The binding affinity of a protein-protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein-protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein-protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein-protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein-protein interactions do not optimally mimic protein-ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.

  10. Carbohydrate-Aromatic Interactions in Proteins.

    PubMed

    Hudson, Kieran L; Bartlett, Gail J; Diehl, Roger C; Agirre, Jon; Gallagher, Timothy; Kiessling, Laura L; Woolfson, Derek N

    2015-12-09

    Protein-carbohydrate interactions play pivotal roles in health and disease. However, defining and manipulating these interactions has been hindered by an incomplete understanding of the underlying fundamental forces. To elucidate common and discriminating features in carbohydrate recognition, we have analyzed quantitatively X-ray crystal structures of proteins with noncovalently bound carbohydrates. Within the carbohydrate-binding pockets, aliphatic hydrophobic residues are disfavored, whereas aromatic side chains are enriched. The greatest preference is for tryptophan with an increased prevalence of 9-fold. Variations in the spatial orientation of amino acids around different monosaccharides indicate specific carbohydrate C-H bonds interact preferentially with aromatic residues. These preferences are consistent with the electronic properties of both the carbohydrate C-H bonds and the aromatic residues. Those carbohydrates that present patches of electropositive saccharide C-H bonds engage more often in CH-π interactions involving electron-rich aromatic partners. These electronic effects are also manifested when carbohydrate-aromatic interactions are monitored in solution: NMR analysis indicates that indole favorably binds to electron-poor C-H bonds of model carbohydrates, and a clear linear free energy relationships with substituted indoles supports the importance of complementary electronic effects in driving protein-carbohydrate interactions. Together, our data indicate that electrostatic and electronic complementarity between carbohydrates and aromatic residues play key roles in driving protein-carbohydrate complexation. Moreover, these weak noncovalent interactions influence which saccharide residues bind to proteins, and how they are positioned within carbohydrate-binding sites.

  11. Nature and consequences of protein-protein interactions in high protein concentration solutions.

    PubMed

    Saluja, Atul; Kalonia, Devendra S

    2008-06-24

    High protein concentration solutions are becoming increasingly important in the pharmaceutical industry. The solution behavior of proteins at high concentrations can markedly differ from that predicted based on dilute solution analysis due to thermodynamic non-ideality in these solutions. The non-ideality observed in these systems is related to the protein-protein interactions (PPI). Different types of forces play a key role in determining the overall nature and extent of these PPI and their relative contributions are affected by solute and solvent properties. However, individual contributions of these forces to the solution properties of concentrated protein solutions are not fully understood. The role of PPI, driven by these intermolecular forces, in governing solution rheology and physical stability of high protein concentration solutions is discussed from the point of view of pharmaceutical product development. Investigation of protein self-association and aggregation in concentrated protein solutions is crucial for ensuring the safety and efficacy of the final product for the duration of the desired product shelf life. Understanding rheology of high concentration protein solutions is critical for addressing issues during product manufacture and administration of final formulation to the patient. To this end, analysis of solution viscoelastic character can also provide an insight into the nature of PPI affecting solution rheology.

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

    PubMed

    De Las Rivas, Javier; Fontanillo, Celia

    2012-11-01

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

  13. BioPlex Display: An Interactive Suite for Large-Scale AP-MS Protein-Protein Interaction Data.

    PubMed

    Schweppe, Devin K; Huttlin, Edward L; Harper, J Wade; Gygi, Steven P

    2018-01-05

    The development of large-scale data sets requires a new means to display and disseminate research studies to large audiences. Knowledge of protein-protein interaction (PPI) networks has become a principle interest of many groups within the field of proteomics. At the confluence of technologies, such as cross-linking mass spectrometry, yeast two-hybrid, protein cofractionation, and affinity purification mass spectrometry (AP-MS), detection of PPIs can uncover novel biological inferences at a high-throughput. Thus new platforms to provide community access to large data sets are necessary. To this end, we have developed a web application that enables exploration and dissemination of the growing BioPlex interaction network. BioPlex is a large-scale interactome data set based on AP-MS of baits from the human ORFeome. The latest BioPlex data set release (BioPlex 2.0) contains 56 553 interactions from 5891 AP-MS experiments. To improve community access to this vast compendium of interactions, we developed BioPlex Display, which integrates individual protein querying, access to empirical data, and on-the-fly annotation of networks within an easy-to-use and mobile web application. BioPlex Display enables rapid acquisition of data from BioPlex and development of hypotheses based on protein interactions.

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

    NASA Astrophysics Data System (ADS)

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

    2004-03-01

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

  15. NPIDB: Nucleic acid-Protein Interaction DataBase.

    PubMed

    Kirsanov, Dmitry D; Zanegina, Olga N; Aksianov, Evgeniy A; Spirin, Sergei A; Karyagina, Anna S; Alexeevski, Andrei V

    2013-01-01

    The Nucleic acid-Protein Interaction DataBase (http://npidb.belozersky.msu.ru/) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from the Protein Data Bank (3846 complexes in October 2012). It provides a web interface and a set of tools for extracting biologically meaningful characteristics of nucleoprotein complexes. The content of the database is updated weekly. The current version of the Nucleic acid-Protein Interaction DataBase is an upgrade of the version published in 2007. The improvements include a new web interface, new tools for calculation of intermolecular interactions, a classification of SCOP families that contains DNA-binding protein domains and data on conserved water molecules on the DNA-protein interface.

  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. Prediction of Protein-Protein Interactions by NanoLuc-Based Protein-Fragment Complementation Assay | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory has developed a new NanoLuc®-based protein-fragment complementation assay (NanoPCA) which allows the detection of novel protein-protein interactions (PPI). NanoPCA allows the study of PPI dynamics with reversible interactions.  Read the abstract. Experimental Approaches Read the detailed Experimetnal Approaches. 

  18. Kinase Pathway Database: An Integrated Protein-Kinase and NLP-Based Protein-Interaction Resource

    PubMed Central

    Koike, Asako; Kobayashi, Yoshiyuki; Takagi, Toshihisa

    2003-01-01

    Protein kinases play a crucial role in the regulation of cellular functions. Various kinds of information about these molecules are important for understanding signaling pathways and organism characteristics. We have developed the Kinase Pathway Database, an integrated database involving major completely sequenced eukaryotes. It contains the classification of protein kinases and their functional conservation, ortholog tables among species, protein–protein, protein–gene, and protein–compound interaction data, domain information, and structural information. It also provides an automatic pathway graphic image interface. The protein, gene, and compound interactions are automatically extracted from abstracts for all genes and proteins by natural-language processing (NLP).The method of automatic extraction uses phrase patterns and the GENA protein, gene, and compound name dictionary, which was developed by our group. With this database, pathways are easily compared among species using data with more than 47,000 protein interactions and protein kinase ortholog tables. The database is available for querying and browsing at http://kinasedb.ontology.ims.u-tokyo.ac.jp/. PMID:12799355

  19. High-Sensitivity Real-Time Imaging of Dual Protein-Protein Interactions in Living Subjects Using Multicolor Luciferases

    PubMed Central

    Hida, Naoki; Awais, Muhammad; Takeuchi, Masaki; Ueno, Naoto; Tashiro, Mayuri; Takagi, Chiyo; Singh, Tanuja; Hayashi, Makoto; Ohmiya, Yoshihiro; Ozawa, Takeaki

    2009-01-01

    Networks of protein-protein interactions play key roles in numerous important biological processes in living subjects. An effective methodology to assess protein-protein interactions in living cells of interest is protein-fragment complement assay (PCA). Particularly the assays using fluorescent proteins are powerful techniques, but they do not directly track interactions because of its irreversibility or the time for chromophore formation. By contrast, PCAs using bioluminescent proteins can overcome these drawbacks. We herein describe an imaging method for real-time analysis of protein-protein interactions using multicolor luciferases with different spectral characteristics. The sensitivity and signal-to-background ratio were improved considerably by developing a carboxy-terminal fragment engineered from a click beetle luciferase. We demonstrate its utility in spatiotemporal characterization of Smad1–Smad4 and Smad2–Smad4 interactions in early developing stages of a single living Xenopus laevis embryo. We also describe the value of this method by application of specific protein-protein interactions in cell cultures and living mice. This technique supports quantitative analyses and imaging of versatile protein-protein interactions with a selective luminescence wavelength in opaque or strongly auto-fluorescent living subjects. PMID:19536355

  20. Affinity purification combined with mass spectrometry to identify herpes simplex virus protein-protein interactions.

    PubMed

    Meckes, David G

    2014-01-01

    The identification and characterization of herpes simplex virus protein interaction complexes are fundamental to understanding the molecular mechanisms governing the replication and pathogenesis of the virus. Recent advances in affinity-based methods, mass spectrometry configurations, and bioinformatics tools have greatly increased the quantity and quality of protein-protein interaction datasets. In this chapter, detailed and reliable methods that can easily be implemented are presented for the identification of protein-protein interactions using cryogenic cell lysis, affinity purification, trypsin digestion, and mass spectrometry.

  1. Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology.

    PubMed

    DeBlasio, Stacy L; Chavez, Juan D; Alexander, Mariko M; Ramsey, John; Eng, Jimmy K; Mahoney, Jaclyn; Gray, Stewart M; Bruce, James E; Cilia, Michelle

    2016-02-15

    Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction

  2. Monitoring Protein–Protein Interactions Using Split Synthetic Renilla Luciferase Protein-Fragment-Assisted Complementation

    PubMed Central

    Paulmurugan, R.; Gambhir, S. S.

    2014-01-01

    In this study we developed an inducible synthetic renilla luciferase protein-fragment-assisted complementation-based bioluminescence assay to quantitatively measure real time protein–protein interactions in mammalian cells. We identified suitable sites to generate fragments of N and C portions of the protein that yield significant recovered activity through complementation. We validate complementation-based activation of split synthetic renilla luciferase protein driven by the interaction of two strongly interacting proteins, MyoD and Id, in five different cell lines utilizing transient transfection studies. The expression level of the system was also modulated by tumor necrosis factor α through NFκB-promoter/enhancer elements used to drive expression of the N portion of synthetic renilla luciferase reporter gene. This new system should help in studying protein–protein interactions and when used with other split reporters (e.g., split firefly luciferase) should help to monitor different components of an intracellular network. PMID:12705589

  3. Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data.

    PubMed

    Nourani, Esmaeil; Khunjush, Farshad; Durmuş, Saliha

    2016-05-24

    Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

  4. Noninvasive imaging of protein-protein interactions in living organisms.

    PubMed

    Haberkorn, Uwe; Altmann, Annette

    2003-06-01

    Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.

  5. Feature generation and representations for protein-protein interaction classification.

    PubMed

    Lan, Man; Tan, Chew Lim; Su, Jian

    2009-10-01

    Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

  6. Carbene footprinting accurately maps binding sites in protein-ligand and protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Manzi, Lucio; Barrow, Andrew S.; Scott, Daniel; Layfield, Robert; Wright, Timothy G.; Moses, John E.; Oldham, Neil J.

    2016-11-01

    Specific interactions between proteins and their binding partners are fundamental to life processes. The ability to detect protein complexes, and map their sites of binding, is crucial to understanding basic biology at the molecular level. Methods that employ sensitive analytical techniques such as mass spectrometry have the potential to provide valuable insights with very little material and on short time scales. Here we present a differential protein footprinting technique employing an efficient photo-activated probe for use with mass spectrometry. Using this methodology the location of a carbohydrate substrate was accurately mapped to the binding cleft of lysozyme, and in a more complex example, the interactions between a 100 kDa, multi-domain deubiquitinating enzyme, USP5 and a diubiquitin substrate were located to different functional domains. The much improved properties of this probe make carbene footprinting a viable method for rapid and accurate identification of protein binding sites utilizing benign, near-UV photoactivation.

  7. 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

  8. Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

    PubMed

    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.

  9. Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

    PubMed Central

    DeBlasio, Stacy L.; Chavez, Juan D.; Alexander, Mariko M.; Ramsey, John; Eng, Jimmy K.; Mahoney, Jaclyn; Gray, Stewart M.; Bruce, James E.

    2015-01-01

    ABSTRACT Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. IMPORTANCE The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used

  10. The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions.

    PubMed

    Yates, Christopher M; Sternberg, Michael J E

    2013-11-01

    Non-synonymous single nucleotide polymorphisms (nsSNPs) are single base changes leading to a change to the amino acid sequence of the encoded protein. Many of these variants are associated with disease, so nsSNPs have been well studied, with studies looking at the effects of nsSNPs on individual proteins, for example, on stability and enzyme active sites. In recent years, the impact of nsSNPs upon protein-protein interactions has also been investigated, giving a greater insight into the mechanisms by which nsSNPs can lead to disease. In this review, we summarize these studies, looking at the various mechanisms by which nsSNPs can affect protein-protein interactions. We focus on structural changes that can impair interaction, changes to disorder, gain of interaction, and post-translational modifications before looking at some examples of nsSNPs at human-pathogen protein-protein interfaces and the analysis of nsSNPs from a network perspective. © 2013.

  11. Conserved Cysteine Residues Provide a Protein-Protein Interaction Surface in Dual Oxidase (DUOX) Proteins*

    PubMed Central

    Meitzler, Jennifer L.; Hinde, Sara; Bánfi, Botond; Nauseef, William M.; Ortiz de Montellano, Paul R.

    2013-01-01

    Intramolecular disulfide bond formation is promoted in oxidizing extracellular and endoplasmic reticulum compartments and often contributes to protein stability and function. DUOX1 and DUOX2 are distinguished from other members of the NOX protein family by the presence of a unique extracellular N-terminal region. These peroxidase-like domains lack the conserved cysteines that confer structural stability to mammalian peroxidases. Sequence-based structure predictions suggest that the thiol groups present are solvent-exposed on a single protein surface and are too distant to support intramolecular disulfide bond formation. To investigate the role of these thiol residues, we introduced four individual cysteine to glycine mutations in the peroxidase-like domains of both human DUOXs and purified the recombinant proteins. The mutations caused little change in the stabilities of the monomeric proteins, supporting the hypothesis that the thiol residues are solvent-exposed and not involved in disulfide bonds that are critical for structural integrity. However, the ability of the isolated hDUOX1 peroxidase-like domain to dimerize was altered, suggesting a role for these cysteines in protein-protein interactions that could facilitate homodimerization of the peroxidase-like domain or, in the full-length protein, heterodimeric interactions with a maturation protein. When full-length hDUOX1 was expressed in HEK293 cells, the mutations resulted in decreased H2O2 production that correlated with a decreased amount of the enzyme localized to the membrane surface rather than with a loss of activity or with a failure to synthesize the mutant proteins. These results support a role for the cysteine residues in intermolecular disulfide bond formation with the DUOX maturation factor DUOXA1. PMID:23362256

  12. New partner proteins containing novel internal recognition motif for human Glutaminase Interacting Protein (hGIP)

    PubMed Central

    Zencir, Sevil; Banerjee, Monimoy; Dobson, Melanie J.; Ayaydin, Ferhan; Fodor, Elfrieda Ayaydin; Topcu, Zeki; Mohanty, Smita

    2013-01-01

    Regulation of gene expression in cells is mediated by protein-protein, DNA-protein and receptor-ligand interactions. PDZ (PSD-95/Discs-large/ZO-1) domains are protein–protein interaction modules. PDZ-containing proteins function in the organization of multi-protein complexes controlling spatial and temporal fidelity of intracellular signaling pathways. In general, PDZ proteins possess multiple domains facilitating distinct interactions. The human Glutaminase Interacting Protein (hGIP) is an unusual PDZ protein comprising entirely of a single PDZ domain and plays pivotal roles in many cellular processes through its interaction with the C-terminus of partner proteins. Here, we report the identification by yeast two-hybrid screening of two new hGIP-interacting partners, DTX1 and STAU1. Both proteins lack the typical C-terminal PDZ recognition motif but contain a novel internal hGIP recognition motif recently identified in a phage display library screen. Fluorescence resonance energy transfer and confocal microscopy analysis confirmed the in vivo association of hGIP with DTX1 and STAU1 in mammalian cells validating the previous discovery of S/T-X-V/L-D as a consensus internal motif for hGIP recognition. Similar to hGIP, DTX1 and STAU1 have been implicated in neuronal function. Identification of these new interacting partners furthers our understanding of GIP-regulated signaling cascades and these interactions may represent potential new drug targets in humans. PMID:23395680

  13. Two potato proteins, including a novel RING finger protein (HIP1), interact with the potyviral multifunctional protein HCpro.

    PubMed

    Guo, Deyin; Spetz, Carl; Saarma, Mart; Valkonen, Jari P T

    2003-05-01

    Potyviral helper-component proteinase (HCpro) is a multifunctional protein exerting its cellular functions in interaction with putative host proteins. In this study, cellular protein partners of the HCpro encoded by Potato virus A (PVA) (genus Potyvirus) were screened in a potato leaf cDNA library using a yeast two-hybrid system. Two cellular proteins were obtained that interact specifically with PVA HCpro in yeast and in the two in vitro binding assays used. Both proteins are encoded by single-copy genes in the potato genome. Analysis of the deduced amino acid sequences revealed that one (HIP1) of the two HCpro interactors is a novel RING finger protein. The sequence of the other protein (HIP2) showed no resemblance to the protein sequences available from databanks and has known biological functions.

  14. Modeling and simulating networks of interdependent protein interactions.

    PubMed

    Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

    2018-05-21

    Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package

  15. UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks.

    PubMed

    Peng, Wei; Wang, Jianxin; Cheng, Yingjiao; Lu, Yu; Wu, Fangxiang; Pan, Yi

    2015-01-01

    Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

  16. Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

    PubMed Central

    Wang, Li; Carnegie, Graeme K.

    2013-01-01

    Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells. BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction. PMID:23979513

  17. Flow cytometric analysis of bimolecular fluorescence complementation: a high throughput quantitative method to study protein-protein interaction.

    PubMed

    Wang, Li; Carnegie, Graeme K

    2013-08-15

    Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells. BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

  18. Functional structural motifs for protein-ligand, protein-protein, and protein-nucleic acid interactions and their connection to supersecondary structures.

    PubMed

    Kinjo, Akira R; Nakamura, Haruki

    2013-01-01

    Protein functions are mediated by interactions between proteins and other molecules. One useful approach to analyze protein functions is to compare and classify the structures of interaction interfaces of proteins. Here, we describe the procedures for compiling a database of interface structures and efficiently comparing the interface structures. To do so requires a good understanding of the data structures of the Protein Data Bank (PDB). Therefore, we also provide a detailed account of the PDB exchange dictionary necessary for extracting data that are relevant for analyzing interaction interfaces and secondary structures. We identify recurring structural motifs by classifying similar interface structures, and we define a coarse-grained representation of supersecondary structures (SSS) which represents a sequence of two or three secondary structure elements including their relative orientations as a string of four to seven letters. By examining the correspondence between structural motifs and SSS strings, we show that no SSS string has particularly high propensity to be found interaction interfaces in general, indicating any SSS can be used as a binding interface. When individual structural motifs are examined, there are some SSS strings that have high propensity for particular groups of structural motifs. In addition, it is shown that while the SSS strings found in particular structural motifs for nonpolymer and protein interfaces are as abundant as in other structural motifs that belong to the same subunit, structural motifs for nucleic acid interfaces exhibit somewhat stronger preference for SSS strings. In regard to protein folds, many motif-specific SSS strings were found across many folds, suggesting that SSS may be a useful description to investigate the universality of ligand binding modes.

  19. Protein-protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM.

    PubMed

    Sriwastava, Brijesh Kumar; Basu, Subhadip; Maulik, Ujjwal

    2015-10-01

    Protein-protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.

  20. Specificity and non-specificity in RNA–protein interactions

    PubMed Central

    Jankowsky, Eckhard; Harris, Michael E.

    2016-01-01

    Gene expression is regulated by complex networks of interactions between RNAs and proteins. Proteins that interact with RNA have been traditionally viewed as either specific or non-specific; specific proteins interact preferentially with defined RNA sequence or structure motifs, whereas non-specific proteins interact with RNA sites devoid of such characteristics. Recent studies indicate that the binary “specific vs. non-specific” classification is insufficient to describe the full spectrum of RNA–protein interactions. Here, we review new methods that enable quantitative measurements of protein binding to large numbers of RNA variants, and the concepts aimed as describing resulting binding spectra: affinity distributions, comprehensive binding models and free energy landscapes. We discuss how these new methodologies and associated concepts enable work towards inclusive, quantitative models for specific and non-specific RNA–protein interactions. PMID:26285679

  1. Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kay, B. K.; Castagnoli, L.; Biosciences Division

    This unit describes the process and analysis of affinity selecting bacteriophage M13 from libraries displaying combinatorial peptides fused to either a minor or major capsid protein. Direct affinity selection uses target protein bound to a microtiter plate followed by purification of selected phage by ELISA. Alternatively, there is a bead-based affinity selection method. These methods allow one to readily isolate peptide ligands that bind to a protein target of interest and use the consensus sequence to search proteomic databases for putative interacting proteins.

  2. Ion-dipole interactions and their functions in proteins.

    PubMed

    Sippel, Katherine H; Quiocho, Florante A

    2015-07-01

    Ion-dipole interactions in biological macromolecules are formed between atomic or molecular ions and neutral protein dipolar groups through either hydrogen bond or coordination. Since their discovery 30 years ago, these interactions have proven to be a frequent occurrence in protein structures, appearing in everything from transporters and ion channels to enzyme active sites to protein-protein interfaces. However, their significance and roles in protein functions are largely underappreciated. We performed PDB data mining to identify a sampling of proteins that possess these interactions. In this review, we will define the ion-dipole interaction and discuss several prominent examples of their functional roles in nature. © 2015 The Protein Society.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. Protein–protein interactions and selection: yeast-based approaches that exploit guanine nucleotide-binding protein signaling.

    PubMed

    Ishii, Jun; Fukuda, Nobuo; Tanaka, Tsutomu; Ogino, Chiaki; Kondo, Akihiko

    2010-05-01

    For elucidating protein–protein interactions, many methodologies have been developed during the past two decades. For investigation of interactions inside cells under physiological conditions, yeast is an attractive organism with which to quickly screen for hopeful candidates using versatile genetic technologies, and various types of approaches are now available.Among them, a variety of unique systems using the guanine nucleotide-binding protein (G-protein) signaling pathway in yeast have been established to investigate the interactions of proteins for biological study and pharmaceutical research. G-proteins involved in various cellular processes are mainly divided into two groups: small monomeric G-proteins,and heterotrimeric G-proteins. In this minireview, we summarize the basic principles and applications of yeast-based screening systems, using these two types of G-protein, which are typically used for elucidating biological protein interactions but are differentiated from traditional yeast two-hybrid systems.

  8. The simulation approach to lipid-protein interactions.

    PubMed

    Paramo, Teresa; Garzón, Diana; Holdbrook, Daniel A; Khalid, Syma; Bond, Peter J

    2013-01-01

    The interactions between lipids and proteins are crucial for a range of biological processes, from the folding and stability of membrane proteins to signaling and metabolism facilitated by lipid-binding proteins. However, high-resolution structural details concerning functional lipid/protein interactions are scarce due to barriers in both experimental isolation of native lipid-bound complexes and subsequent biophysical characterization. The molecular dynamics (MD) simulation approach provides a means to complement available structural data, yielding dynamic, structural, and thermodynamic data for a protein embedded within a physiologically realistic, modelled lipid environment. In this chapter, we provide a guide to current methods for setting up and running simulations of membrane proteins and soluble, lipid-binding proteins, using standard atomistically detailed representations, as well as simplified, coarse-grained models. In addition, we outline recent studies that illustrate the power of the simulation approach in the context of biologically relevant lipid/protein interactions.

  9. 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).

  10. 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

  11. A conserved NAD+ binding pocket that regulates protein-protein interactions during aging

    PubMed Central

    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.

    2017-01-01

    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. PMID:28336669

  12. Nanoparticle-Protein Interaction: The Significance and Role of Protein Corona.

    PubMed

    Ahsan, Saad Mohammad; Rao, Chintalagiri Mohan; Ahmad, Md Faiz

    2018-01-01

    The physico-chemical properties of nanoparticles, as characterized under idealized laboratory conditions, have been suggested to differ significantly when studied under complex physiological environments. A major reason for this variation has been the adsorption of biomolecules (mainly proteins) on the nanoparticle surface, constituting the so-called "biomolecular corona". The formation of biomolecular corona on the nanoparticle surface has been reported to influence various nanoparticle properties viz. cellular targeting, cellular interaction, in vivo clearance, toxicity, etc. Understanding the interaction of nanoparticles with proteins upon administration in vivo thus becomes important for the development of effective nanotechnology-based platforms for biomedical applications. In this chapter, we describe the formation of protein corona on nanoparticles and the differences arising in its composition due to variations in nanoparticle properties. Also discussed is the influence of protein corona on various nanoparticle activities.

  13. A simple and efficient method for predicting protein-protein interaction sites.

    PubMed

    Higa, R H; Tozzi, C L

    2008-09-23

    Computational methods for predicting protein-protein interaction sites based on structural data are characterized by an accuracy between 70 and 80%. Some experimental studies indicate that only a fraction of the residues, forming clusters in the center of the interaction site, are energetically important for binding. In addition, the analysis of amino acid composition has shown that residues located in the center of the interaction site can be better discriminated from the residues in other parts of the protein surface. In the present study, we implement a simple method to predict interaction site residues exploiting this fact and show that it achieves a very competitive performance compared to other methods using the same dataset and criteria for performance evaluation (success rate of 82.1%).

  14. PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

    NASA Astrophysics Data System (ADS)

    Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

    2011-06-01

    Physics approaches focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including protein-protein, protein-DNA, protein-RNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. In this issue

  15. 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

  16. Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nery, Flavia C.; Departamento de Genetica e Evolucao, Universidade Estadual de Campinas, Campinas, SP; Rui, Edmilson

    Ki-1/57 is a cytoplasmic and nuclear phospho-protein of 57 kDa and interacts with the adaptor protein RACK1, the transcription factor MEF2C, and the chromatin remodeling factor CHD3, suggesting that it might be involved in the regulation of transcription. Here, we describe yeast two-hybrid studies that identified a total of 11 proteins interacting with Ki-1/57, all of which interact or are functionally associated with p53 or other members of the p53 family of proteins. We further found that Ki-1/57 is able to interact with p53 itself in the yeast two-hybrid system when the interaction was tested directly. This interaction could bemore » confirmed by pull down assays with purified proteins in vitro and by reciprocal co-immunoprecipitation assays from the human Hodgkin analogous lymphoma cell line L540. Furthermore, we found that the phosphorylation of p53 by PKC abolishes its interaction with Ki-1/57 in vitro.« less

  17. Cotton and Protein Interactions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Goheen, Steven C.; Edwards, J. V.; Rayburn, Alfred R.

    The adsorbent properties of important wound fluid proteins and cotton cellulose are reviewed. This review focuses on the adsorption of albumin to cotton-based wound dressings and some chemically modified derivatives targeted for chronic wounds. Adsorption of elastase in the presence of albumin was examined as a model to understand the interactive properties of these wound fluid components with cotton fibers. In the chronic non-healing wound, elastase appears to be over-expressed, and it digests tissue and growth factors, interfering with the normal healing process. Albumin is the most prevalent protein in wound fluid, and in highly to moderately exudative wounds, itmore » may bind significantly to the fibers of wound dressings. Thus, the relative binding properties of both elastase and albumin to wound dressing fibers are of interest in the design of more effective wound dressings. The present work examines the binding of albumin to two different derivatives of cotton, and quantifies the elastase binding to the same derivatives following exposure of albumin to the fiber surface. An HPLC adsorption technique was employed coupled with a colorimetric enzyme assay to quantify the relative binding properties of albumin and elastase to cotton. The results of wound protein binding are discussed in relation to the porosity and surface chemistry interactions of cotton and wound proteins. Studies are directed to understanding the implications of protein adsorption phenomena in terms of fiber-protein models that have implications for rationally designing dressings for chronic wounds.« less

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

    PubMed

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    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 Elsevier Ltd. All rights reserved.

  19. The interactions of peripheral membrane proteins with biological membranes

    DOE PAGES

    Johs, Alexander; Whited, A. M.

    2015-07-29

    The interactions of peripheral proteins with membrane surfaces are critical to many biological processes, including signaling, recognition, membrane trafficking, cell division and cell structure. On a molecular level, peripheral membrane proteins can modulate lipid composition, membrane dynamics and protein-protein interactions. Biochemical and biophysical studies have shown that these interactions are in fact highly complex, dominated by several different types of interactions, and have an interdependent effect on both the protein and membrane. Here we examine three major mechanisms underlying the interactions between peripheral membrane proteins and membranes: electrostatic interactions, hydrophobic interactions, and fatty acid modification of proteins. While experimental approachesmore » continue to provide critical insights into specific interaction mechanisms, emerging bioinformatics resources and tools contribute to a systems-level picture of protein-lipid interactions. Through these recent advances, we begin to understand the pivotal role of protein-lipid interactions underlying complex biological functions at membrane interfaces.« less

  20. Single Molecule Approaches in RNA-Protein Interactions.

    PubMed

    Serebrov, Victor; Moore, Melissa J

    RNA-protein interactions govern every aspect of RNA metabolism, and aberrant RNA-binding proteins are the cause of hundreds of genetic diseases. Quantitative measurements of these interactions are necessary in order to understand mechanisms leading to diseases and to develop efficient therapies. Existing methods of RNA-protein interactome capture can afford a comprehensive snapshot of RNA-protein interaction networks but lack the ability to characterize the dynamics of these interactions. As all ensemble methods, their resolution is also limited by statistical averaging. Here we discuss recent advances in single molecule techniques that have the potential to tackle these challenges. We also provide a thorough overview of single molecule colocalization microscopy and the essential protein and RNA tagging and detection techniques.

  1. ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.

    PubMed

    Frenkel-Morgenstern, Milana; Gorohovski, Alessandro; Tagore, Somnath; Sekar, Vaishnovi; Vazquez, Miguel; Valencia, Alfonso

    2017-07-07

    Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. A rice kinase-protein interaction map.

    PubMed

    Ding, Xiaodong; Richter, Todd; Chen, Mei; Fujii, Hiroaki; Seo, Young Su; Xie, Mingtang; Zheng, Xianwu; Kanrar, Siddhartha; Stevenson, Rebecca A; Dardick, Christopher; Li, Ying; Jiang, Hao; Zhang, Yan; Yu, Fahong; Bartley, Laura E; Chern, Mawsheng; Bart, Rebecca; Chen, Xiuhua; Zhu, Lihuang; Farmerie, William G; Gribskov, Michael; Zhu, Jian-Kang; Fromm, Michael E; Ronald, Pamela C; Song, Wen-Yuan

    2009-03-01

    Plants uniquely contain large numbers of protein kinases, and for the vast majority of the 1,429 kinases predicted in the rice (Oryza sativa) genome, little is known of their functions. Genetic approaches often fail to produce observable phenotypes; thus, new strategies are needed to delineate kinase function. We previously developed a cost-effective high-throughput yeast two-hybrid system. Using this system, we have generated a protein interaction map of 116 representative rice kinases and 254 of their interacting proteins. Overall, the resulting interaction map supports a large number of known or predicted kinase-protein interactions from both plants and animals and reveals many new functional insights. Notably, we found a potential widespread role for E3 ubiquitin ligases in pathogen defense signaling mediated by receptor-like kinases, particularly by the kinases that may have evolved from recently expanded kinase subfamilies in rice. We anticipate that the data provided here will serve as a foundation for targeted functional studies in rice and other plants. The application of yeast two-hybrid and TAPtag analyses for large-scale plant protein interaction studies is also discussed.

  3. Protein-Phospholipid Interactions in Nonclassical Protein Secretion: Problem and Methods of Study

    PubMed Central

    Prudovsky, Igor; Kumar, Thallapuranam Krishnaswamy Suresh; Sterling, Sarah; Neivandt, David

    2013-01-01

    Extracellular proteins devoid of signal peptides use nonclassical secretion mechanisms for their export. These mechanisms are independent of the endoplasmic reticulum and Golgi. Some nonclassically released proteins, particularly fibroblast growth factors (FGF) 1 and 2, are exported as a result of their direct translocation through the cell membrane. This process requires specific interactions of released proteins with membrane phospholipids. In this review written by a cell biologist, a structural biologist and two membrane engineers, we discuss the following subjects: (i) Phenomenon of nonclassical protein release and its biological significance; (ii) Composition of the FGF1 multiprotein release complex (MRC); (iii) The relationship between FGF1 export and acidic phospholipid externalization; (iv) Interactions of FGF1 MRC components with acidic phospholipids; (v) Methods to study the transmembrane translocation of proteins; (vi) Membrane models to study nonclassical protein release. PMID:23396106

  4. 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. Copyright © 2017, American Association for the Advancement of Science.

  5. TSEMA: interactive prediction of protein pairings between interacting families

    PubMed Central

    Izarzugaza, José M. G.; Juan, David; Pons, Carles; Ranea, Juan A. G.; Valencia, Alfonso; Pazos, Florencio

    2006-01-01

    An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at . Standalone versions of the software behind the interface are available upon request from the authors. PMID:16845017

  6. Patterns of protein–protein interactions in salt solutions and implications for protein crystallization

    PubMed Central

    Dumetz, André C.; Snellinger-O'Brien, Ann M.; Kaler, Eric W.; Lenhoff, Abraham M.

    2007-01-01

    The second osmotic virial coefficients of seven proteins—ovalbumin, ribonuclease A, bovine serum albumin, α-lactalbumin, myoglobin, cytochrome c, and catalase—were measured in salt solutions. Comparison of the interaction trends in terms of the dimensionless second virial coefficient b2 shows that, at low salt concentrations, protein–protein interactions can be either attractive or repulsive, possibly due to the anisotropy of the protein charge distribution. At high salt concentrations, the behavior depends on the salt: In sodium chloride, protein interactions generally show little salt dependence up to very high salt concentrations, whereas in ammonium sulfate, proteins show a sharp drop in b2 with increasing salt concentration beyond a particular threshold. The experimental phase behavior of the proteins corroborates these observations in that precipitation always follows the drop in b2. When the proteins crystallize, they do so at slightly lower salt concentrations than seen for precipitation. The b2 measurements were extended to other salts for ovalbumin and catalase. The trends follow the Hofmeister series, and the effect of the salt can be interpreted as a water-mediated effect between the protein and salt molecules. The b2 trends quantify protein–protein interactions and provide some understanding of the corresponding phase behavior. The results explain both why ammonium sulfate is among the best crystallization agents, as well as some of the difficulties that can be encountered in protein crystallization. PMID:17766383

  7. Contribution of Hydrophobic Interactions to Protein Stability

    PubMed Central

    Pace, C. Nick; Fu, Hailong; Fryar, Katrina Lee; Landua, John; Trevino, Saul R.; Shirley, Bret A.; Hendricks, Marsha McNutt; Iimura, Satoshi; Gajiwala, Ketan; Scholtz, J. Martin; Grimsley, Gerald R.

    2011-01-01

    Our goal was to gain a better understanding of the contribution of hydrophobic interactions to protein stability. We measured the change in conformational stability, Δ(ΔG), for hydrophobic mutants of four proteins: villin head piece subdomain (VHP) with 36 residues, a surface protein from Borrelia burgdorferi (VlsE) with 341 residues, and two proteins previously studied in our laboratory, ribonucleases Sa and T1. We compare our results with previous studies and reach the following conclusions. 1. Hydrophobic interactions contribute less to the stability of a small protein, VHP (0.6 ± 0.3 kcal/mole per –CH2– group), than to the stability of a large protein, VlsE (1.6 ± 0.3 kcal/mol per –CH2– group). 2. Hydrophobic interactions make the major contribution to the stability of VHP (40 kcal/mol) and the major contributors are (in kcal/mol): Phe 18 (3.9), Met 13 (3.1), Phe 7 (2.9), Phe 11 (2.7), and Leu 21 (2.7). 3. Based on Δ(ΔG) values for 148 hydrophobic mutants in 13 proteins, burying a –CH2– group on folding contributes, on average, 1.1 ± 0.5 kcal/mol to protein stability. 4. The experimental Δ(ΔG) values for aliphatic side chains (Ala, Val, Ile, and Leu) are in good agreement with their ΔGtr values from water to cyclohexane. 5. For 22 proteins with 36 to 534 residues, hydrophobic interactions contribute 60 ± 4% and hydrogen bonds 40 ± 4% to protein stability. 6. Conformational entropy contributes about 2.4 kcal/mol per residue to protein instability. The globular conformation of proteins is stabilized predominately by hydrophobic interactions. PMID:21377472

  8. Single-well monitoring of protein-protein interaction and phosphorylation-dephosphorylation events.

    PubMed

    Arcand, Mathieu; Roby, Philippe; Bossé, Roger; Lipari, Francesco; Padrós, Jaime; Beaudet, Lucille; Marcil, Alexandre; Dahan, Sophie

    2010-04-20

    We combined oxygen channeling assays with two distinct chemiluminescent beads to detect simultaneously protein phosphorylation and interaction events that are usually monitored separately. This novel method was tested in the ERK1/2 MAP kinase pathway. It was first used to directly monitor dissociation of MAP kinase ERK2 from MEK1 upon phosphorylation and to evaluate MAP kinase phosphatase (MKP) selectivity and mechanism of action. In addition, MEK1 and ERK2 were probed with an ATP competitor and an allosteric MEK1 inhibitor, which generated distinct phosphorylation-interaction patterns. Simultaneous monitoring of protein-protein interactions and substrate phosphorylation can provide significant mechanistic insight into enzyme activity and small molecule action.

  9. Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

    PubMed

    Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

    2015-01-16

    Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

  10. Protein Interaction Profile Sequencing (PIP-seq).

    PubMed

    Foley, Shawn W; Gregory, Brian D

    2016-10-10

    Every eukaryotic RNA transcript undergoes extensive post-transcriptional processing from the moment of transcription up through degradation. This regulation is performed by a distinct cohort of RNA-binding proteins which recognize their target transcript by both its primary sequence and secondary structure. Here, we describe protein interaction profile sequencing (PIP-seq), a technique that uses ribonuclease-based footprinting followed by high-throughput sequencing to globally assess both protein-bound RNA sequences and RNA secondary structure. PIP-seq utilizes single- and double-stranded RNA-specific nucleases in the absence of proteins to infer RNA secondary structure. These libraries are also compared to samples that undergo nuclease digestion in the presence of proteins in order to find enriched protein-bound sequences. Combined, these four libraries provide a comprehensive, transcriptome-wide view of RNA secondary structure and RNA protein interaction sites from a single experimental technique. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

  11. Novel fusion protein approach for efficient high-throughput screening of small molecule-mediating protein-protein interactions in cells and living animals.

    PubMed

    Paulmurugan, Ramasamy; Gambhir, Sanjiv S

    2005-08-15

    Networks of protein interactions execute many different intracellular pathways. Small molecules either synthesized within the cell or obtained from the external environment mediate many of these protein-protein interactions. The study of these small molecule-mediated protein-protein interactions is important in understanding abnormal signal transduction pathways in a variety of disorders, as well as in optimizing the process of drug development and validation. In this study, we evaluated the rapamycin-mediated interaction of the human proteins FK506-binding protein (FKBP12) rapamycin-binding domain (FRB) and FKBP12 by constructing a fusion of these proteins with a split-Renilla luciferase or a split enhanced green fluorescent protein (split-EGFP) such that complementation of the reporter fragments occurs in the presence of rapamycin. Different linker peptides in the fusion protein were evaluated for the efficient maintenance of complemented reporter activity. This system was studied in both cell culture and xenografts in living animals. We found that peptide linkers with two or four EAAAR repeat showed higher protein-protein interaction-mediated signal with lower background signal compared with having no linker or linkers with amino acid sequences GGGGSGGGGS, ACGSLSCGSF, and ACGSLSCGSFACGSLSCGSF. A 9 +/- 2-fold increase in signal intensity both in cell culture and in living mice was seen compared with a system that expresses both reporter fragments and the interacting proteins separately. In this fusion system, rapamycin induced heterodimerization of the FRB and FKBP12 moieties occurred rapidly even at very lower concentrations (0.00001 nmol/L) of rapamycin. For a similar fusion system employing split-EGFP, flow cytometry analysis showed significant level of rapamycin-induced complementation.

  12. Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

    PubMed

    Yu, Jingkai; Finley, Russell L

    2009-01-01

    High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space. We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

  13. Viscosity Analysis of Dual Variable Domain Immunoglobulin Protein Solutions: Role of Size, Electroviscous Effect and Protein-Protein Interactions.

    PubMed

    Raut, Ashlesha S; Kalonia, Devendra S

    2016-01-01

    Increased solution viscosity results in difficulties in manufacturing and delivery of therapeutic protein formulations, increasing both the time and production costs, and leading to patient inconvenience. The solution viscosity is affected by the molecular properties of both the solute and the solvent. The purpose of this work was to investigate the effect of size, charge and protein-protein interactions on the viscosity of Dual Variable Domain Immunoglobulin (DVD-Ig(TM)) protein solutions. The effect of size of the protein molecule on solution viscosity was investigated by measuring intrinsic viscosity and excluded volume calculations for monoclonal antibody (mAb) and DVD-Ig(TM) protein solutions. The role of the electrostatic charge resulting in electroviscous effects for DVD-Ig(TM) protein was assessed by measuring zeta potential. Light scattering measurements were performed to detect protein-protein interactions affecting solution viscosity. DVD-Ig(TM) protein exhibited significantly higher viscosity compared to mAb. Intrinsic viscosity and excluded volume calculations indicated that the size of the molecule affects viscosity significantly at higher concentrations, while the effect was minimal at intermediate concentrations. Electroviscous contribution to the viscosity of DVD-Ig(TM) protein varied depending on the presence or absence of ions in the solution. In buffered solutions, negative k D and B 2 values indicated the presence of attractive interactions which resulted in high viscosity for DVD-Ig(TM) protein at certain pH and ionic strength conditions. Results show that more than one factor contributes to the increased viscosity of DVD-Ig(TM) protein and interplay of these factors modulates the overall viscosity behavior of the solution, especially at higher concentrations.

  14. Coarse-Grained Model for Colloidal Protein Interactions, B22, and Protein Cluster Formation

    PubMed Central

    Blanco, Marco A.; Sahin, Eric; Robinson, Anne S.; Roberts, Christopher J.

    2014-01-01

    Reversible protein cluster formation is an important initial step in the processes of native and non-native protein aggregation, but involves relatively long time and length scales for detailed atomistic simulations and extensive mapping of free energy landscapes. A coarse-grained (CG) model is presented to semi-quantitatively characterize the thermodynamics and key configurations involved in the landscape for protein oligomerization, as well as experimental measures of interactions such as the osmotic second virial coefficient (B22). Based on earlier work, this CG model treats proteins as rigid bodies composed of one bead per amino acid, with each amino acid having specific parameters for its size, hydrophobicity, and charge. The net interactions are a combination of steric repulsions, short-range attractions, and screened long-range charge-charge interactions. Model parametrization was done by fitting simulation results against experimental values of the B22 as a function of solution ionic strength for α-chymotrypsinogen A and γD-crystallin (gD-Crys). The CG model is applied to characterize the pairwise interactions and dimerization of gD-Crys and the dependance on temperature, protein concentration, and ionic strength. The results illustrate that at experimentally relevant conditions where stable dimers do not form, the entropic contributions are predominant in the free-energy of protein cluster formation and colloidal protein interactions, arguing against interpretations that treat B22 primarily from energetic considerations alone. Additionally, the results suggest that electrostatic interactions help to modulate the population of the different stable configurations for protein nearest-neighbor pairs, while short-range attractions determine the relative orientations of proteins within these configurations. Finally, simulation results are combined with Principal Component Analysis to identify those amino-acids / surface patches that form inter-protein contacts

  15. A lanthipeptide library used to identify a protein-protein interaction inhibitor.

    PubMed

    Yang, Xiao; Lennard, Katherine R; He, Chang; Walker, Mark C; Ball, Andrew T; Doigneaux, Cyrielle; Tavassoli, Ali; van der Donk, Wilfred A

    2018-04-01

    In this article we describe the production and screening of a genetically encoded library of 10 6 lanthipeptides in Escherichia coli using the substrate-tolerant lanthipeptide synthetase ProcM. This plasmid-encoded library was combined with a bacterial reverse two-hybrid system for the interaction of the HIV p6 protein with the UEV domain of the human TSG101 protein, which is a critical protein-protein interaction for HIV budding from infected cells. Using this approach, we identified an inhibitor of this interaction from the lanthipeptide library, whose activity was verified in vitro and in cell-based virus-like particle-budding assays. Given the variety of lanthipeptide backbone scaffolds that may be produced with ProcM, this method may be used for the generation of genetically encoded libraries of natural product-like lanthipeptides containing substantial structural diversity. Such libraries may be combined with any cell-based assay to identify lanthipeptides with new biological activities.

  16. Computational design of protein interactions: designing proteins that neutralize influenza by inhibiting its hemagglutinin surface protein

    NASA Astrophysics Data System (ADS)

    Fleishman, Sarel

    2012-02-01

    Molecular recognition underlies all life processes. Design of interactions not seen in nature is a test of our understanding of molecular recognition and could unlock the vast potential of subtle control over molecular interaction networks, allowing the design of novel diagnostics and therapeutics for basic and applied research. We developed the first general method for designing protein interactions. The method starts by computing a region of high affinity interactions between dismembered amino acid residues and the target surface and then identifying proteins that can harbor these residues. Designs are tested experimentally for binding the target surface and successful ones are affinity matured using yeast cell surface display. Applied to the conserved stem region of influenza hemagglutinin we designed two unrelated proteins that, following affinity maturation, bound hemagglutinin at subnanomolar dissociation constants. Co-crystal structures of hemagglutinin bound to the two designed binders were within 1Angstrom RMSd of their models, validating the accuracy of the design strategy. One of the designed proteins inhibits the conformational changes that underlie hemagglutinin's cell-invasion functions and blocks virus infectivity in cell culture, suggesting that such proteins may in future serve as diagnostics and antivirals against a wide range of pathogenic influenza strains. We have used this method to obtain experimentally validated binders of several other target proteins, demonstrating the generality of the approach. We discuss the combination of modeling and high-throughput characterization of design variants which has been key to the success of this approach, as well as how we have used the data obtained in this project to enhance our understanding of molecular recognition. References: Science 332:816 JMB, in press Protein Sci 20:753

  17. 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

  18. 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

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

    PubMed Central

    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. PMID:28691014

  20. 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

  1. Identification of host proteins, Spata3 and Dkk2, interacting with Toxoplasma gondii micronemal protein MIC3.

    PubMed

    Wang, Yifan; Fang, Rui; Yuan, Yuan; Pan, Ming; Hu, Min; Zhou, Yanqin; Shen, Bang; Zhao, Junlong

    2016-07-01

    As an obligate intracellular protozoan, Toxoplasma gondii is a successful pathogen infecting a variety of animals, including humans. As an adhesin involving in host invasion, the micronemal protein MIC3 plays important roles in host cell attachment, as well as modulation of host EGFR signaling cascade. However, the specific host proteins that interact with MIC3 are unknown and the identification of such proteins will increase our understanding of how MIC3 exerts its functions. This study was designed to identify host proteins interacting with MIC3 by yeast two-hybrid screens. Using MIC3 as bait, a library expressing mouse proteins was screened, uncovering eight mouse proteins that showed positive interactions with MIC3. Two of which, spermatogenesis-associated protein 3 (Spata3) and dickkopf-related protein 2 (Dkk2), were further confirmed to interact with MIC3 by additional protein-protein interaction tests. The results also revealed that the tandem repeat EGF domains of MIC3 were critical in mediating the interactions with the identified host proteins. This is the first study to show that MIC3 interacts with host proteins that are involved in reproduction, growth, and development. The results will provide a clearer understanding of the functions of adhesion-associated micronemal proteins in T. gondii.

  2. Protein conformational modifications and kinetics of water-protein interactions in milk protein concentrate powder upon aging: effect on solubility.

    PubMed

    Haque, Enamul; Bhandari, Bhesh R; Gidley, Michael J; Deeth, Hilton C; Møller, Sandie M; Whittaker, Andrew K

    2010-07-14

    Protein conformational modifications and water-protein interactions are two major factors believed to induce instability of protein and eventually affect the solubility of milk protein concentrate (MPC) powder. To test these hypotheses, MPC was stored at different water activities (a(w) 0.0-0.85) and temperatures (25 and 45 degrees C) for up to 12 weeks. Samples were examined periodically to determine solubility, change in protein conformation by Fourier transform infrared (FTIR) spectroscopy and principal component analysis (PCA), and water status (interaction of water with the protein molecule/surface) by measuring the transverse relaxation time (T(2)) with proton nuclear magnetic resonance ((1)H NMR). The solubility of MPC decreased significantly with aging, and this process was enhanced by increasing water activity (a(w)) and storage temperature. Minor changes in protein secondary structure were observed with FTIR, which indicated some degree of unfolding of protein molecules. PCA of the FTIR data was able to discriminate samples according to moisture content and storage period. Partial least-squares (PLS) analysis showed some correlation between FTIR spectral feature and solubility. The NMR T(2) results indicated the presence of three distinct populations of water molecules, and the proton signal intensity and T(2) values of proton fractions varied with storage conditions (humidity, temperature) and aging. Results suggest that protein/protein interactions may be initiated by unfolding of protein molecules that eventually affects solubility.

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

    PubMed

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

    2015-05-15

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

  4. Hepatitis B virus X protein modulates peroxisome proliferator-activated receptor gamma through protein-protein interaction.

    PubMed

    Choi, Youn-Hee; Kim, Ha-il; Seong, Je Kyung; Yu, Dae-Yeul; Cho, Hyeseong; Lee, Mi-Ock; Lee, Jae Myun; Ahn, Yong-ho; Kim, Se Jong; Park, Jeon Han

    2004-01-16

    Ligand activation of peroxisome proliferator-activated receptor gamma (PPARgamma) has been reported to induce growth inhibition and apoptosis in various cancers including hepatocellular carcinoma (HCC). However, the effect of hepatitis B virus X protein (HBx) on PPARgamma activation has not been characterized in hepatitis B virus (HBV)-associated HCC. Herein, we demonstrated that HBx counteracted growth inhibition caused by PPARgamma ligand in HBx-associated HCC cells. We found that HBx bound to DNA binding domain of PPARgamma and HBx/PPARgamma interaction blocked nuclear localization and binding to recognition site of PPARgamma. HBx significantly suppressed a PPARgamma-mediated transactivation. These results suggest that HBx modulates PPARgamma function through protein-protein interaction.

  5. Website on Protein Interaction and Protein Structure Related Work

    NASA Technical Reports Server (NTRS)

    Samanta, Manoj; Liang, Shoudan; Biegel, Bryan (Technical Monitor)

    2003-01-01

    In today's world, three seemingly diverse fields - computer information technology, nanotechnology and biotechnology are joining forces to enlarge our scientific knowledge and solve complex technological problems. Our group is dedicated to conduct theoretical research exploring the challenges in this area. The major areas of research include: 1) Yeast Protein Interactions; 2) Protein Structures; and 3) Current Transport through Small Molecules.

  6. Hydrophobic Interaction Chromatography for Bottom-Up Proteomics Analysis of Single Proteins and Protein Complexes.

    PubMed

    Rackiewicz, Michal; Große-Hovest, Ludger; Alpert, Andrew J; Zarei, Mostafa; Dengjel, Jörn

    2017-06-02

    Hydrophobic interaction chromatography (HIC) is a robust standard analytical method to purify proteins while preserving their biological activity. It is widely used to study post-translational modifications of proteins and drug-protein interactions. In the current manuscript we employed HIC to separate proteins, followed by bottom-up LC-MS/MS experiments. We used this approach to fractionate antibody species followed by comprehensive peptide mapping as well as to study protein complexes in human cells. HIC-reversed-phase chromatography (RPC)-mass spectrometry (MS) is a powerful alternative to fractionate proteins for bottom-up proteomics experiments making use of their distinct hydrophobic properties.

  7. Dynamics, Conformational Entropy, and Frustration in Protein-Protein Interactions Involving an Intrinsically Disordered Protein Domain.

    PubMed

    Lindström, Ida; Dogan, Jakob

    2018-05-18

    Intrinsically disordered proteins (IDPs) are abundant in the eukaryotic proteome. However, little is known about the role of subnanosecond dynamics and the conformational entropy that it represents in protein-protein interactions involving IDPs. Using nuclear magnetic resonance side chain and backbone relaxation, stopped-flow kinetics, isothermal titration calorimetry, and computational studies, we have characterized the interaction between the globular TAZ1 domain of the CREB binding protein and the intrinsically disordered transactivation domain of STAT2 (TAD-STAT2). We show that the TAZ1/TAD-STAT2 complex retains considerable subnanosecond motions, with TAD-STAT2 undergoing only a partial disorder-to-order transition. We report here the first experimental determination of the conformational entropy change for both binding partners in an IDP binding interaction and find that the total change even exceeds in magnitude the binding enthalpy and is comparable to the contribution from the hydrophobic effect, demonstrating its importance in the binding energetics. Furthermore, we show that the conformational entropy change for TAZ1 is also instrumental in maintaining a biologically meaningful binding affinity. Strikingly, a spatial clustering of very high amplitude motions and a cluster of more rigid sites in the complex exist, which through computational studies we found to overlap with regions that experience energetic frustration and are less frustrated, respectively. Thus, the residual dynamics in the bound state could be necessary for faster dissociation, which is important for proteins that interact with multiple binding partners.

  8. Phosphorylation of Tat-interactive protein 60 kDa by protein kinase C epsilon is important for its subcellular localisation.

    PubMed

    Sapountzi, Vasileia; Logan, Ian R; Nelson, Glyn; Cook, Susan; Robson, Craig N

    2008-01-01

    Tat-interactive protein 60 kDa is a nuclear acetyltransferase that both coactivates and corepresses transcription factors and has a definitive function in the DNA damage response. Here, we provide evidence that Tat-interactive protein 60 kDa is phosphorylated by protein kinase C epsilon. In vitro, protein kinase C epsilon phosphorylates Tat-interactive protein 60 kDa on at least two sites within the acetyltransferase domain. In whole cells, activation of protein kinase C increases the levels of phosphorylated Tat-interactive protein 60 kDa and the interaction of Tat-interactive protein 60 kDa with protein kinase C epsilon. A phosphomimetic mutant Tat-interactive protein 60 kDa has distinct subcellular localisation compared to the wild-type protein in whole cells. Taken together, these findings suggest that the protein kinase C epsilon phosphorylation sites on Tat-interactive protein 60 kDa are important for its subcellular localisation. Regulation of the subcellular localisation of Tat-interactive protein 60 kDa via phosphorylation provides a novel means of controlling Tat-interactive protein 60 kDa function.

  9. 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.

  10. Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

    PubMed

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

    2012-06-21

    Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

  11. A Three-Hybrid System to Probe In Vivo Protein-Protein Interactions: Application to the Essential Proteins of the RD1 Complex of M. tuberculosis

    PubMed Central

    Bhalla, Kuhulika; Ghosh, Anamika; Kumar, Krishan; Kumar, Sushil; Ranganathan, Anand

    2011-01-01

    Background Protein-protein interactions play a crucial role in enabling a pathogen to survive within a host. In many cases the interactions involve a complex of proteins rather than just two given proteins. This is especially true for pathogens like M. tuberculosis that are able to successfully survive the inhospitable environment of the macrophage. Studying such interactions in detail may help in developing small molecules that either disrupt or augment the interactions. Here, we describe the development of an E. coli based bacterial three-hybrid system that can be used effectively to study ternary protein complexes. Methodology/Principal Findings The protein-protein interactions involved in M. tuberculosis pathogenesis have been used as a model for the validation of the three-hybrid system. Using the M. tuberculosis RD1 encoded proteins CFP10, ESAT6 and Rv3871 for our proof-of-concept studies, we show that the interaction between the proteins CFP10 and Rv3871 is strengthened and stabilized in the presence of ESAT6, the known heterodimeric partner of CFP10. Isolating peptide candidates that can disrupt crucial protein-protein interactions is another application that the system offers. We demonstrate this by using CFP10 protein as a disruptor of a previously established interaction between ESAT6 and a small peptide HCL1; at the same time we also show that CFP10 is not able to disrupt the strong interaction between ESAT6 and another peptide SL3. Conclusions/Significance The validation of the three-hybrid system paves the way for finding new peptides that are stronger binders of ESAT6 compared even to its natural partner CFP10. Additionally, we believe that the system offers an opportunity to study tri-protein complexes and also perform a screening of protein/peptide binders to known interacting proteins so as to elucidate novel tri-protein complexes. PMID:22087330

  12. Relevance Rank Platform (RRP) for Functional Filtering of High Content Protein-Protein Interaction Data.

    PubMed

    Pokharel, Yuba Raj; Saarela, Jani; Szwajda, Agnieszka; Rupp, Christian; Rokka, Anne; Lal Kumar Karna, Shibendra; Teittinen, Kaisa; Corthals, Garry; Kallioniemi, Olli; Wennerberg, Krister; Aittokallio, Tero; Westermarck, Jukka

    2015-12-01

    High content protein interaction screens have revolutionized our understanding of protein complex assembly. However, one of the major challenges in translation of high content protein interaction data is identification of those interactions that are functionally relevant for a particular biological question. To address this challenge, we developed a relevance ranking platform (RRP), which consist of modular functional and bioinformatic filters to provide relevance rank among the interactome proteins. We demonstrate the versatility of RRP to enable a systematic prioritization of the most relevant interaction partners from high content data, highlighted by the analysis of cancer relevant protein interactions for oncoproteins Pin1 and PME-1. We validated the importance of selected interactions by demonstration of PTOV1 and CSKN2B as novel regulators of Pin1 target c-Jun phosphorylation and reveal previously unknown interacting proteins that may mediate PME-1 effects via PP2A-inhibition. The RRP framework is modular and can be modified to answer versatile research problems depending on the nature of the biological question under study. Based on comparison of RRP to other existing filtering tools, the presented data indicate that RRP offers added value especially for the analysis of interacting proteins for which there is no sufficient prior knowledge available. Finally, we encourage the use of RRP in combination with either SAINT or CRAPome computational tools for selecting the candidate interactors that fulfill the both important requirements, functional relevance, and high confidence interaction detection. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

  13. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Concentration Dependent Ion-Protein Interaction Patterns Underlying Protein Oligomerization Behaviours

    NASA Astrophysics Data System (ADS)

    Batoulis, Helena; Schmidt, Thomas H.; Weber, Pascal; Schloetel, Jan-Gero; Kandt, Christian; Lang, Thorsten

    2016-04-01

    Salts and proteins comprise two of the basic molecular components of biological materials. Kosmotropic/chaotropic co-solvation and matching ion water affinities explain basic ionic effects on protein aggregation observed in simple solutions. However, it is unclear how these theories apply to proteins in complex biological environments and what the underlying ionic binding patterns are. Using the positive ion Ca2+ and the negatively charged membrane protein SNAP25, we studied ion effects on protein oligomerization in solution, in native membranes and in molecular dynamics (MD) simulations. We find that concentration-dependent ion-induced protein oligomerization is a fundamental chemico-physical principle applying not only to soluble but also to membrane-anchored proteins in their native environment. Oligomerization is driven by the interaction of Ca2+ ions with the carboxylate groups of aspartate and glutamate. From low up to middle concentrations, salt bridges between Ca2+ ions and two or more protein residues lead to increasingly larger oligomers, while at high concentrations oligomers disperse due to overcharging effects. The insights provide a conceptual framework at the interface of physics, chemistry and biology to explain binding of ions to charged protein surfaces on an atomistic scale, as occurring during protein solubilisation, aggregation and oligomerization both in simple solutions and membrane systems.

  15. Toward a molecular understanding of nanoparticle-protein interactions.

    PubMed

    Treuel, Lennart; Nienhaus, Gerd Ulrich

    2012-06-01

    Wherever nanoparticles (NPs) come in contact with a living organism, physical and chemical interactions take place between the surfaces of the NPs and biomatter, in particular proteins. When NP are exposed to biological fluids, an adsorption layer of proteins, a "protein corona" forms around the NPs. Consequently, living systems interact with the protein-coated NP rather than with a bare NP. To anticipate biological responses to NPs, we thus require comprehensive knowledge of the interactions at the bio-nano interface. In recent years, a wide variety of biophysical techniques have been employed to elucidate mechanistic aspects of NP-protein interactions. In this brief review, we present the latest findings regarding the composition of the protein corona as it forms on NPs in the blood stream. We also discuss molecular aspects of this adsorption layer and its time evolution. The current state of knowledge is summarized, and issues that still need to be addressed to further advance our understanding of NP-protein interactions are identified.

  16. Nuclear Protein Sam68 Interacts with the Enterovirus 71 Internal Ribosome Entry Site and Positively Regulates Viral Protein Translation.

    PubMed

    Zhang, Hua; Song, Lei; Cong, Haolong; Tien, Po

    2015-10-01

    Enterovirus 71 (EV71) recruits various cellular factors to assist in the replication and translation of its genome. Identification of the host factors involved in the EV71 life cycle not only will enable a better understanding of the infection mechanism but also has the potential to be of use in the development of antiviral therapeutics. In this study, we demonstrated that the cellular factor 68-kDa Src-associated protein in mitosis (Sam68) acts as an internal ribosome entry site (IRES) trans-acting factor (ITAF) that binds specifically to the EV71 5' untranslated region (5'UTR). Interaction sites in both the viral IRES (stem-loops IV and V) and the heterogeneous nuclear ribonucleoprotein K homology (KH) domain of Sam68 protein were further mapped using an electrophoretic mobility shift assay (EMSA) and biotin RNA pulldown assay. More importantly, dual-luciferase (firefly) reporter analysis suggested that overexpression of Sam68 positively regulated IRES-dependent translation of virus proteins. In contrast, both IRES activity and viral protein translation significantly decreased in Sam68 knockdown cells compared with the negative-control cells treated with short hairpin RNA (shRNA). However, downregulation of Sam68 did not have a significant inhibitory effect on the accumulation of the EV71 genome. Moreover, Sam68 was redistributed from the nucleus to the cytoplasm and interacts with cellular factors, such as poly(rC)-binding protein 2 (PCBP2) and poly(A)-binding protein (PABP), during EV71 infection. The cytoplasmic relocalization of Sam68 in EV71-infected cells may be involved in the enhancement of EV71 IRES-mediated translation. Since Sam68 is known to be a RNA-binding protein, these results provide direct evidence that Sam68 is a novel ITAF that interacts with EV71 IRES and positively regulates viral protein translation. The nuclear protein Sam68 is found as an additional new host factor that interacts with the EV71 IRES during infection and could potentially

  17. Course 1: Physics of Protein-DNA Interaction

    NASA Astrophysics Data System (ADS)

    Bruinsma, R. F.

    1 Introduction 1.1 The central dogma and bacterial gene expression 1.2 Molecular structure 2 Thermodynamics and kinetics of repressor-DNA interaction 2.1 Thermodynamics and the lac repressor 2.2 Kinetics of repressor-DNA interaction 3 DNA deformability and protein-DNA interaction 3.1 Introduction 3.2 The worm-like chain 3.3 The RST model 4 Electrostatics in water and protein-DNA interaction 4.1 Macro-ions and aqueous electrostatics 4.2 The primitive model 4.3 Manning condensation 4.4 Counter-ion release and non-specific protein-DNA interaction

  18. Novel Fusion Protein Approach for Efficient High-Throughput Screening of Small Molecule–Mediating Protein-Protein Interactions in Cells and Living Animals

    PubMed Central

    Paulmurugan, Ramasamy; Gambhir, Sanjiv S.

    2014-01-01

    Networks of protein interactions execute many different intracellular pathways. Small molecules either synthesized within the cell or obtained from the external environment mediate many of these protein-protein interactions. The study of these small molecule–mediated protein-protein interactions is important in understanding abnormal signal transduction pathways in a variety of disorders, as well as in optimizing the process of drug development and validation. In this study, we evaluated the rapamycin-mediated interaction of the human proteins FK506-binding protein (FKBP12) rapamycin-binding domain (FRB) and FKBP12 by constructing a fusion of these proteins with a split-Renilla luciferase or a split enhanced green fluorescent protein (split-EGFP) such that complementation of the reporter fragments occurs in the presence of rapamycin. Different linker peptides in the fusion protein were evaluated for the efficient maintenance of complemented reporter activity. This system was studied in both cell culture and xenografts in living animals. We found that peptide linkers with two or four EAAAR repeat showed higher protein-protein interaction–mediated signal with lower background signal compared with having no linker or linkers with amino acid sequences GGGGSGGGGS, ACGSLSCGSF, and ACGSLSCGS-FACGSLSCGSF. A 9 ± 2-fold increase in signal intensity both in cell culture and in living mice was seen compared with a system that expresses both reporter fragments and the interacting proteins separately. In this fusion system, rapamycin induced heterodimerization of the FRB and FKBP12 moieties occurred rapidly even at very lower concentrations (0.00001 nmol/L) of rapamycin. For a similar fusion system employing split-EGFP, flow cytometry analysis showed significant level of rapamycin-induced complementation. PMID:16103094

  19. Emory University: Prediction of Protein-Protein Interactions by NanoLuc-Based Protein-Fragment Complementation Assay | Office of Cancer Genomics

    Cancer.gov

    The CTD2 Center at Emory has developed a new NanoLuc®-based protein-fragment complementation assay (NanoPCA) which allows the detection of novel protein-protein interactions (PPI). NanoPCA allows the study of PPI dynamics with reversible interactions.  Read the abstract. Experimental Approaches Read the detailed Experimetnal Approaches. 

  20. Energetics of protein-DNA interactions.

    PubMed

    Donald, Jason E; Chen, William W; Shakhnovich, Eugene I

    2007-01-01

    Protein-DNA interactions are vital for many processes in living cells, especially transcriptional regulation and DNA modification. To further our understanding of these important processes on the microscopic level, it is necessary that theoretical models describe the macromolecular interaction energetics accurately. While several methods have been proposed, there has not been a careful comparison of how well the different methods are able to predict biologically important quantities such as the correct DNA binding sequence, total binding free energy and free energy changes caused by DNA mutation. In addition to carrying out the comparison, we present two important theoretical models developed initially in protein folding that have not yet been tried on protein-DNA interactions. In the process, we find that the results of these knowledge-based potentials show a strong dependence on the interaction distance and the derivation method. Finally, we present a knowledge-based potential that gives comparable or superior results to the best of the other methods, including the molecular mechanics force field AMBER99.

  1. Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation.

    PubMed

    Blanco, Marco A; Sahin, Erinc; Robinson, Anne S; Roberts, Christopher J

    2013-12-19

    Reversible protein cluster formation is an important initial step in the processes of native and non-native protein aggregation, but involves relatively long time and length scales for detailed atomistic simulations and extensive mapping of free energy landscapes. A coarse-grained (CG) model is presented to semiquantitatively characterize the thermodynamics and key configurations involved in the landscape for protein oligomerization, as well as experimental measures of interactions such as the osmotic second virial coefficient (B22). Based on earlier work (Grüenberger et al., J. Phys. Chem. B 2013, 117, 763), this CG model treats proteins as rigid bodies composed of one bead per amino acid, with each amino acid having specific parameters for its size, hydrophobicity, and charge. The net interactions are a combination of steric repulsions, short-range attractions, and screened long-range charge-charge interactions. Model parametrization was done by fitting simulation results against experimental value of B22 as a function of solution ionic strength for α-chymotrypsinogen A and γD-Crystallin (gD-Crys). The CG model is applied to characterize the pairwise interactions and dimerization of gD-Crys and the dependence on temperature, protein concentration, and ionic strength. The results illustrate that at experimentally relevant conditions where stable dimers do not form, the entropic contributions are predominant in the free-energy of protein cluster formation and colloidal protein interactions, arguing against interpretations that treat B22 primarily from energetic considerations alone. Additionally, the results suggest that electrostatic interactions help to modulate the population of the different stable configurations for protein nearest-neighbor pairs, while short-range attractions determine the relative orientations of proteins within these configurations. Finally, simulation results are combined with Principal Component Analysis to identify those amino

  2. Thioredoxin-interacting protein regulates protein disulfide isomerases and endoplasmic reticulum stress.

    PubMed

    Lee, Samuel; Min Kim, Soo; Dotimas, James; Li, Letitia; Feener, Edward P; Baldus, Stephan; Myers, Ronald B; Chutkow, William A; Patwari, Parth; Yoshioka, Jun; Lee, Richard T

    2014-06-01

    The endoplasmic reticulum (ER) is responsible for protein folding, modification, and trafficking. Accumulation of unfolded or misfolded proteins represents the condition of ER stress and triggers the unfolded protein response (UPR), a key mechanism linking supply of excess nutrients to insulin resistance and type 2 diabetes in obesity. The ER harbors proteins that participate in protein folding including protein disulfide isomerases (PDIs). Changes in PDI activity are associated with protein misfolding and ER stress. Here, we show that thioredoxin-interacting protein (Txnip), a member of the arrestin protein superfamily and one of the most strongly induced proteins in diabetic patients, regulates PDI activity and UPR signaling. We found that Txnip binds to PDIs and increases their enzymatic activity. Genetic deletion of Txnip in cells and mice led to increased protein ubiquitination and splicing of the UPR regulated transcription factor X-box-binding protein 1 (Xbp1s) at baseline as well as under ER stress. Our results reveal Txnip as a novel direct regulator of PDI activity and a feedback mechanism of UPR signaling to decrease ER stress. © 2014 Brigham and Women's Hospital. Published under the terms of the CC BY 4.0 license.

  3. Studying Protein-Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry.

    PubMed

    Xie, Zhongqiu; Jia, Yuemeng; Li, Hui

    2017-01-01

    The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

  4. The role of protein-protein interactions in the intracellular traffic of the potassium channels TASK-1 and TASK-3.

    PubMed

    Kilisch, Markus; Lytovchenko, Olga; Schwappach, Blanche; Renigunta, Vijay; Daut, Jürgen

    2015-05-01

    The intracellular transport of membrane proteins is controlled by trafficking signals: Short peptide motifs that mediate the contact with COPI, COPII or various clathrin-associated coat proteins. In addition, many membrane proteins interact with accessory proteins that are involved in the sorting of these proteins to different intracellular compartments. In the K2P channels, TASK-1 and TASK-3, the influence of protein-protein interactions on sorting decisions has been studied in some detail. Both TASK paralogues interact with the adaptor protein 14-3-3; TASK-1 interacts, in addition, with the adaptor protein p11 (S100A10) and the endosomal SNARE protein syntaxin-8. The role of these interacting proteins in controlling the intracellular traffic of the channels and the underlying molecular mechanisms are summarised in this review. In the case of 14-3-3, the interacting protein masks a retention signal in the C-terminus of the channel; in the case of p11, the interacting protein carries a retention signal that localises the channel to the endoplasmic reticulum; and in the case of syntaxin-8, the interacting protein carries an endocytosis signal that complements an endocytosis signal of the channel. These examples illustrate some of the mechanisms by which interacting proteins may determine the itinerary of a membrane protein within a cell and suggest that the intracellular traffic of membrane proteins may be adapted to the specific functions of that protein by multiple protein-protein interactions.

  5. PLIP: fully automated protein-ligand interaction profiler.

    PubMed

    Salentin, Sebastian; Schreiber, Sven; Haupt, V Joachim; Adasme, Melissa F; Schroeder, Michael

    2015-07-01

    The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  6. General M13 phage display: M13 phage display in identification and characterization of protein-protein interactions.

    PubMed

    Hertveldt, Kirsten; Beliën, Tim; Volckaert, Guido

    2009-01-01

    In M13 phage display, proteins and peptides are exposed on one of the surface proteins of filamentous phage particles and become accessible to affinity enrichment against a bait of interest. We describe the construction of fragmented whole genome and gene fragment phage display libraries and interaction selection by panning. This strategy allows the identification and characterization of interacting proteins on a genomic scale by screening the fragmented "proteome" against protein baits. Gene fragment libraries allow a more in depth characterization of the protein-protein interaction site by identification of the protein region involved in the interaction.

  7. New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size.

    PubMed

    Sambourg, Laure; Thierry-Mieg, Nicolas

    2010-12-21

    As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required. We examined the yeast interactome from a new perspective, by taking into account how thoroughly proteins have been studied. We discovered that the set of literature-curated protein-protein interactions is qualitatively different when restricted to proteins that have received extensive attention from the scientific community. In particular, these interactions are less often supported by yeast two-hybrid, and more often by more complex experiments such as biochemical activity assays. Our analysis showed that high-throughput and literature-curated interactome datasets are more correlated than commonly assumed, but that this bias can be corrected for by focusing on well-studied proteins. We thus propose a simple and reliable method to estimate the size of an interactome, combining literature-curated data involving well-studied proteins with high-throughput data. It yields an estimate of at least 37, 600 direct physical protein-protein interactions in S. cerevisiae. Our method leads to higher and more accurate estimates of the interactome size, as it accounts for interactions that are genuine yet difficult to detect with commonly-used experimental assays. This shows that we are even further from completing the yeast interactome map than previously expected.

  8. How Structure Defines Affinity in Protein-Protein Interactions

    PubMed Central

    Erijman, Ariel; Rosenthal, Eran; Shifman, Julia M.

    2014-01-01

    Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins. PMID:25329579

  9. INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species.

    PubMed

    Kwon, Daehong; Lee, Daehwan; Kim, Juyeon; Lee, Jongin; Sim, Mikang; Kim, Jaebum

    2018-05-09

    Proteins perform biological functions through cascading interactions with each other by forming protein complexes. As a result, interactions among proteins, called protein-protein interactions (PPIs) are not completely free from selection constraint during evolution. Therefore, the identification and analysis of PPI changes during evolution can give us new insight into the evolution of functions. Although many algorithms, databases and websites have been developed to help the study of PPIs, most of them are limited to visualize the structure and features of PPIs in a chosen single species with limited functions in the visualization perspective. This leads to difficulties in the identification of different patterns of PPIs in different species and their functional consequences. To resolve these issues, we developed a web application, called INTER-Species Protein Interaction Analysis (INTERSPIA). Given a set of proteins of user's interest, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins and searches for different patterns of PPIs in multiple species through a server-side pipeline, and second visualizes the dynamics of PPIs in multiple species using an easy-to-use web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/.

  10. Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

    PubMed

    Nariai, N; Kim, S; Imoto, S; Miyano, S

    2004-01-01

    We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

  11. Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

    NASA Astrophysics Data System (ADS)

    He, Yi-Ming; Ma, Bin-Guang

    2016-05-01

    Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions.

  12. Interaction of AIP with protein kinase A (cAMP-dependent protein kinase).

    PubMed

    Schernthaner-Reiter, Marie Helene; Trivellin, Giampaolo; Stratakis, Constantine A

    2018-05-02

    Germline mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene cause mostly somatotropinomas and/or prolactinomas in a subset of familial isolated pituitary adenomas (FIPA). AIP has been shown to interact with phosphodiesterases (PDEs) and G proteins, suggesting a link to the cyclic AMP (cAMP)-dependent protein kinase (PKA) pathway. Upregulation of PKA is seen in sporadic somatotropinomas that carry GNAS1 mutations, and those in Carney complex that are due to PRKAR1A mutations. To elucidate the mechanism of AIP-dependent pituitary tumorigenesis, we studied potential functional and physical interactions of AIP with PKA's main subunits PRKAR1A (R1α) and PRKACA (Cα). We found that AIP physically interacts with both R1α and Cα; this interaction is enhanced when all three components are present, but maintained during Cα-R1α dissociation by PKA pathway activation, indicating that AIP binds Cα/R1α both in complex and separately. The interaction between AIP and R1α/Cα is reduced when the frequent AIP pathogenic mutation p.R304* is present. AIP protein levels are regulated both by translation and the ubiquitin/proteasome pathway and Cα stabilizes both AIP and R1α protein levels. AIP reduction by siRNA leads to an increase of PKA pathway activity, which is disproportionately enhanced during PDE4-inhibition. We show that AIP interacts with the PKA pathway on multiple levels, including a physical interaction with both the main regulatory (R1α) and catalytic (Cα) PKA subunits and a functional interaction with PDE4-dependent PKA activation. These findings provide novel insights on the mechanisms of AIP-dependent pituitary tumorigenesis.

  13. Trimeric transmembrane domain interactions in paramyxovirus fusion proteins: roles in protein folding, stability, and function.

    PubMed

    Smith, Everett Clinton; Smith, Stacy E; Carter, James R; Webb, Stacy R; Gibson, Kathleen M; Hellman, Lance M; Fried, Michael G; Dutch, Rebecca Ellis

    2013-12-13

    Paramyxovirus fusion (F) proteins promote membrane fusion between the viral envelope and host cell membranes, a critical early step in viral infection. Although mutational analyses have indicated that transmembrane (TM) domain residues can affect folding or function of viral fusion proteins, direct analysis of TM-TM interactions has proved challenging. To directly assess TM interactions, the oligomeric state of purified chimeric proteins containing the Staphylococcal nuclease (SN) protein linked to the TM segments from three paramyxovirus F proteins was analyzed by sedimentation equilibrium analysis in detergent and buffer conditions that allowed density matching. A monomer-trimer equilibrium best fit was found for all three SN-TM constructs tested, and similar fits were obtained with peptides corresponding to just the TM region of two different paramyxovirus F proteins. These findings demonstrate for the first time that class I viral fusion protein TM domains can self-associate as trimeric complexes in the absence of the rest of the protein. Glycine residues have been implicated in TM helix interactions, so the effect of mutations at Hendra F Gly-508 was assessed in the context of the whole F protein. Mutations G508I or G508L resulted in decreased cell surface expression of the fusogenic form, consistent with decreased stability of the prefusion form of the protein. Sedimentation equilibrium analysis of TM domains containing these mutations gave higher relative association constants, suggesting altered TM-TM interactions. Overall, these results suggest that trimeric TM interactions are important driving forces for protein folding, stability and membrane fusion promotion.

  14. A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

    PubMed

    Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

    2014-01-21

    Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  15. Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

    PubMed

    Bauer, Katharina Christin; Hämmerling, Frank; Kittelmann, Jörg; Dürr, Cathrin; Görlich, Fabian; Hubbuch, Jürgen

    2017-04-01

    Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  16. Single Molecule Study of Metalloregulatory Protein-DNA Interactions

    NASA Astrophysics Data System (ADS)

    Sarkar, Susanta; Benitez, Jaime; Huang, Zhengxi; Wang, Qi; Chen, Peng

    2007-03-01

    Control of metal concentrations is essential for living body. Metalloregulatory proteins respond to metal concentrations by regulating transcriptions of metal resistance genes via protein-DNA interactions. It is thus necessary to understand interactions of metalloregulatory proteins with DNA. Ensemble measurements provide average behavior of a vast number of biomolecules. In contrast, single molecule spectroscopy can track single molecules individually and elucidate dynamics of processes of short time scales and intermediate structures not revealed by ensemble measurements. Here we present single molecule study of interactions between PbrR691, a MerR-family metalloregulatory protein and DNA. We presume that the dynamics of protein/DNA conformational changes and interactions are important for the transcription regulation and kinetics of these dynamic processes can provide useful information about the mechanisms of these metalloregulatory proteins.

  17. CellMap visualizes protein-protein interactions and subcellular localization

    PubMed Central

    Dallago, Christian; Goldberg, Tatyana; Andrade-Navarro, Miguel Angel; Alanis-Lobato, Gregorio; Rost, Burkhard

    2018-01-01

    Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers. PMID:29497493

  18. A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

    PubMed

    Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F

    2017-05-25

    Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

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

    PubMed

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

    2011-11-01

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

  20. The calcium-sensing receptor and its interacting proteins

    PubMed Central

    Huang, Chunfa; Miller, R Tyler

    2007-01-01

    Abstract Seven membrane-spanning, or G protein-coupled receptors were originally thought to act through het-erotrimeric G proteins that in turn activate intracellular enzymes or ion channels, creating relatively simple, linear signalling pathways. Although this basic model remains true in that this family does act via a relatively small number of G proteins, these signalling systems are considerably more complex because the receptors interact with or are located near additional proteins that are often unique to a receptor or subset of receptors. These additional proteins give receptors their unique signalling ‘personalities’. The extracellular Ca-sensing receptor (CaR) signals via Gαi, Gαq and Gα12/13, but its effects in vivo demonstrate that the signalling pathways controlled by these subunits are not sufficient to explain all its biologic effects. Additional structural or signalling proteins that interact with the CaR may explain its behaviour more fully. Although the CaR is less well studied in this respect than other receptors, several CaR-interacting proteins such as filamin, a potential scaffolding protein, receptor activity modifying proteins (RAMPs) and potassium channels may contribute to the unique characteristics of the CaR. The CaR also appears to interact with additional proteins common to other G protein-coupled receptors such as arrestins, G protein receptor kinases, protein kinase C, caveolin and proteins in the ubiquitination pathway. These proteins probably represent a few initial members of CaR-based signalling complex. These and other proteins may not all be associated with the CaR in all tissues, but they form the basis for understanding the complete nature of CaR signalling. PMID:17979874

  1. An Interactive Introduction to Protein Structure

    ERIC Educational Resources Information Center

    Lee, W. Theodore

    2004-01-01

    To improve student understanding of protein structure and the significance of noncovalent interactions in protein structure and function, students are assigned a project to write a paper complemented with computer-generated images. The assignment provides an opportunity for students to select a protein structure that is of interest and detail…

  2. Structure and expression of a novel compact myelin protein – Small VCP-interacting protein (SVIP)

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Wu, Jiawen; Peng, Dungeng; Voehler, Markus

    2013-10-11

    Highlights: •SVIP (small p97/VCP-interacting protein) co-localizes with myelin basic protein (MBP) in compact myelin. •We determined that SVIP is an intrinsically disordered protein (IDP). •The helical content of SVIP increases dramatically during its interaction with negatively charged lipid membrane. •This study provides structural insight into interactions between SVIP and myelin membranes. -- Abstract: SVIP (small p97/VCP-interacting protein) was initially identified as one of many cofactors regulating the valosin containing protein (VCP), an AAA+ ATPase involved in endoplasmic-reticulum-associated protein degradation (ERAD). Our previous study showed that SVIP is expressed exclusively in the nervous system. In the present study, SVIP and VCPmore » were seen to be co-localized in neuronal cell bodies. Interestingly, we also observed that SVIP co-localizes with myelin basic protein (MBP) in compact myelin, where VCP was absent. Furthermore, using nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopic measurements, we determined that SVIP is an intrinsically disordered protein (IDP). However, upon binding to the surface of membranes containing a net negative charge, the helical content of SVIP increases dramatically. These findings provide structural insight into interactions between SVIP and myelin membranes.« less

  3. Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

    PubMed Central

    Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

    2012-01-01

    Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126

  4. Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution

    PubMed Central

    He, Yi-Ming; Ma, Bin-Guang

    2016-01-01

    Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions. PMID:27220911

  5. Femtosecond UV-laser pulses to unveil protein-protein interactions in living cells.

    PubMed

    Itri, Francesco; Monti, Daria M; Della Ventura, Bartolomeo; Vinciguerra, Roberto; Chino, Marco; Gesuele, Felice; Lombardi, Angelina; Velotta, Raffaele; Altucci, Carlo; Birolo, Leila; Piccoli, Renata; Arciello, Angela

    2016-02-01

    A hallmark to decipher bioprocesses is to characterize protein-protein interactions in living cells. To do this, the development of innovative methodologies, which do not alter proteins and their natural environment, is particularly needed. Here, we report a method (LUCK, Laser UV Cross-linKing) to in vivo cross-link proteins by UV-laser irradiation of living cells. Upon irradiation of HeLa cells under controlled conditions, cross-linked products of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were detected, whose yield was found to be a linear function of the total irradiation energy. We demonstrated that stable dimers of GAPDH were formed through intersubunit cross-linking, as also observed when the pure protein was irradiated by UV-laser in vitro. We proposed a defined patch of aromatic residues located at the enzyme subunit interface as the cross-linking sites involved in dimer formation. Hence, by this technique, UV-laser is able to photofix protein surfaces that come in direct contact. Due to the ultra-short time scale of UV-laser-induced cross-linking, this technique could be extended to weld even transient protein interactions in their native context.

  6. Protein interactions in concentrated ribonuclease solutions

    NASA Astrophysics Data System (ADS)

    Boyer, Mireille; Roy, Marie-Odile; Jullien, Magali; Bonneté, Françoise; Tardieu, Annette

    1999-01-01

    To investigate the protein interactions involved in the crystallization process of ribonuclease A, dynamic light scattering (DLS) and small angle X-ray scattering experiments (SAXS) were performed on concentrated solutions. Whereas the translational diffusion coefficient obtained from DLS is sensitive to thermodynamic and hydrodynamic interactions and permits to calculate an interaction parameter, the shape of the SAXS curves is related to the type of interaction (attractive or repulsive). We compared the effect of pH on protein interactions in the case of two types of crystallizing agents: a mixture of salts (3 M sodium chloride plus 0.2 M ammonium sulfate) and an organic solvent (ethanol). The results show that in the presence of ethanol, as in low salt, protein interactions become more attractive as the pH increases from 4 to 8 and approaches the isoelectric point. In contrast, a reverse effect is observed in high salt conditions: the strength of attractive interactions decreases as the pH increases. The range of the pH effect can be related to ionization of histidine residues, particularly those located in the active site of the protein. The present observations point out the important role played by localized charges in crystallization conditions, whatever the precipitating agent.

  7. RAIN: RNA–protein Association and Interaction Networks

    PubMed Central

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

    2017-01-01

    Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) as well. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA–RNA and ncRNA–protein interactions and its integration with the STRING database of protein–protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literature mining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessible web interface and all interaction data can be downloaded. Database URL: http://rth.dk/resources/rain PMID:28077569

  8. Protein-surface interactions on stimuli-responsive polymeric biomaterials.

    PubMed

    Cross, Michael C; Toomey, Ryan G; Gallant, Nathan D

    2016-03-04

    Responsive surfaces: a review of the dependence of protein adsorption on the reversible volume phase transition in stimuli-responsive polymers. Specifically addressed are a widely studied subset: thermoresponsive polymers. Findings are also generalizable to other materials which undergo a similarly reversible volume phase transition. As of 2015, over 100,000 articles have been published on stimuli-responsive polymers and many more on protein-biomaterial interactions. Significantly, fewer than 100 of these have focused specifically on protein interactions with stimuli-responsive polymers. These report a clear trend of increased protein adsorption in the collapsed state compared to the swollen state. This control over protein interactions makes stimuli-responsive polymers highly useful in biomedical applications such as wound repair scaffolds, on-demand drug delivery, and antifouling surfaces. Outstanding questions are whether the protein adsorption is reversible with the volume phase transition and whether there is a time-dependence. A clear understanding of protein interactions with stimuli-responsive polymers will advance theoretical models, experimental results, and biomedical applications.

  9. 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

  10. An ensemble framework for clustering protein-protein interaction networks.

    PubMed

    Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

    2007-07-01

    Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

  11. 3D model for Cancerous Inhibitor of Protein Phosphatase 2A armadillo domain unveils highly conserved protein-protein interaction characteristics.

    PubMed

    Dahlström, Käthe M; Salminen, Tiina A

    2015-12-07

    Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) is a human oncoprotein, which exerts its cancer-promoting function through interaction with other proteins, for example Protein Phosphatase 2A (PP2A) and MYC. The lack of structural information for CIP2A significantly prevents the design of anti-cancer therapeutics targeting this protein. In an attempt to counteract this fact, we modeled the three-dimensional structure of the N-terminal domain (CIP2A-ArmRP), analyzed key areas and amino acids, and coupled the results to the existing literature. The model reliably shows a stable armadillo repeat fold with a positively charged groove. The fact that this conserved groove highly likely binds peptides is corroborated by the presence of a conserved polar ladder, which is essential for the proper peptide-binding mode of armadillo repeat proteins and, according to our results, several known CIP2A interaction partners appropriately possess an ArmRP-binding consensus motif. Moreover, we show that Arg229Gln, which has been linked to the development of cancer, causes a significant change in charge and surface properties of CIP2A-ArmRP. In conclusion, our results reveal that CIP2A-ArmRP shares the typical fold, protein-protein interaction site and interaction patterns with other natural armadillo proteins and that, presumably, several interaction partners bind into the central groove of the modeled CIP2A-ArmRP. By providing essential structural characteristics of CIP2A, the present study significantly increases our knowledge on how CIP2A interacts with other proteins in cancer progression and how to develop new therapeutics targeting CIP2A. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. Using an Engineered Protein Model to Constrain Protein-Mineral Interactions

    NASA Astrophysics Data System (ADS)

    Chacon, S. S.; Reardon, P. N.; Washton, N.; Kleber, M.

    2015-12-01

    Exoenzymes are proteins that can catalyze the depolymerization of soil organic matter (SOM). Proteins can also be an important source of organic N for microorganisms, but must be fragmented into small peptides in order to be transported through their membranes. An exoenzyme's affinity to mineral surfaces found in soil affects their capacity to degrade SOM or other proteins. Our goal was to determine the range of modifications on proteins when they interact with a mineral surface. We hypothesized that pedogenic oxides would fragment or promote greater chemical modifications to a protein than phyllosilicates. A well-characterized protein proxy (Gb1, IEP 4.0, 6.2 kDA) was adsorbed onto functionally different mineral surfaces (goethite, montmorillonite, kaolinite and birnesite) at pH 5 and pH 7. We then generated three engineered proxies of Gb1 by inserting either negatively charged, positively charged or aromatic amino acids into the second loop. We used liquid chromatography coupled with a mass spectrometer (LC-MS/MS) and solution-state Heteronuclear Single Quantum Coherence Spectroscopy Nuclear Magnetic Resonance (HSQC NMR) to observe modifications to Gb1 that was allowed to equilibrate during the adsorption process for kaolinite, goethite, birnessite, and montmorillonite. We also used Helium Ion Microscopy (HIM) to determine which surface archetypes Gb1 preferentially adsorbed to as a function of the mineral type. The three engineered proxies were used to determine how variation of the amino acid sequence affects a protein interaction with a mineral surface. Preliminary results in the LC-MS/MS indicate that birnessite hydrolytically fragments Gb1 into polypeptides. Our results suggest that not all mineral surfaces in soil may act as sorbents for EEs and that chemical modification of their structure should also be considered as an explanation for decrease in EE activity. Our results also indicate an abiotic pathway for the turnover of proteins, although its relative

  13. An alternative easy method for antibody purification and analysis of protein-protein interaction using GST fusion proteins immobilized onto glutathione-agarose.

    PubMed

    Zalazar, L; Alonso, C A I; De Castro, R E; Cesari, A

    2014-01-01

    Immobilization of small proteins designed to perform protein-protein assays can be a difficult task. Often, the modification of reactive residues necessary for the interaction between the immobilized protein and the matrix compromises the interaction between the protein and its target. In these cases, glutathione-S-transferase (GST) is a valuable tag providing a long arm that makes the bait protein accessible to the mobile flow phase of the chromatography. In the present report, we used a GST fusion version of the 8-kDa protein serine protease inhibitor Kazal-type 3 (SPINK3) as the bait to purify anti-SPINK3 antibodies from a rabbit crude serum. The protocol for immobilization of GST-SPINK3 to glutathione-agarose beads was modified from previously reported protocols by using an alternative bifunctional cross-linker (dithiobis(succinimidyl propionate)) in a very simple procedure and by using simple buffers under physiological conditions. We concluded that the immobilized protein remained bound to the column after elution with low pH, allowing the reuse of the column for alternative uses, such as screening for other protein-protein interactions using SPINK3 as the bait.

  14. Trimeric Transmembrane Domain Interactions in Paramyxovirus Fusion Proteins

    PubMed Central

    Smith, Everett Clinton; Smith, Stacy E.; Carter, James R.; Webb, Stacy R.; Gibson, Kathleen M.; Hellman, Lance M.; Fried, Michael G.; Dutch, Rebecca Ellis

    2013-01-01

    Paramyxovirus fusion (F) proteins promote membrane fusion between the viral envelope and host cell membranes, a critical early step in viral infection. Although mutational analyses have indicated that transmembrane (TM) domain residues can affect folding or function of viral fusion proteins, direct analysis of TM-TM interactions has proved challenging. To directly assess TM interactions, the oligomeric state of purified chimeric proteins containing the Staphylococcal nuclease (SN) protein linked to the TM segments from three paramyxovirus F proteins was analyzed by sedimentation equilibrium analysis in detergent and buffer conditions that allowed density matching. A monomer-trimer equilibrium best fit was found for all three SN-TM constructs tested, and similar fits were obtained with peptides corresponding to just the TM region of two different paramyxovirus F proteins. These findings demonstrate for the first time that class I viral fusion protein TM domains can self-associate as trimeric complexes in the absence of the rest of the protein. Glycine residues have been implicated in TM helix interactions, so the effect of mutations at Hendra F Gly-508 was assessed in the context of the whole F protein. Mutations G508I or G508L resulted in decreased cell surface expression of the fusogenic form, consistent with decreased stability of the prefusion form of the protein. Sedimentation equilibrium analysis of TM domains containing these mutations gave higher relative association constants, suggesting altered TM-TM interactions. Overall, these results suggest that trimeric TM interactions are important driving forces for protein folding, stability and membrane fusion promotion. PMID:24178297

  15. Rosetta stone method for detecting protein function and protein-protein interactions from genome sequences

    DOEpatents

    Eisenberg, David; Marcotte, Edward M.; Pellegrini, Matteo; Thompson, Michael J.; Yeates, Todd O.

    2002-10-15

    A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

  16. A Brief Review of RNA–Protein Interaction Database Resources

    PubMed Central

    Yi, Ying; Zhao, Yue; Huang, Yan; Wang, Dong

    2017-01-01

    RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions. PMID:29657278

  17. Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

    PubMed

    Garamszegi, Sara; Franzosa, Eric A; Xia, Yu

    2013-01-01

    A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are

  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. A mammalian germ cell-specific RNA-binding protein interacts with ubiquitously expressed proteins involved in splice site selection

    NASA Astrophysics Data System (ADS)

    Elliott, David J.; Bourgeois, Cyril F.; Klink, Albrecht; Stévenin, James; Cooke, Howard J.

    2000-05-01

    RNA-binding motif (RBM) genes are found on all mammalian Y chromosomes and are implicated in spermatogenesis. Within human germ cells, RBM protein shows a similar nuclear distribution to components of the pre-mRNA splicing machinery. To address the function of RBM, we have used protein-protein interaction assays to test for possible physical interactions between these proteins. We find that RBM protein directly interacts with members of the SR family of splicing factors and, in addition, strongly interacts with itself. We have mapped the protein domains responsible for mediating these interactions and expressed the mouse RBM interaction region as a bacterial fusion protein. This fusion protein can pull-down several functionally active SR protein species from cell extracts. Depletion and add-back experiments indicate that these SR proteins are the only splicing factors bound by RBM which are required for the splicing of a panel of pre-mRNAs. Our results suggest that RBM protein is an evolutionarily conserved mammalian splicing regulator which operates as a germ cell-specific cofactor for more ubiquitously expressed pre-mRNA splicing activators.

  20. Optimizing pH response of affinity between protein G and IgG Fc: how electrostatic modulations affect protein-protein interactions.

    PubMed

    Watanabe, Hideki; Matsumaru, Hiroyuki; Ooishi, Ayako; Feng, Yanwen; Odahara, Takayuki; Suto, Kyoko; Honda, Shinya

    2009-05-01

    Protein-protein interaction in response to environmental conditions enables sophisticated biological and biotechnological processes. Aiming toward the rational design of a pH-sensitive protein-protein interaction, we engineered pH-sensitive mutants of streptococcal protein G B1, a binder to the IgG constant region. We systematically introduced histidine residues into the binding interface to cause electrostatic repulsion on the basis of a rigid body model. Exquisite pH sensitivity of this interaction was confirmed by surface plasmon resonance and affinity chromatography employing a clinically used human IgG. The pH-sensitive mechanism of the interaction was analyzed and evaluated from kinetic, thermodynamic, and structural viewpoints. Histidine-mediated electrostatic repulsion resulted in significant loss of exothermic heat of the binding that decreased the affinity only at acidic conditions, thereby improving the pH sensitivity. The reduced binding energy was partly recovered by "enthalpy-entropy compensation." Crystal structures of the designed mutants confirmed the validity of the rigid body model on which the effective electrostatic repulsion was based. Moreover, our data suggested that the entropy gain involved exclusion of water molecules solvated in a space formed by the introduced histidine and adjacent tryptophan residue. Our findings concerning the mechanism of histidine-introduced interactions will provide a guideline for the rational design of pH-sensitive protein-protein recognition.

  1. Gγ recruitment system incorporating a novel signal amplification circuit to screen transient protein-protein interactions.

    PubMed

    Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

    2011-09-01

    Weak and transient protein-protein interactions are associated with biological processes, but many are still undefined because of the difficulties in their identification. Here, we describe a redesigned method to screen transient protein-protein interactions by using a novel signal amplification circuit, which is incorporated into yeast to artificially magnify the signal responding to the interactions. This refined method is based on the previously established Gγ recruitment system, which utilizes yeast G-protein signaling and mating growth selection to screen interacting protein pairs. In the current study, to test the capability of our method, we chose mutants of the Z-domain derived from Staphylococcus aureus protein A as candidate proteins, and the Fc region of human IgG as the counterpart. By introduction of an artificial signal amplifier into the previous Gγ recruitment system, the signal transduction responding to transient interactions between Z-domain mutants and the Fc region with significantly low affinity (8.0 × 10(3) M(-1)) was successfully amplified in recombinant haploid yeast cells. As a result of zygosis with the opposite mating type of wild-type haploid cells, diploid colonies were vigorously and selectively generated on the screening plates, whereas our previous system rarely produced positive colonies. This new approach will be useful for exploring the numerous transient interactions that remain undefined because of the lack of powerful screening tools for their identification. © 2011 The Authors Journal compilation © 2011 FEBS.

  2. Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

    PubMed

    Liu, Zhi-Ping; Chen, Luonan

    2016-01-01

    Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

  3. Super-resolution imaging and tracking of protein-protein interactions in sub-diffraction cellular space

    NASA Astrophysics Data System (ADS)

    Liu, Zhen; Xing, Dong; Su, Qian Peter; Zhu, Yun; Zhang, Jiamei; Kong, Xinyu; Xue, Boxin; Wang, Sheng; Sun, Hao; Tao, Yile; Sun, Yujie

    2014-07-01

    Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein-protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB-EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB-EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB-EF-Tu interactions.

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

    PubMed

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

    2010-07-01

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

  5. 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.

  6. Interaction of silver nanoparticles with proteins: a characteristic protein concentration dependent profile of SPR signal.

    PubMed

    Banerjee, Victor; Das, K P

    2013-11-01

    Silver nanoparticles are finding increasing applications in biological systems, for example as antimicrobial agents and potential candidates for control drug release systems. In all such applications, silver nanoparticles interact with proteins and other biomolecules. Hence, the study of such interactions is of considerable importance. While BSA has been extensively used as a model protein for the study of interaction with the silver nanoparticles, studies using other proteins are rather limited. The interaction of silver nanoparticles with light leads to collective oscillation of the conducting electrons giving rise to surface plasmon resonance (SPR). Here, we have studied the protein concentration dependence of the SPR band profiles for a number of proteins. We found that for all the proteins, with increase in concentration, the SPR band intensity initially decreased, reaching minima and then increased again leading to a characteristic "dip and rise" pattern. Minimum point of the pattern appeared to be related to the isoelectric point of the proteins. Detailed dynamic light scattering and transmission electron microscopy studies revealed that the consistency of SPR profile was dependent on the average particle size and state of association of the silver nanoparticles with the change in the protein concentration. Fluorescence spectroscopic studies showed the binding constants of the proteins with the silver nanoparticles were in the nano molar range with more than one nanoparticle binding to protein molecule. Structural studies demonstrate that protein retains its native-like structure on the nanoparticle surface unless the molar ratio of silver nanoparticles to protein exceeds 10. Our study reveals that nature of the protein concentration dependent profile of SPR signal is a general phenomena and mostly independent of the size and structure of the proteins. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. The Molybdenum Cofactor Biosynthesis Network: In vivo Protein-Protein Interactions of an Actin Associated Multi-Protein Complex.

    PubMed

    Kaufholdt, David; Baillie, Christin-Kirsty; Meinen, Rieke; Mendel, Ralf R; Hänsch, Robert

    2017-01-01

    Survival of plants and nearly all organisms depends on the pterin based molybdenum cofactor (Moco) as well as its effective biosynthesis and insertion into apo-enzymes. To this end, both the central Moco biosynthesis enzymes are characterized and the conserved four-step reaction pathway for Moco biosynthesis is well-understood. However, protection mechanisms to prevent degradation during biosynthesis as well as transfer of the highly oxygen sensitive Moco and its intermediates are not fully enlightened. The formation of protein complexes involving transient protein-protein interactions is an efficient strategy for protected metabolic channelling of sensitive molecules. In this review, Moco biosynthesis and allocation network is presented and discussed. This network was intensively studied based on two in vivo interaction methods: bimolecular fluorescence complementation (BiFC) and split-luciferase. Whereas BiFC allows localisation of interacting partners, split-luciferase assay determines interaction strengths in vivo . Results demonstrate (i) interaction of Cnx2 and Cnx3 within the mitochondria and (ii) assembly of a biosynthesis complex including the cytosolic enzymes Cnx5, Cnx6, Cnx7, and Cnx1, which enables a protected transfer of intermediates. The whole complex is associated with actin filaments via Cnx1 as anchor protein. After biosynthesis, Moco needs to be handed over to the specific apo-enzymes. A potential pathway was discovered. Molybdenum-containing enzymes of the sulphite oxidase family interact directly with Cnx1. In contrast, the xanthine oxidoreductase family acquires Moco indirectly via a Moco binding protein (MoBP2) and Moco sulphurase ABA3. In summary, the uncovered interaction matrix enables an efficient transfer for intermediate and product protection via micro-compartmentation.

  8. Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM.

    PubMed

    Tuncbag, Nurcan; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

    2011-08-11

    Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

  9. Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

    PubMed

    Cao, Buwen; Deng, Shuguang; Qin, Hua; Ding, Pingjian; Chen, Shaopeng; Li, Guanghui

    2018-06-15

    High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks. In this study, based on penalized matrix decomposition ( PMD ), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD pc ) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

  10. Hydrogen-deuterium exchange mass spectrometry reveals folding and allostery in protein-protein interactions.

    PubMed

    Ramirez-Sarmiento, Cesar A; Komives, Elizabeth A

    2018-04-06

    Hydrogen-deuterium exchange mass spectrometry (HDXMS) has emerged as a powerful approach for revealing folding and allostery in protein-protein interactions. The advent of higher resolution mass spectrometers combined with ion mobility separation and ultra performance liquid chromatographic separations have allowed the complete coverage of large protein sequences and multi-protein complexes. Liquid-handling robots have improved the reproducibility and accurate temperature control of the sample preparation. Many researchers are also appreciating the power of combining biophysical approaches such as stopped-flow fluorescence, single molecule FRET, and molecular dynamics simulations with HDXMS. In this review, we focus on studies that have used a combination of approaches to reveal (re)folding of proteins as well as on long-distance allosteric changes upon interaction. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. Interactions between whey proteins and salivary proteins as related to astringency of whey protein beverages at low pH.

    PubMed

    Ye, A; Streicher, C; Singh, H

    2011-12-01

    Whey protein beverages have been shown to be astringent at low pH. In the present study, the interactions between model whey proteins (β-lactoglobulin and lactoferrin) and human saliva in the pH range from 7 to 2 were investigated using particle size, turbidity, and ζ-potential measurements and sodium dodecyl sulfate-PAGE. The correlation between the sensory results of astringency and the physicochemical data was discussed. Strong interactions between β-lactoglobulin and salivary proteins led to an increase in the particle size and turbidity of mixtures of both unheated and heated β-lactoglobulin and human saliva at pH ∼3.4. However, the large particle size and high turbidity that occurred at pH 2.0 were the result of aggregation of human salivary proteins. The intense astringency in whey protein beverages may result from these increases in particle size and turbidity at these pH values and from the aggregation and precipitation of human salivary proteins alone at pH <3.0. The involvement of salivary proteins in the interaction is a key factor in the perception of astringency in whey protein beverages. At any pH, the increases in particle size and turbidity were much smaller in mixtures of lactoferrin and saliva, which suggests that aggregation and precipitation may not be the only mechanism linked to the perception of astringency in whey protein. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

    PubMed

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

    2013-03-06

    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.

  13. 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.

  14. Screening for Protein-DNA Interactions by Automatable DNA-Protein Interaction ELISA

    PubMed Central

    Schüssler, Axel; Kolukisaoglu, H. Üner; Koch, Grit; Wallmeroth, Niklas; Hecker, Andreas; Thurow, Kerstin; Zell, Andreas; Harter, Klaus; Wanke, Dierk

    2013-01-01

    DNA-binding proteins (DBPs), such as transcription factors, constitute about 10% of the protein-coding genes in eukaryotic genomes and play pivotal roles in the regulation of chromatin structure and gene expression by binding to short stretches of DNA. Despite their number and importance, only for a minor portion of DBPs the binding sequence had been disclosed. Methods that allow the de novo identification of DNA-binding motifs of known DBPs, such as protein binding microarray technology or SELEX, are not yet suited for high-throughput and automation. To close this gap, we report an automatable DNA-protein-interaction (DPI)-ELISA screen of an optimized double-stranded DNA (dsDNA) probe library that allows the high-throughput identification of hexanucleotide DNA-binding motifs. In contrast to other methods, this DPI-ELISA screen can be performed manually or with standard laboratory automation. Furthermore, output evaluation does not require extensive computational analysis to derive a binding consensus. We could show that the DPI-ELISA screen disclosed the full spectrum of binding preferences for a given DBP. As an example, AtWRKY11 was used to demonstrate that the automated DPI-ELISA screen revealed the entire range of in vitro binding preferences. In addition, protein extracts of AtbZIP63 and the DNA-binding domain of AtWRKY33 were analyzed, which led to a refinement of their known DNA-binding consensi. Finally, we performed a DPI-ELISA screen to disclose the DNA-binding consensus of a yet uncharacterized putative DBP, AtTIFY1. A palindromic TGATCA-consensus was uncovered and we could show that the GATC-core is compulsory for AtTIFY1 binding. This specific interaction between AtTIFY1 and its DNA-binding motif was confirmed by in vivo plant one-hybrid assays in protoplasts. Thus, the value and applicability of the DPI-ELISA screen for de novo binding site identification of DBPs, also under automatized conditions, is a promising approach for a deeper understanding

  15. 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.

  16. Dynamic interactions of proteins in complex networks

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Appella, E.; Anderson, C.

    2009-10-01

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

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

    USDA-ARS?s Scientific Manuscript database

    Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures have been helpful in identifying critical genes and proteins in biomolecular networks. The proposed centrality measure DiffSLc uses the number of interactions of a protein and gen...

  18. Multiple protein–protein interactions converging on the Prp38 protein during activation of the human spliceosome

    PubMed Central

    Schütze, Tonio; Ulrich, Alexander K.C.; Apelt, Luise; Will, Cindy L.; Bartlick, Natascha; Seeger, Martin; Weber, Gert; Lührmann, Reinhard; Stelzl, Ulrich; Wahl, Markus C.

    2016-01-01

    Spliceosomal Prp38 proteins contain a conserved amino-terminal domain, but only higher eukaryotic orthologs also harbor a carboxy-terminal RS domain, a hallmark of splicing regulatory SR proteins. We show by crystal structure analysis that the amino-terminal domain of human Prp38 is organized around three pairs of antiparallel α-helices and lacks similarities to RNA-binding domains found in canonical SR proteins. Instead, yeast two-hybrid analyses suggest that the amino-terminal domain is a versatile protein–protein interaction hub that possibly binds 12 other spliceosomal proteins, most of which are recruited at the same stage as Prp38. By quantitative, alanine surface-scanning two-hybrid screens and biochemical analyses we delineated four distinct interfaces on the Prp38 amino-terminal domain. In vitro interaction assays using recombinant proteins showed that Prp38 can bind at least two proteins simultaneously via two different interfaces. Addition of excess Prp38 amino-terminal domain to in vitro splicing assays, but not of an interaction-deficient mutant, stalled splicing at a precatalytic stage. Our results show that human Prp38 is an unusual SR protein, whose amino-terminal domain is a multi-interface protein–protein interaction platform that might organize the relative positioning of other proteins during splicing. PMID:26673105

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

    PubMed

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

    2009-03-11

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

  20. [Expression of Dengue virus type 2 nonstructural protein 3 and isolation of host proteins interacting with it].

    PubMed

    Weng, Daihui; Lei, Yingfeng; Dong, Yangchao; Han, Peijun; Ye, Chuantao; Yang, Jing; Wang, Yuan; Yin, Wen

    2015-12-01

    To construct the plasmid expressing the fusion protein of Dengue virus type 2 (DENV2) nonstructural protein 3 (NS3) with affinity tag, and isolate the cellular proteins interacting with NS3 protein using tandem affinity purification (TAP) assay. Primers for amplifying NS3 gene were designed according to the sequence of DENV2 genome and chemically synthesized. The NS3 fragments, after amplified by PCR with DENV2 cDNA as template, were digested and cloned into the mammalian eukaryotic expression vector pCI-SF with the tandem affinity tag (FLAG-StrepII). The recombinant pCI-NS3-SF was transiently transformed by Lipofectamine(TM) 2000 into HEK293T cells, and the expression of the fusion protein was confirmed by Western blotting. Cellular proteins that interacted with NS3 were isolated and purified by TAP assay. The eukaryotic expression vector expressing NS3 protein was successfully constructed. The host proteins interacting with NS3 protein were isolated by TAP system. TAP is an efficient method to isolate the cellular proteins interacting with DENV2 NS3.

  1. Unfolding and inactivation of proteins by counterions in protein-nanoparticles interaction.

    PubMed

    Ghosh, Goutam; Gaikwad, Pallavi S; Panicker, Lata; Nath, Bimalendu B; Mukhopadhyaya, Rita

    2016-09-01

    In this work, the structure and activity of proteins; such as, hen egg lysozyme (HEWL) and calf intestine alkaline phosphatase (CIAP); have been investigated after incubation with surface coated iron oxide nanoparticles (IONPs) in water. IONPs were coated with counterions bound charge-ligands and were named as the charge-ligand counterions iron oxide nanoparticles (CLC-IONPs). The coating was done with tri-lithium citrate (TLC) and tri-potassium citrate (TKC) to have negative surface charge of CLC-IONPs and Li(+) and K(+), respectively, as counterions. To have positive surface charge, IONPs were coated with cetylpyridinium chloride (CPC) and cetylpyridinium iodide (CPI) having Cl(-) and I(-), respectively, as counterions. The secondary structure of proteins was measured using far ultraviolet circular dichroism (CD) spectroscopy which showed that both proteins were irreversibly unfolded after incubation with CLC-IONPs. The unfolded proteins were seen to be functionally inactive, as confirmed through their activity assays, i.e., HEWL with Escherichia coli (E. coli) and CIAP with para-nitrophenyl phosphate (pNPP). Additionally, we have observed that monomeric hemoglobin (Hb) from radio-resistant insect Chironomus ramosus (ChHb) was also partially unfolded upon interaction with CLC-IONPs. This work clearly shows the role of counterions in protein inactivation via protein-nanoparticles interaction and, therefore, CLC-IONPs could be used for therapeutic purpose. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Bacterial ice crystal controlling proteins.

    PubMed

    Lorv, Janet S H; Rose, David R; Glick, Bernard R

    2014-01-01

    Across the world, many ice active bacteria utilize ice crystal controlling proteins for aid in freezing tolerance at subzero temperatures. Ice crystal controlling proteins include both antifreeze and ice nucleation proteins. Antifreeze proteins minimize freezing damage by inhibiting growth of large ice crystals, while ice nucleation proteins induce formation of embryonic ice crystals. Although both protein classes have differing functions, these proteins use the same ice binding mechanisms. Rather than direct binding, it is probable that these protein classes create an ice surface prior to ice crystal surface adsorption. Function is differentiated by molecular size of the protein. This paper reviews the similar and different aspects of bacterial antifreeze and ice nucleation proteins, the role of these proteins in freezing tolerance, prevalence of these proteins in psychrophiles, and current mechanisms of protein-ice interactions.

  3. Catching the PEG-induced attractive interaction between proteins.

    PubMed

    Vivarès, D; Belloni, L; Tardieu, A; Bonneté, F

    2002-09-01

    We present the experimental and theoretical background of a method to characterize the protein-protein attractive potential induced by one of the mostly used crystallizing agents in the protein-field, the poly(ethylene glycol) (PEG). This attractive interaction is commonly called, in colloid physics, the depletion interaction. Small-Angle X-ray Scattering experiments and numerical treatments based on liquid-state theories were performed on urate oxidase-PEG mixtures with two different PEGs (3350 Da and 8000 Da). A "two-component" approach was used in which the polymer-polymer, the protein-polymer and the protein-protein pair potentials were determined. The resulting effective protein-protein potential was characterized. This potential is the sum of the free-polymer protein-protein potential and of the PEG-induced depletion potential. The depletion potential was found to be hardly dependent upon the protein concentration but strongly function of the polymer size and concentration. Our results were also compared with two models, which give an analytic expression for the depletion potential.

  4. Survey of rice proteins interacting with OsFCA and OsFY proteins which are homologous to the Arabidopsis flowering time proteins, FCA and FY.

    PubMed

    Jang, Yun Hee; Park, Hyo-Young; Kim, Soon-Kap; Lee, Jeong Hwan; Suh, Mi Chung; Chung, Young Soo; Paek, Kyung-Hee; Kim, Jeong-Kook

    2009-08-01

    The FCA protein is involved in controlling flowering time and plays more general roles in RNA-mediated chromatin silencing in Arabidopsis. It contains two RNA-binding domains and a WW domain. The FCA protein interacts with FY, a polyadenylation factor, via its WW domain. We previously characterized a rice gene, OsFCA, which was homologous to FCA. Here, we found that the OsFCA protein could interact through its WW domain with the following proteins: OsFY, a protein containing a CID domain present in RNA-processing factors such as Pcf11 and Nrd1; a protein similar to splicing factor SF1; a protein similar to FUSE splicing factor; and OsMADS8. The FY protein is associated with the 3' end processing machinery in Arabidopsis. Thus, we examined interactions between OsFY and the rice homologs (OsCstF-50, -64 and -77) of the AtCstF-50, -64 and -77 proteins. We found that OsFY could bind OsCstF50, whereas the OsCstF77 protein could bridge the interaction between OsCstF50 and OsCstF64. Taken together, our data suggest that OsFCA could interact with several proteins other than OsFY through its WW domain and may play several roles in rice.

  5. A simple method to generate stable cell lines for the analysis of transient protein-protein interactions.

    PubMed

    Savage, Emilia Elizabeth; Wootten, Denise; Christopoulos, Arthur; Sexton, Patrick Michael; Furness, Sebastian George Barton

    2013-04-01

    Transient protein-protein interactions form the basis of signal transduction pathways in addition to many other biological processes. One tool for studying these interactions is bioluminescence resonance energy transfer (BRET). This technique has been widely applied to study signaling pathways, in particular those initiated by G protein-coupled receptors (GPCRs). These assays are routinely performed using transient transfection, a technique that has limitations in terms of assay cost and variability, overexpression of interacting proteins, vector uptake limited to cycling cells, and non-homogenous expression across cells within the assay. To address these issues, we developed bicistronic vectors for use with Life Technology's Gateway and flpIN systems. These vectors provide a means to generate isogenic cell lines for comparison of interacting proteins. They have the advantage of stable, single copy, isogenic, homogeneous expression with low inter-assay variation. We demonstrate their utility by assessing ligand-induced interactions between GPCRs and arrestin proteins.

  6. Liquid-Liquid Phase Separation in a Dual Variable Domain Immunoglobulin Protein Solution: Effect of Formulation Factors and Protein-Protein Interactions.

    PubMed

    Raut, Ashlesha S; Kalonia, Devendra S

    2015-09-08

    Dual variable domain immunoglobulin proteins (DVD-Ig proteins) are large molecules (MW ∼ 200 kDa) with increased asymmetry because of their extended Y-like shape, which results in increased formulation challenges. Liquid-liquid phase separation (LLPS) of protein solutions into protein-rich and protein-poor phases reduces solution stability at intermediate concentrations and lower temperatures, and is a serious concern in formulation development as therapeutic proteins are generally stored at refrigerated conditions. In the current work, LLPS was studied for a DVD-Ig protein molecule as a function of solution conditions by measuring solution opalescence. LLPS of the protein was confirmed by equilibrium studies and by visually observing under microscope. The protein does not undergo any structural change after phase separation. Protein-protein interactions were measured by light scattering (kD) and Tcloud (temperature that marks the onset of phase separation). There is a good agreement between kD measured in dilute solution with Tcloud measured in the critical concentration range. Results indicate that the increased complexity of the molecule (with respect to size, shape, and charge distribution on the molecule) increases contribution of specific and nonspecific interactions in solution, which are affected by formulation factors, resulting in LLPS for DVD-Ig protein.

  7. Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties

    PubMed Central

    Shi, Xiaohe; Lu, Wen-Cong; Cai, Yu-Dong; Chou, Kuo-Chen

    2011-01-01

    Background With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner. Methodology/Principal Findings Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%. Conclusions/Significance The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem. PMID:21283518

  8. 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.

  9. Analysis of Protein-RNA and Protein-Peptide Interactions in Equine Infectious Anemia

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lee, Jae-Hyung

    2007-01-01

    Macromolecular interactions are essential for virtually all cellular functions including signal transduction processes, metabolic processes, regulation of gene expression and immune responses. This dissertation focuses on the characterization of two important macromolecular interactions involved in the relationship between Equine Infectious Anemia Virus (EIAV) and its host cell in horse: (1) the interaction between the EIAV Rev protein and its binding site, the Rev-responsive element (RRE) and (2) interactions between equine MHC class I molecules and epitope peptides derived from EIAV proteins. EIAV, one of the most divergent members of the lentivirus family, has a single-stranded RNA genome and carries severalmore » regulatory and structural proteins within its viral particle. Rev is an essential EIAV regulatory encoded protein that interacts with the viral RRE, a specific binding site in the viral mRNA. Using a combination of experimental and computational methods, the interactions between EIAV Rev and RRE were characterized in detail. EIAV Rev was shown to have a bipartite RNA binding domain contain two arginine rich motifs (ARMs). The RRE secondary structure was determined and specific structural motifs that act as cis-regulatory elements for EIAV Rev-RRE interaction were identified. Interestingly, a structural motif located in the high affinity Rev binding site is well conserved in several diverse lentiviral genoes, including HIV-1. Macromolecular interactions involved in the immune response of the horse to EIAV infection were investigated by analyzing complexes between MHC class I proteins and epitope peptides derived from EIAV Rev, Env and Gag proteins. Computational modeling results provided a mechanistic explanation for the experimental finding that a single amino acid change in the peptide binding domain of the quine MHC class I molecule differentially affectes the recognitino of specific epitopes by EIAV-specific CTL. Together, the findings in this

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.

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

  11. Characterization of essential proteins based on network topology in proteins interaction networks

    NASA Astrophysics Data System (ADS)

    Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

    2014-06-01

    The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

  12. Studies of protein-protein and protein-water interactions by small angle x-ray scattering, terahertz spectroscopy, ASMOS, and computer simulation

    NASA Astrophysics Data System (ADS)

    Kim, Seung Joong

    The protein folding problem has been one of the most challenging subjects in biological physics due to its complexity. Energy landscape theory based on statistical mechanics provides a thermodynamic interpretation of the protein folding process. We have been working to answer fundamental questions about protein-protein and protein-water interactions, which are very important for describing the energy landscape surface of proteins correctly. At first, we present a new method for computing protein-protein interaction potentials of solvated proteins directly from SAXS data. An ensemble of proteins was modeled by Metropolis Monte Carlo and Molecular Dynamics simulations, and the global X-ray scattering of the whole model ensemble was computed at each snapshot of the simulation. The interaction potential model was optimized and iterated by a Levenberg-Marquardt algorithm. Secondly, we report that terahertz spectroscopy directly probes hydration dynamics around proteins and determines the size of the dynamical hydration shell. We also present the sequence and pH-dependence of the hydration shell and the effect of the hydrophobicity. On the other hand, kinetic terahertz absorption (KITA) spectroscopy is introduced to study the refolding kinetics of ubiquitin and its mutants. KITA results are compared to small angle X-ray scattering, tryptophan fluorescence, and circular dichroism results. We propose that KITA monitors the rearrangement of hydrogen bonding during secondary structure formation. Finally, we present development of the automated single molecule operating system (ASMOS) for a high throughput single molecule detector, which levitates a single protein molecule in a 10 microm diameter droplet by the laser guidance. I also have performed supporting calculations and simulations with my own program codes.

  13. Prediction of scaffold proteins based on protein interaction and domain architectures.

    PubMed

    Oh, Kimin; Yi, Gwan-Su

    2016-07-28

    Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively. From a total of 10,419 basic scaffold protein candidates in protein interactomes, we classified them into three classes according to the structural evidences for scaffolding, such as domain architectures, domain interactions and protein complexes. Finally, we could define 2716 highly reliable scaffold protein candidates and their characterized functional features. To assess the accuracy of our prediction, the gold standard positive and negative data sets were constructed. We prepared 158 gold standard positive data and 844 gold standard negative data based on the functional information from Gene Ontology consortium. The precision, sensitivity and specificity of our testing was 80.3, 51.0, and 98.5 % respectively. Through the function enrichment analysis of highly reliable scaffold proteins, we could confirm the significantly enriched functions that are related to scaffold protein binding. We also identified functional association between scaffold proteins and their recruited proteins. Furthermore, we checked that the disease association of scaffold proteins is higher than kinases. In

  14. Macrocyclic peptide inhibitors for the protein-protein interaction of Zaire Ebola virus protein 24 and karyopherin alpha 5.

    PubMed

    Song, Xiao; Lu, Lu-Yi; Passioura, Toby; Suga, Hiroaki

    2017-06-21

    Ebola virus infection leads to severe hemorrhagic fever in human and non-human primates with an average case fatality rate of 50%. To date, numerous potential therapies are in development, but FDA-approved drugs or vaccines are yet unavailable. Ebola viral protein 24 (VP24) is a multifunctional protein that plays critical roles in the pathogenesis of Ebola virus infection, e.g. innate immune suppression by blocking the interaction between KPNA and PY-STAT1. Here we report macrocyclic peptide inhibitors of the VP24-KPNA5 protein-protein interaction (PPI) by means of the RaPID (Random non-standard Peptides Integrated Discovery) system. These macrocyclic peptides showed remarkably high affinity to recombinant Zaire Ebola virus VP24 (eVP24), with a dissociation constant in the single digit nanomolar range, and could also successfully disrupt the eVP24-KPNA interaction. This work provides for the first time a chemical probe capable of modulating this PPI interaction and is the starting point for the development of unique anti-viral drugs against the Ebola virus.

  15. Role of electrostatic interaction on surfactant induced protein unfolding

    NASA Astrophysics Data System (ADS)

    Sumit, Kumar, Sugam; Aswal, V. K.

    2013-02-01

    Small Angle Neutron Scattering has been used to examine the effect of electrostatic interaction on surfactant induced protein unfolding. Measurements are carried out from 1 wt% Bovine Serum Albumin (BSA) protein with 1 wt% Sodium Dodecyl Sulphate (SDS) surfactant at pH 7 in presence of varying concentration of NaCl. It is found that both the components (protein and surfactant micelle which are likely charged) exist individually without any interaction in absence of salt, whereas their interaction and protein unfolding is enhanced with the increase in salt concentration. The structure of protein-surfactant interaction is characterized by fractal bead-necklace model.

  16. Role for protein–protein interaction databases in human genetics

    PubMed Central

    Pattin, Kristine A; Moore, Jason H

    2010-01-01

    Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies. PMID:19929610

  17. Role of protein concentration and protein-saliva interactions in the astringency of whey proteins at low pH.

    PubMed

    Kelly, M; Vardhanabhuti, B; Luck, P; Drake, M A; Osborne, J; Foegeding, E A

    2010-05-01

    Whey protein beverages are adjusted to pH <4.5 to enhance clarity and stability, but this pH level is also associated with increased astringency. The goal of this investigation was to determine the effects of protein concentration on astringency and interactions between whey and salivary proteins. Whey protein beverages containing 0.25 to 13% (wt/wt) beta-lactoglobulin and 0.017% (wt/wt) sucralose at pH 2.6 to 4.2 were examined using descriptive sensory analysis. Controls were similar pH phosphate buffers at phosphate concentrations equivalent to the amount of phosphoric acid required to adjust the pH of the protein solution. Changes in astringency with protein concentration depended on pH. At pH 3.5, astringency significantly increased with protein concentration from 0.25 to 4% (wt/wt) and then remained constant from 4 to 13% (wt/wt). Conversely, at pH 2.6, astringency decreased with an increase in protein concentration [0.5-10% (wt/wt)]. This suggests a complex relationship that includes pH and buffering capacity of the beverages. Furthermore, saliva flow rates increased with increasing protein concentrations, showing that the physiological conditions in the mouth change with protein concentration. Maximum turbidity of whey protein-saliva mixtures was observed between pH 4.6 and 5.2. Both sensory evaluation and in vitro study of interactions between beta-LG and saliva indicate that astringency of whey proteins is a complex process determined by the extent of aggregation occurring in the mouth, which depends on the whey protein beverage pH and buffering capacity in addition to saliva flow rate. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  18. Simultaneously measuring multiple protein interactions and their correlations in a cell by Protein-interactome Footprinting

    PubMed Central

    Luo, Si-Wei; Liang, Zhi; Wu, Jia-Rui

    2017-01-01

    Quantitatively detecting correlations of multiple protein-protein interactions (PPIs) in vivo is a big challenge. Here we introduce a novel method, termed Protein-interactome Footprinting (PiF), to simultaneously measure multiple PPIs in one cell. The principle of PiF is that each target physical PPI in the interactome is simultaneously transcoded into a specific DNA sequence based on dimerization of the target proteins fused with DNA-binding domains. The interaction intensity of each target protein is quantified as the copy number of the specific DNA sequences bound by each fusion protein dimers. Using PiF, we quantitatively reveal dynamic patterns of PPIs and their correlation network in E. coli two-component systems. PMID:28338015

  19. An efficient way of studying protein-protein interactions involving HIF-α, c-Myc, and Sp1.

    PubMed

    To, Kenneth K-W; Huang, L Eric

    2013-01-01

    Protein-protein interaction is an essential biochemical event that mediates various cellular processes including gene expression, intracellular signaling, and intercellular interaction. Understanding such interaction is key to the elucidation of mechanisms of cellular processes in biology and diseases. The hypoxia-inducible transcription factor HIF-1α possesses a non-transcriptional activity that competes with c-Myc for Sp1 binding, whereas its isoform HIF-2α lacks Sp1-binding activity due to phosphorylation. Here, we describe the use of in vitro translation to effectively investigate the dynamics of protein-protein interactions among HIF-1α, c-Myc, and Sp1 and to demonstrate protein phosphorylation as a molecular determinant that functionally distinguishes HIF-2α from HIF-1α.

  20. Integration of Structural Dynamics and Molecular Evolution via Protein Interaction Networks: A New Era in Genomic Medicine

    PubMed Central

    Kumar, Avishek; Butler, Brandon M.; Kumar, Sudhir; Ozkan, S. Banu

    2016-01-01

    Summary Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. PMID:26684487

  1. Interactions of signaling proteins, growth factors and other proteins with heparan sulfate: mechanisms and mysteries.

    PubMed

    Billings, Paul C; Pacifici, Maurizio

    2015-01-01

    Heparan sulfate (HS) is a component of cell surface and matrix-associated proteoglycans (HSPGs) that, collectively, play crucial roles in many physiologic processes including cell differentiation, organ morphogenesis and cancer. A key function of HS is to bind and interact with signaling proteins, growth factors, plasma proteins, immune-modulators and other factors. In doing so, the HS chains and HSPGs are able to regulate protein distribution, bio-availability and action on target cells and can also serve as cell surface co-receptors, facilitating ligand-receptor interactions. These proteins contain an HS/heparin-binding domain (HBD) that mediates their association and contacts with HS. HBDs are highly diverse in sequence and predicted structure, contain clusters of basic amino acids (Lys and Arg) and possess an overall net positive charge, most often within a consensus Cardin-Weintraub (CW) motif. Interestingly, other domains and residues are now known to influence protein-HS interactions, as well as interactions with other glycosaminoglycans, such as chondroitin sulfate. In this review, we provide a description and analysis of HBDs in proteins including amphiregulin, fibroblast growth factor family members, heparanase, sclerostin and hedgehog protein family members. We discuss HBD structural and functional features and important roles carried out by other protein domains, and also provide novel conformational insights into the diversity of CW motifs present in Sonic, Indian and Desert hedgehogs. Finally, we review progress in understanding the pathogenesis of a rare pediatric skeletal disorder, Hereditary Multiple Exostoses (HME), characterized by HS deficiency and cartilage tumor formation. Advances in understanding protein-HS interactions will have broad implications for basic biology and translational medicine as well as for the development of HS-based therapeutics.

  2. Gene essentiality and the topology of protein interaction networks

    PubMed Central

    Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

    2005-01-01

    The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

  3. Protein-protein interaction specificity is captured by contact preferences and interface composition.

    PubMed

    Nadalin, Francesca; Carbone, Alessandra

    2018-02-01

    Large-scale computational docking will be increasingly used in future years to discriminate protein-protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein-protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations. We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue-residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis. CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS. alessandra.carbone@lip6.fr. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  4. Identification of novel direct protein-protein interactions by irradiating living cells with femtosecond UV laser pulses.

    PubMed

    Itri, Francesco; Monti, Daria Maria; Chino, Marco; Vinciguerra, Roberto; Altucci, Carlo; Lombardi, Angela; Piccoli, Renata; Birolo, Leila; Arciello, Angela

    2017-10-07

    The identification of protein-protein interaction networks in living cells is becoming increasingly fundamental to elucidate main biological processes and to understand disease molecular bases on a system-wide level. We recently described a method (LUCK, Laser UV Cross-linKing) to cross-link interacting protein surfaces in living cells by UV laser irradiation. By using this innovative methodology, that does not require any protein modification or cell engineering, here we demonstrate that, upon UV laser irradiation of HeLa cells, a direct interaction between GAPDH and alpha-enolase was "frozen" by a cross-linking event. We validated the occurrence of this direct interaction by co-immunoprecipitation and Immuno-FRET analyses. This represents a proof of principle of the LUCK capability to reveal direct protein interactions in their physiological environment. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Physics-based scoring of protein-ligand interactions: explicit polarizability, quantum mechanics and free energies.

    PubMed

    Bryce, Richard A

    2011-04-01

    The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

  6. 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.

  7. Interactions among tobacco sieve element occlusion (SEO) proteins.

    PubMed

    Jekat, Stephan B; Ernst, Antonia M; Zielonka, Sascia; Noll, Gundula A; Prüfer, Dirk

    2012-12-01

    Angiosperms transport their photoassimilates through sieve tubes, which comprise longitudinally-connected sieve elements. In dicots and also some monocots, the sieve elements contain parietal structural proteins known as phloem proteins or P-proteins. Following injury, P proteins disperse and accumulate as viscous plugs at the sieve plates to prevent the loss of valuable transport sugars. Tobacco (Nicotiana tabacum) P-proteins are multimeric complexes comprising subunits encoded by members of the SEO (sieve element occlusion) gene family. The existence of multiple subunits suggests that P-protein assembly involves interactions between SEO proteins, but this process is largely uncharacterized and it is unclear whether the different subunits perform unique roles or are redundant. We therefore extended our analysis of the tobacco P-proteins NtSEO1 and NtSEO2 to investigate potential interactions between them, and found that both proteins can form homomeric and heteromeric complexes in planta.

  8. Identification of interacting proteins of the TaFVE protein involved in spike development in bread wheat.

    PubMed

    Zheng, Yong-Sheng; Lu, Yu-Qing; Meng, Ying-Ying; Zhang, Rong-Zhi; Zhang, Han; Sun, Jia-Mei; Wang, Mu-Mu; Li, Li-Hui; Li, Ru-Yu

    2017-05-01

    WD-40 repeat-containing protein MSI4 (FVE)/MSI4 plays important roles in determining flowering time in Arabidopsis. However, its function is unexplored in wheat. In the present study, coimmunoprecipitation and nanoscale liquid chromatography coupled to MS/MS were used to identify FVE in wheat (TaFVE)-interacting or associated proteins. Altogether 89 differentially expressed proteins showed the same downregulated expression trends as TaFVE in wheat line 5660M. Among them, 62 proteins were further predicted to be involved in the interaction network of TaFVE and 11 proteins have been shown to be potential TaFVE interactors based on curated databases and experimentally determined in other species by the STRING. Both yeast two-hybrid assay and bimolecular fluorescence complementation assay showed that histone deacetylase 6 and histone deacetylase 15 directly interacted with TaFVE. Multiple chromatin-remodelling proteins and polycomb group proteins were also identified and predicted to interact with TaFVE. These results showed that TaFVE directly interacted with multiple proteins to form multiple complexes to regulate spike developmental process, e.g. histone deacetylate, chromatin-remodelling and polycomb repressive complex 2 complexes. In addition, multiple flower development regulation factors (e.g. flowering locus K homology domain, flowering time control protein FPA, FY, flowering time control protein FCA, APETALA 1) involved in floral transition were also identified in the present study. Taken together, these results further elucidate the regulatory functions of TaFVE and help reveal the genetic mechanisms underlying wheat spike differentiation. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Protein Interactions during the Flavivirus and Hepacivirus Life Cycle.

    PubMed

    Gerold, Gisa; Bruening, Janina; Weigel, Bettina; Pietschmann, Thomas

    2017-04-01

    Protein-protein interactions govern biological functions in cells, in the extracellular milieu, and at the border between cells and extracellular space. Viruses are small intracellular parasites and thus rely on protein interactions to produce progeny inside host cells and to spread from cell to cell. Usage of host proteins by viruses can have severe consequences e.g. apoptosis, metabolic disequilibria, or altered cell proliferation and mobility. Understanding protein interactions during virus infection can thus educate us on viral infection and pathogenesis mechanisms. Moreover, it has led to important clinical translations, including the development of new therapeutic and vaccination strategies. Here, we will discuss protein interactions of members of the Flaviviridae family, which are small enveloped RNA viruses. Dengue virus, Zika virus and hepatitis C virus belong to the most prominent human pathogenic Flaviviridae With a genome of roughly ten kilobases encoding only ten viral proteins, Flaviviridae display intricate mechanisms to engage the host cell machinery for their purpose. In this review, we will highlight how dengue virus, hepatitis C virus, Japanese encephalitis virus, tick-borne encephalitis virus, West Nile virus, yellow fever virus, and Zika virus proteins engage host proteins and how this knowledge helps elucidate Flaviviridae infection. We will specifically address the protein composition of the virus particle as well as the protein interactions during virus entry, replication, particle assembly, and release from the host cell. Finally, we will give a perspective on future challenges in Flaviviridae interaction proteomics and why we believe these challenges should be met. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  10. Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information

    PubMed Central

    Lopes, Anne; Sacquin-Mora, Sophie; Dimitrova, Viktoriya; Laine, Elodie; Ponty, Yann; Carbone, Alessandra

    2013-01-01

    Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary

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

    PubMed

    Holland, David O; Johnson, Margaret E

    2018-03-01

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

  12. 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.

  13. Bacterial Ice Crystal Controlling Proteins

    PubMed Central

    Lorv, Janet S. H.; Rose, David R.; Glick, Bernard R.

    2014-01-01

    Across the world, many ice active bacteria utilize ice crystal controlling proteins for aid in freezing tolerance at subzero temperatures. Ice crystal controlling proteins include both antifreeze and ice nucleation proteins. Antifreeze proteins minimize freezing damage by inhibiting growth of large ice crystals, while ice nucleation proteins induce formation of embryonic ice crystals. Although both protein classes have differing functions, these proteins use the same ice binding mechanisms. Rather than direct binding, it is probable that these protein classes create an ice surface prior to ice crystal surface adsorption. Function is differentiated by molecular size of the protein. This paper reviews the similar and different aspects of bacterial antifreeze and ice nucleation proteins, the role of these proteins in freezing tolerance, prevalence of these proteins in psychrophiles, and current mechanisms of protein-ice interactions. PMID:24579057

  14. Quantitative analysis of protein-ligand interactions by NMR.

    PubMed

    Furukawa, Ayako; Konuma, Tsuyoshi; Yanaka, Saeko; Sugase, Kenji

    2016-08-01

    Protein-ligand interactions have been commonly studied through static structures of the protein-ligand complex. Recently, however, there has been increasing interest in investigating the dynamics of protein-ligand interactions both for fundamental understanding of the underlying mechanisms and for drug development. NMR is a versatile and powerful tool, especially because it provides site-specific quantitative information. NMR has widely been used to determine the dissociation constant (KD), in particular, for relatively weak interactions. The simplest NMR method is a chemical-shift titration experiment, in which the chemical-shift changes of a protein in response to ligand titration are measured. There are other quantitative NMR methods, but they mostly apply only to interactions in the fast-exchange regime. These methods derive the dissociation constant from population-averaged NMR quantities of the free and bound states of a protein or ligand. In contrast, the recent advent of new relaxation-based experiments, including R2 relaxation dispersion and ZZ-exchange, has enabled us to obtain kinetic information on protein-ligand interactions in the intermediate- and slow-exchange regimes. Based on R2 dispersion or ZZ-exchange, methods that can determine the association rate, kon, dissociation rate, koff, and KD have been developed. In these approaches, R2 dispersion or ZZ-exchange curves are measured for multiple samples with different protein and/or ligand concentration ratios, and the relaxation data are fitted to theoretical kinetic models. It is critical to choose an appropriate kinetic model, such as the two- or three-state exchange model, to derive the correct kinetic information. The R2 dispersion and ZZ-exchange methods are suitable for the analysis of protein-ligand interactions with a micromolar or sub-micromolar dissociation constant but not for very weak interactions, which are typical in very fast exchange. This contrasts with the NMR methods that are used

  15. Template-Based Modeling of Protein-RNA Interactions.

    PubMed

    Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A; Liu, Shiyong

    2016-09-01

    Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

  16. Template-Based Modeling of Protein-RNA Interactions

    PubMed Central

    Zheng, Jinfang; Kundrotas, Petras J.; Vakser, Ilya A.

    2016-01-01

    Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. PMID:27662342

  17. 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

  18. A protein interaction map for cell polarity development

    PubMed Central

    Drees, Becky L.; Sundin, Bryan; Brazeau, Elizabeth; Caviston, Juliane P.; Chen, Guang-Chao; Guo, Wei; Kozminski, Keith G.; Lau, Michelle W.; Moskow, John J.; Tong, Amy; Schenkman, Laura R.; McKenzie, Amos; Brennwald, Patrick; Longtine, Mark; Bi, Erfei; Chan, Clarence; Novick, Peter; Boone, Charles; Pringle, John R.; Davis, Trisha N.; Fields, Stanley; Drubin, David G.

    2001-01-01

    Many genes required for cell polarity development in budding yeast have been identified and arranged into a functional hierarchy. Core elements of the hierarchy are widely conserved, underlying cell polarity development in diverse eukaryotes. To enumerate more fully the protein–protein interactions that mediate cell polarity development, and to uncover novel mechanisms that coordinate the numerous events involved, we carried out a large-scale two-hybrid experiment. 68 Gal4 DNA binding domain fusions of yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases were used to screen an array of yeast transformants that express ∼90% of the predicted Saccharomyces cerevisiae open reading frames as Gal4 activation domain fusions. 191 protein–protein interactions were detected, of which 128 had not been described previously. 44 interactions implicated 20 previously uncharacterized proteins in cell polarity development. Further insights into possible roles of 13 of these proteins were revealed by their multiple two-hybrid interactions and by subcellular localization. Included in the interaction network were associations of Cdc42 and Rho1 pathways with proteins involved in exocytosis, septin organization, actin assembly, microtubule organization, autophagy, cytokinesis, and cell wall synthesis. Other interactions suggested direct connections between Rho1- and Cdc42-regulated pathways; the secretory apparatus and regulators of polarity establishment; actin assembly and the morphogenesis checkpoint; and the exocytic and endocytic machinery. In total, a network of interactions that provide an integrated response of signaling proteins, the cytoskeleton, and organelles to the spatial cues that direct polarity development was revealed. PMID:11489916

  19. Molecular insights into the stabilization of protein-protein interactions with small molecule: The FKBP12-rapamycin-FRB case study

    NASA Astrophysics Data System (ADS)

    Chaurasia, Shilpi; Pieraccini, Stefano; De Gonda, Riccardo; Conti, Simone; Sironi, Maurizio

    2013-11-01

    Targetting protein-protein interactions is a challenging task in drug discovery process. Despite the challenges, several studies provided evidences for the development of small molecules modulating protein-protein interactions. Here we consider a typical case of protein-protein interaction stabilization: the complex between FKBP12 and FRB with rapamycin. We have analyzed the stability of the complex and characterized its interactions at the atomic level by performing free energy calculations and computational alanine scanning. It is shown that rapamycin stabilizes the complex by acting as a bridge between the two proteins; and the complex is stable only in the presence of rapamycin.

  20. PPCM: Combing multiple classifiers to improve protein-protein interaction prediction

    DOE PAGES

    Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

    2015-08-01

    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. Ultimately, this pipeline will be useful for predicting PPI in nonmodel species.« less

  1. Characterizing protein-protein-interaction in high-concentration monoclonal antibody systems with the quartz crystal microbalance.

    PubMed

    Hartl, Josef; Peschel, Astrid; Johannsmann, Diethelm; Garidel, Patrick

    2017-12-13

    Making use of a quartz crystal microbalance (QCM), concentrated solutions of therapeutic antibodies were studied with respect to their behavior under shear excitation with frequencies in the MHz range. At high protein concentration and neutral pH, viscoelastic behavior was found in the sense that the storage modulus, G', was nonzero. Fits of the frequency dependence of G'(ω) and G''(ω) (G'' being the loss modulus) using the Maxwell-model produced good agreement with the experimental data. The fit parameters were the relaxation time, τ, and the shear modulus at the inverse relaxation time, G* (at the "cross-over frequency" ω C = 1/τ). The influence of two different pharmaceutical excipients (histidine and citrate) was studied at variable concentrations of the antibody and variable pH. In cases, where viscoelasticity was observed, G* was in the range of a few kPa, consistent with entropy-driven interactions. τ was small at low pH, where the antibody carries a positive charge. τ increased with increasing pH. The relaxation time τ was found to be correlated with other parameters quantifying protein-protein interactions, namely the steady shear viscosity (η), the second osmotic virial coefficient as determined with both self-interaction chromatography (B 22,SIC ) and static light scattering (B 22,SLS ), and the diffusion interaction parameter as determined with dynamic light scattering (k D ). While B 22 and k D describe protein-protein interactions in diluted samples, the QCM can be applied to concentrated solutions, thereby being sensitive to higher-order protein-protein interactions.

  2. Capturing the Interaction Potential of Amyloidogenic Proteins

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Javid, Nadeem; Vogtt, Karsten; Winter, Roland

    2007-07-13

    Experimentally derived static structure factors obtained for the aggregation-prone protein insulin were analyzed with a statistical mechanical model based on the Derjaguin-Landau-Verwey-Overbeek potential. The data reveal that the protein self-assembles into equilibrium clusters already at low concentrations. Furthermore, striking differences regarding interaction forces between aggregation-prone proteins such as insulin in the preaggregated regime and natively stable globular proteins are found.

  3. The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis.

    PubMed

    Mao, Yimin; Kuo, Su-Wei; Chen, Le; Heckman, C J; Jiang, M C

    2017-01-01

    Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.

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

    DTIC Science & Technology

    2011-01-01

    may partially explain why we did not observe any of the interactions between RNA polymerase II compo- nents in any of the Y2H set (11). Methodological...DNA. Fig. 5 shows that RNA syn- thesis complexes formed a highly interconnected cluster, in- cluding RNA polymerases I, II , and III, Transcription...factor complexes II F (TFIIF) and III C (TFIIIC), which were connected via direct protein-protein interactions with many other func- tional complexes. Fig

  5. Combining random gene fission and rational gene fusion to discover near-infrared fluorescent protein fragments that report on protein-protein interactions.

    PubMed

    Pandey, Naresh; Nobles, Christopher L; Zechiedrich, Lynn; Maresso, Anthony W; Silberg, Jonathan J

    2015-05-15

    Gene fission can convert monomeric proteins into two-piece catalysts, reporters, and transcription factors for systems and synthetic biology. However, some proteins can be challenging to fragment without disrupting function, such as near-infrared fluorescent protein (IFP). We describe a directed evolution strategy that can overcome this challenge by randomly fragmenting proteins and concomitantly fusing the protein fragments to pairs of proteins or peptides that associate. We used this method to create libraries that express fragmented IFP as fusions to a pair of associating peptides (IAAL-E3 and IAAL-K3) and proteins (CheA and CheY) and screened for fragmented IFP with detectable near-infrared fluorescence. Thirteen novel fragmented IFPs were identified, all of which arose from backbone fission proximal to the interdomain linker. Either the IAAL-E3 and IAAL-K3 peptides or CheA and CheY proteins could assist with IFP fragment complementation, although the IAAL-E3 and IAAL-K3 peptides consistently yielded higher fluorescence. These results demonstrate how random gene fission can be coupled to rational gene fusion to create libraries enriched in fragmented proteins with AND gate logic that is dependent upon a protein-protein interaction, and they suggest that these near-infrared fluorescent protein fragments will be suitable as reporters for pairs of promoters and protein-protein interactions within whole animals.

  6. The amyloid interactome: Exploring protein aggregation

    PubMed Central

    Mastrokalou, Chara V.; Hamodrakas, Stavros J.

    2017-01-01

    Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders. PMID:28249044

  7. Identification of proteins that may directly interact with human RPA.

    PubMed

    Nakaya, Ryou; Takaya, Junichiro; Onuki, Takeshi; Moritani, Mariko; Nozaki, Naohito; Ishimi, Yukio

    2010-11-01

    RPA, which consisted of three subunits (RPA1, 2 and 3), plays essential roles in DNA transactions. At the DNA replication forks, RPA binds to single-stranded DNA region to stabilize the structure and to assemble other replication proteins. Interactions between RPA and several replication proteins have been reported but the analysis is not comprehensive. We systematically performed the qualitative analysis to identify RPA interaction partners to understand the protein-protein interaction at the replication forks. We expressed in insect cells the three subunits of human RPA, together with one replication protein, which is present at the forks under normal conditions and/or under the replication stress conditions, to examine the interaction. Among 30 proteins examined in total, it was found that at least 14 proteins interacted with RPA. RPA interacted with MCM3-7, MCM-BP and CDC45 proteins among the proteins that play roles in the initiation and the elongation of the DNA replication. RPA bound with TIPIN, CLASPIN and RAD17, which are involved in the DNA replication checkpoint functions. RPA also bound with cyclin-dependent kinases and an amino-terminal fragment of Rb protein that negatively regulates DNA replication. These results suggest that RPA interacts with the specific proteins among those that play roles in the regulation of the replication fork progression.

  8. Carbohydrate-protein interactions: molecular modeling insights.

    PubMed

    Pérez, Serge; Tvaroška, Igor

    2014-01-01

    The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses. © 2014 Elsevier Inc. All rights reserved.

  9. Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis.

    PubMed

    Bai, Fang; Morcos, Faruck; Cheng, Ryan R; Jiang, Hualiang; Onuchic, José N

    2016-12-13

    Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

  10. Multiplex single-molecule interaction profiling of DNA-barcoded proteins.

    PubMed

    Gu, Liangcai; Li, Chao; Aach, John; Hill, David E; Vidal, Marc; Church, George M

    2014-11-27

    In contrast with advances in massively parallel DNA sequencing, high-throughput protein analyses are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule protein detection using optical methods is limited by the number of spectrally non-overlapping chromophores. Here we introduce a single-molecular-interaction sequencing (SMI-seq) technology for parallel protein interaction profiling leveraging single-molecule advantages. DNA barcodes are attached to proteins collectively via ribosome display or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide thin film to construct a random single-molecule array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies) and analysed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimetre. Furthermore, protein interactions can be measured on the basis of the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor and antibody-binding profiling, are demonstrated. SMI-seq enables 'library versus library' screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity.

  11. Tuning of protein-surfactant interaction to modify the resultant structure.

    PubMed

    Mehan, Sumit; Aswal, Vinod K; Kohlbrecher, Joachim

    2015-09-01

    Small-angle neutron scattering and dynamic light scattering studies have been carried out to examine the interaction of bovine serum albumin (BSA) protein with different surfactants under varying solution conditions. We show that the interaction of anionic BSA protein (pH7) with surfactant and the resultant structure are strongly modified by the charge head group of the surfactant, ionic strength of the solution, and mixed surfactants. The protein-surfactant interaction is maximum when two components are oppositely charged, followed by components being similarly charged through the site-specific binding, and no interaction in the case of a nonionic surfactant. This interaction of protein with ionic surfactants is characterized by the fractal structure representing a bead-necklace structure of micellelike clusters adsorbed along the unfolded protein chain. The interaction is enhanced with ionic strength only in the case of site-specific binding of an anionic surfactant with an anionic protein, whereas it is almost unchanged for other complexes of cationic and nonionic surfactants with anionic proteins. Interestingly, the interaction of BSA protein with ionic surfactants is significantly suppressed in the presence of nonionic surfactant. These results with mixed surfactants thus can be used to fold back the unfolded protein as well as to prevent surfactant-induced protein unfolding. For different solution conditions, the results are interpreted in terms of a change in fractal dimension, the overall size of the protein-surfactant complex, and the number of micelles attached to the protein. The interplay of electrostatic and hydrophobic interactions is found to govern the resultant structure of complexes.

  12. Tuning of protein-surfactant interaction to modify the resultant structure

    NASA Astrophysics Data System (ADS)

    Mehan, Sumit; Aswal, Vinod K.; Kohlbrecher, Joachim

    2015-09-01

    Small-angle neutron scattering and dynamic light scattering studies have been carried out to examine the interaction of bovine serum albumin (BSA) protein with different surfactants under varying solution conditions. We show that the interaction of anionic BSA protein (p H 7 ) with surfactant and the resultant structure are strongly modified by the charge head group of the surfactant, ionic strength of the solution, and mixed surfactants. The protein-surfactant interaction is maximum when two components are oppositely charged, followed by components being similarly charged through the site-specific binding, and no interaction in the case of a nonionic surfactant. This interaction of protein with ionic surfactants is characterized by the fractal structure representing a bead-necklace structure of micellelike clusters adsorbed along the unfolded protein chain. The interaction is enhanced with ionic strength only in the case of site-specific binding of an anionic surfactant with an anionic protein, whereas it is almost unchanged for other complexes of cationic and nonionic surfactants with anionic proteins. Interestingly, the interaction of BSA protein with ionic surfactants is significantly suppressed in the presence of nonionic surfactant. These results with mixed surfactants thus can be used to fold back the unfolded protein as well as to prevent surfactant-induced protein unfolding. For different solution conditions, the results are interpreted in terms of a change in fractal dimension, the overall size of the protein-surfactant complex, and the number of micelles attached to the protein. The interplay of electrostatic and hydrophobic interactions is found to govern the resultant structure of complexes.

  13. Using Förster-Resonance Energy Transfer to Measure Protein Interactions Between Bcl-2 Family Proteins on Mitochondrial Membranes.

    PubMed

    Pogmore, Justin P; Pemberton, James M; Chi, Xiaoke; Andrews, David W

    2016-01-01

    The Bcl-2 family of proteins regulates the process of mitochondrial outer membrane permeabilization, causing the release of cytochrome c and committing a cell to apoptosis. The majority of the functional interactions between these proteins occur at, on, or within the mitochondrial outer membrane, complicating structural studies of the proteins and complexes. As a result most in vitro studies of these protein-protein interactions use truncated proteins and/or detergents which can cause artificial interactions. Herein, we describe a detergent-free, fluorescence-based, in vitro technique to study binding between full-length recombinant Bcl-2 family proteins, particularly cleaved BID (cBID) and BCL-XL, on the membranes of purified mitochondria.

  14. Interaction between Vaccinium bracteatum Thunb. leaf pigment and rice proteins.

    PubMed

    Wang, Li; Xu, Yuan; Zhou, Sumei; Qian, Haifeng; Zhang, Hui; Qi, Xiguang; Fan, Meihua

    2016-03-01

    In this study, we investigated the interaction of Vaccinium bracteatum Thunb. leaf (VBTL) pigment and rice proteins. In the presence of rice protein, VBTL pigment antioxidant activity and free polyphenol content decreased by 67.19% and 68.11%, respectively, and L(∗) of the protein-pigment complex decreased significantly over time. L(∗) values of albumin, globulin and glutelin during 60-min pigment exposure decreased by 55.00, 57.14, and 54.30%, respectively, indicating that these proteins had bound to the pigment. A significant difference in protein surface hydrophobicity was observed between rice proteins and pigment-protein complexes, indicating that hydrophobic interaction is a major binding mechanism between VBTL pigment and rice proteins. A significant difference in secondary structures between proteins and protein-pigment complexes was also uncovered, indicating that hydrogen bonding may be another mode of interaction between VBTL pigment and rice proteins. Our results indicate that VBTL pigment can stain rice proteins with hydrophobic and hydrogen interactions. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties*

    PubMed Central

    Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

    2013-01-01

    Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This

  16. Protein Interactions during the Flavivirus and Hepacivirus Life Cycle*

    PubMed Central

    Bruening, Janina; Weigel, Bettina; Pietschmann, Thomas

    2017-01-01

    Protein–protein interactions govern biological functions in cells, in the extracellular milieu, and at the border between cells and extracellular space. Viruses are small intracellular parasites and thus rely on protein interactions to produce progeny inside host cells and to spread from cell to cell. Usage of host proteins by viruses can have severe consequences e.g. apoptosis, metabolic disequilibria, or altered cell proliferation and mobility. Understanding protein interactions during virus infection can thus educate us on viral infection and pathogenesis mechanisms. Moreover, it has led to important clinical translations, including the development of new therapeutic and vaccination strategies. Here, we will discuss protein interactions of members of the Flaviviridae family, which are small enveloped RNA viruses. Dengue virus, Zika virus and hepatitis C virus belong to the most prominent human pathogenic Flaviviridae. With a genome of roughly ten kilobases encoding only ten viral proteins, Flaviviridae display intricate mechanisms to engage the host cell machinery for their purpose. In this review, we will highlight how dengue virus, hepatitis C virus, Japanese encephalitis virus, tick-borne encephalitis virus, West Nile virus, yellow fever virus, and Zika virus proteins engage host proteins and how this knowledge helps elucidate Flaviviridae infection. We will specifically address the protein composition of the virus particle as well as the protein interactions during virus entry, replication, particle assembly, and release from the host cell. Finally, we will give a perspective on future challenges in Flaviviridae interaction proteomics and why we believe these challenges should be met. PMID:28077444

  17. Structural study of surfactant-dependent interaction with protein

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Mehan, Sumit; Aswal, Vinod K., E-mail: vkaswal@barc.gov.in; Kohlbrecher, Joachim

    2015-06-24

    Small-angle neutron scattering (SANS) has been used to study the complex structure of anionic BSA protein with three different (cationic DTAB, anionic SDS and non-ionic C12E10) surfactants. These systems form very different surfactant-dependent complexes. We show that the structure of protein-surfactant complex is initiated by the site-specific electrostatic interaction between the components, followed by the hydrophobic interaction at high surfactant concentrations. It is also found that hydrophobic interaction is preferred over the electrostatic interaction in deciding the resultant structure of protein-surfactant complexes.

  18. Structural study of surfactant-dependent interaction with protein

    NASA Astrophysics Data System (ADS)

    Mehan, Sumit; Aswal, Vinod K.; Kohlbrecher, Joachim

    2015-06-01

    Small-angle neutron scattering (SANS) has been used to study the complex structure of anionic BSA protein with three different (cationic DTAB, anionic SDS and non-ionic C12E10) surfactants. These systems form very different surfactant-dependent complexes. We show that the structure of protein-surfactant complex is initiated by the site-specific electrostatic interaction between the components, followed by the hydrophobic interaction at high surfactant concentrations. It is also found that hydrophobic interaction is preferred over the electrostatic interaction in deciding the resultant structure of protein-surfactant complexes.

  19. Prediction and functional analysis of the sweet orange protein-protein interaction network.

    PubMed

    Ding, Yu-Duan; Chang, Ji-Wei; Guo, Jing; Chen, Dijun; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Cheng, Yun-Jiang; Chen, Ling-Ling

    2014-08-05

    Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

  20. 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.

  1. Role of electrostatic interactions during protein ultrafiltration.

    PubMed

    Rohani, Mahsa M; Zydney, Andrew L

    2010-10-15

    A number of studies over the last decade have clearly demonstrated the importance of electrostatic interactions on the transport of charged proteins through semipermeable ultrafiltration membranes. This paper provides a review of recent developments in this field with a focus on the role of both protein and membrane charge on the rate of protein transport. Experimental results are analyzed using available theoretical models developed from the solution of the Poisson-Boltzmann equation for the partitioning of a charged particle into a charged pore. The potential of exploiting these electrostatic interactions for selective protein separations and for the development of ultrafiltration membranes with enhanced performance characteristics is also examined. Copyright © 2010 Elsevier B.V. All rights reserved.

  2. BRCA1 interacts directly with the Fanconi anemia protein FANCA.

    PubMed

    Folias, Alexandra; Matkovic, Mara; Bruun, Donald; Reid, Sonja; Hejna, James; Grompe, Markus; D'Andrea, Alan; Moses, Robb

    2002-10-01

    Fanconi anemia (FA) is a rare autosomal recessive disease characterized by skeletal defects, anemia, chromosomal instability and increased risk of leukemia. At the cellular level FA is characterized by increased sensitivity to agents forming interstrand crosslinks (ICL) in DNA. Six FA genes have been cloned and interactions among individual FANC proteins have been found. The FANCD2 protein co-localizes in nuclear foci with the BRCA1 protein following DNA damage and during S-phase, requiring the FANCA, C, E and G proteins to do so. This finding may reflect a direct role for the BRCA1 protein in double strand break (DSB) repair and interaction with the FANC proteins. Therefore interactions between BRCA1 and the FANC proteins were investigated. Among the known FANC proteins, we find evidence for direct interaction only between the FANCA protein and BRCA1. The evidence rests on three different tests: yeast two-hybrid analysis, coimmunoprecipitation from in vitro synthesis, and coimmunoprecipitation from cell extracts. The amino terminal portion of FANCA and the central part (aa 740-1083) of BRCA1 contain the sites of interaction. The interaction does not depend on DNA damage, thus FANCA and BRCA1 are constitutively interacting. The demonstrated interaction directly connects BRCA1 to the FA pathway of DNA repair.

  3. Predicting protein interactions by Brownian dynamics simulations.

    PubMed

    Meng, Xuan-Yu; Xu, Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng

    2012-01-01

    We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

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

    PubMed Central

    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

    2015-01-01

    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 crosslink 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 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 crosslinking to produce synergistic benefits. PMID:26317395

  5. 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.

  6. Studies of Dynamic Protein-Protein Interactions in Bacteria Using Renilla Luciferase Complementation Are Undermined by Nonspecific Enzyme Inhibition

    PubMed Central

    Hatzios, Stavroula K.; Ringgaard, Simon; Davis, Brigid M.; Waldor, Matthew K.

    2012-01-01

    The luciferase protein fragment complementation assay is a powerful tool for studying protein-protein interactions. Two inactive fragments of luciferase are genetically fused to interacting proteins, and when these two proteins interact, the luciferase fragments can reversibly associate and reconstitute enzyme activity. Though this technology has been used extensively in live eukaryotic cells, split luciferase complementation has not yet been applied to studies of dynamic protein-protein interactions in live bacteria. As proof of concept and to develop a new tool for studies of bacterial chemotaxis, fragments of Renilla luciferase (Rluc) were fused to the chemotaxis-associated response regulator CheY3 and its phosphatase CheZ in the enteric pathogen Vibrio cholerae. Luciferase activity was dependent on the presence of both CheY3 and CheZ fusion proteins, demonstrating the specificity of the assay. Furthermore, enzyme activity was markedly reduced in V. cholerae chemotaxis mutants, suggesting that this approach can measure defects in chemotactic signaling. However, attempts to measure changes in dynamic CheY3-CheZ interactions in response to various chemoeffectors were undermined by nonspecific inhibition of the full-length luciferase. These observations reveal an unexpected limitation of split Rluc complementation that may have implications for existing data and highlight the need for great caution when evaluating small molecule effects on dynamic protein-protein interactions using the split luciferase technology. PMID:22905225

  7. Studies of dynamic protein-protein interactions in bacteria using Renilla luciferase complementation are undermined by nonspecific enzyme inhibition.

    PubMed

    Hatzios, Stavroula K; Ringgaard, Simon; Davis, Brigid M; Waldor, Matthew K

    2012-01-01

    The luciferase protein fragment complementation assay is a powerful tool for studying protein-protein interactions. Two inactive fragments of luciferase are genetically fused to interacting proteins, and when these two proteins interact, the luciferase fragments can reversibly associate and reconstitute enzyme activity. Though this technology has been used extensively in live eukaryotic cells, split luciferase complementation has not yet been applied to studies of dynamic protein-protein interactions in live bacteria. As proof of concept and to develop a new tool for studies of bacterial chemotaxis, fragments of Renilla luciferase (Rluc) were fused to the chemotaxis-associated response regulator CheY3 and its phosphatase CheZ in the enteric pathogen Vibrio cholerae. Luciferase activity was dependent on the presence of both CheY3 and CheZ fusion proteins, demonstrating the specificity of the assay. Furthermore, enzyme activity was markedly reduced in V. cholerae chemotaxis mutants, suggesting that this approach can measure defects in chemotactic signaling. However, attempts to measure changes in dynamic CheY3-CheZ interactions in response to various chemoeffectors were undermined by nonspecific inhibition of the full-length luciferase. These observations reveal an unexpected limitation of split Rluc complementation that may have implications for existing data and highlight the need for great caution when evaluating small molecule effects on dynamic protein-protein interactions using the split luciferase technology.

  8. Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

    PubMed

    Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

    2015-12-01

    Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Protein-protein interactions and metabolite channelling in the plant tricarboxylic acid cycle

    PubMed Central

    Zhang, Youjun; Beard, Katherine F. M.; Swart, Corné; Bergmann, Susan; Krahnert, Ina; Nikoloski, Zoran; Graf, Alexander; Ratcliffe, R. George; Sweetlove, Lee J.; Fernie, Alisdair R.; Obata, Toshihiro

    2017-01-01

    Protein complexes of sequential metabolic enzymes, often termed metabolons, may permit direct channelling of metabolites between the enzymes, providing increased control over metabolic pathway fluxes. Experimental evidence supporting their existence in vivo remains fragmentary. In the present study, we test binary interactions of the proteins constituting the plant tricarboxylic acid (TCA) cycle. We integrate (semi-)quantitative results from affinity purification-mass spectrometry, split-luciferase and yeast-two-hybrid assays to generate a single reliability score for assessing protein–protein interactions. By this approach, we identify 158 interactions including those between catalytic subunits of sequential enzymes and between subunits of enzymes mediating non-adjacent reactions. We reveal channelling of citrate and fumarate in isolated potato mitochondria by isotope dilution experiments. These results provide evidence for a functional TCA cycle metabolon in plants, which we discuss in the context of contemporary understanding of this pathway in other kingdoms. PMID:28508886

  10. Trapping of the Enoyl-Acyl Carrier Protein Reductase–Acyl Carrier Protein Interaction

    PubMed Central

    Tallorin, Lorillee; Finzel, Kara; Nguyen, Quynh G.; Beld, Joris; La Clair, James J.; Burkart, Michael D.

    2016-01-01

    An ideal target for metabolic engineering, fatty acid biosynthesis remains poorly understood on a molecular level. These carrier protein-dependent pathways require fundamental protein–protein interactions to guide reactivity and processivity, and their control has become one of the major hurdles in successfully adapting these biological machines. Our laboratory has developed methods to prepare acyl carrier proteins (ACPs) loaded with substrate mimetics and cross-linkers to visualize and trap interactions with partner enzymes, and we continue to expand the tools for studying these pathways. We now describe application of the slow-onset, tight-binding inhibitor triclosan to explore the interactions between the type II fatty acid ACP from Escherichia coli, AcpP, and its corresponding enoyl-ACP reductase, FabI. We show that the AcpP–triclosan complex demonstrates nM binding, inhibits in vitro activity, and can be used to isolate FabI in complex proteomes. PMID:26938266

  11. Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.

    PubMed

    Alvarez-Ponce, David; Feyertag, Felix; Chakraborty, Sandip

    2017-06-01

    The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  12. Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

    PubMed Central

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W. V

    2007-01-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information. PMID:17892321

  13. 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.

  14. Split luciferase complementation assay to detect regulated protein-protein interactions in rice protoplasts in a large-scale format

    PubMed Central

    2014-01-01

    Background The rice interactome, in which a network of protein-protein interactions has been elucidated in rice, is a useful resource to identify functional modules of rice signal transduction pathways. Protein-protein interactions occur in cells in two ways, constitutive and regulative. While a yeast-based high-throughput method has been widely used to identify the constitutive interactions, a method to detect the regulated interactions is rarely developed for a large-scale analysis. Results A split luciferase complementation assay was applied to detect the regulated interactions in rice. A transformation method of rice protoplasts in a 96-well plate was first established for a large-scale analysis. In addition, an antibody that specifically recognizes a carboxyl-terminal fragment of Renilla luciferase was newly developed. A pair of antibodies that recognize amino- and carboxyl- terminal fragments of Renilla luciferase, respectively, was then used to monitor quality and quantity of interacting recombinant-proteins accumulated in the cells. For a proof-of-concept, the method was applied to detect the gibberellin-dependent interaction between GIBBERELLIN INSENSITIVE DWARF1 and SLENDER RICE 1. Conclusions A method to detect regulated protein-protein interactions was developed towards establishment of the rice interactome. PMID:24987490

  15. "Plasmo2D": an ancillary proteomic tool to aid identification of proteins from Plasmodium falciparum.

    PubMed

    Khachane, Amit; Kumar, Ranjit; Jain, Sanyam; Jain, Samta; Banumathy, Gowrishankar; Singh, Varsha; Nagpal, Saurabh; Tatu, Utpal

    2005-01-01

    Bioinformatics tools to aid gene and protein sequence analysis have become an integral part of biology in the post-genomic era. Release of the Plasmodium falciparum genome sequence has allowed biologists to define the gene and the predicted protein content as well as their sequences in the parasite. Using pI and molecular weight as characteristics unique to each protein, we have developed a bioinformatics tool to aid identification of proteins from Plasmodium falciparum. The tool makes use of a Virtual 2-DE generated by plotting all of the proteins from the Plasmodium database on a pI versus molecular weight scale. Proteins are identified by comparing the position of migration of desired protein spots from an experimental 2-DE and that on a virtual 2-DE. The procedure has been automated in the form of user-friendly software called "Plasmo2D". The tool can be downloaded from http://144.16.89.25/Plasmo2D.zip.

  16. Flavin-Induced Oligomerization in Escherichia coli Adaptive Response Protein AidB

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hamill, Michael J.; Jost, Marco; Wong, Cintyu

    2011-11-21

    The process known as 'adaptive response' allows Escherichia coli to respond to small doses of DNA-methylating agents by upregulating the expression of four proteins. While the role of three of these proteins in mitigating DNA damage is well understood, the function of AidB is less clear. Although AidB is a flavoprotein, no catalytic role has been established for the bound cofactor. Here we investigate the possibility that flavin plays a structural role in the assembly of the AidB tetramer. We report the generation and biophysical characterization of deflavinated AidB and of an AidB mutant that has greatly reduced affinity formore » flavin adenine dinucleotide (FAD). Using fluorescence quenching and analytical ultracentrifugation, we find that apo AidB has a high affinity for FAD, as indicated by an apparent dissociation constant of 402.1 {+-} 35.1 nM, and that binding of substoichiometric amounts of FAD triggers a transition in the AidB oligomeric state. In particular, deflavinated AidB is dimeric, whereas the addition of FAD yields a tetramer. We further investigate the dimerization and tetramerization interfaces of AidB by determining a 2.8 {angstrom} resolution crystal structure in space group P3{sub 2} that contains three intact tetramers in the asymmetric unit. Taken together, our findings provide strong evidence that FAD plays a structural role in the formation of tetrameric AidB.« less

  17. Amphipathic helical peptides hamper protein-protein interactions of the intrinsically disordered chromatin nuclear protein 1 (NUPR1).

    PubMed

    Santofimia-Castaño, Patricia; Rizzuti, Bruno; Abián, Olga; Velázquez-Campoy, Adrián; Iovanna, Juan L; Neira, José L

    2018-06-01

    NUPR1 is a multifunctional intrinsically disordered protein (IDP) involved, among other functions, in chromatin remodelling, and development of pancreatic ductal adenocarcinoma (PDAC). It interacts with several biomolecules through hydrophobic patches around residues Ala33 and Thr68. The drug trifluoperazine (TFP), which hampers PDAC development in xenografted mice, also binds to those regions. Because of the large size of the hot-spot interface of NUPR1, small molecules could not be adequate to modulate its functions. We explored how amphipathic helical-designed peptides were capable of interacting with wild-type NUPR1 and the Thr68Gln mutant, inhibiting the interaction with NUPR1 protein partners. We used in vitro biophysical techniques (fluorescence, circular dichroism (CD), nuclear magnetic resonance (NMR) and isothermal titration calorimetry (ITC)), in silico studies (docking and molecular dynamics (MD)), and in cellulo protein ligation assays (PLAs) to study the interaction. Peptide dissociation constants towards wild-type NUPR1 were ~ 3 μM, whereas no interaction was observed with the Thr68Gln mutant. Peptides interacted with wild-type NUPR1 residues around Ala33 and residues at the C terminus, as shown by NMR. The computational results clarified the main determinants of the interactions, providing a mechanism for the ligand-capture that explains why peptide binding was not observed for Thr68Gln mutant. Finally, the in cellulo assays indicated that two out of four peptides inhibited the interaction of NUPR1 with the C-terminal region of the Polycomb RING protein 1 (C-RING1B). Designed peptides can be used as lead compounds to inhibit NUPR1 interactions. Peptides may be exploited as drugs to target IDPs. Copyright © 2018 Elsevier B.V. All rights reserved.

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

    PubMed Central

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

    2011-01-01

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

  19. Proof of concept of a "greener" protein purification/enrichment method based on carboxylate-terminated carbosilane dendrimer-protein interactions.

    PubMed

    González-García, Estefanía; Maly, Marek; de la Mata, Francisco Javier; Gómez, Rafael; Marina, María Luisa; García, María Concepción

    2016-11-01

    Protein sample preparation is a critical and an unsustainable step since it involves the use of tedious methods that usually require high amount of solvents. The development of new materials offers additional opportunities in protein sample preparation. This work explores, for the first time, the potential application of carboxylate-terminated carbosilane dendrimers to the purification/enrichment of proteins. Studies on dendrimer binding to proteins, based on protein fluorescence intensity and emission wavelengths measurements, demonstrated the interaction between carboxylate-terminated carbosilane dendrimers and proteins at all tested pH levels. Interactions were greatly affected by the protein itself, pH, and dendrimer concentration and generation. Especially interesting was the interaction at acidic pH since it resulted in a significant protein precipitation. Dendrimer-protein interactions were modeled observing stable complexes for all proteins. Carboxylate-terminated carbosilane dendrimers at acidic pH were successfully used in the purification/enrichment of proteins extracted from a complex sample. Graphical Abstract Images showing the growing turbidity of solutions containing a mixture of proteins (lysozyme, myoglobin, and BSA) at different protein:dendrimer ratios (1:0, 1:1, 1:8, and 1:20) at acidic pH and SDS-PAGE profiles of the corresponsing supernatants. Comparison of SDS-PAGE profiles for the pellets obtained during the purification of proteins present in a complex sample using a conventional "no-clean" method based on acetone precipitation and the proposed "greener" method using carboxylate-terminated carbosilane dendrimer at a 1:20 protein:dendrimer ratio.

  20. Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis.

    PubMed

    Mahajan, Gaurang; Mande, Shekhar C

    2017-04-04

    A comprehensive map of the human-M. tuberculosis (MTB) protein interactome would help fill the gaps in our understanding of the disease, and computational prediction can aid and complement experimental studies towards this end. Several sequence-based in silico approaches tap the existing data on experimentally validated protein-protein interactions (PPIs); these PPIs serve as templates from which novel interactions between pathogen and host are inferred. Such comparative approaches typically make use of local sequence alignment, which, in the absence of structural details about the interfaces mediating the template interactions, could lead to incorrect inferences, particularly when multi-domain proteins are involved. We propose leveraging the domain-domain interaction (DDI) information in PDB complexes to score and prioritize candidate PPIs between host and pathogen proteomes based on targeted sequence-level comparisons. Our method picks out a small set of human-MTB protein pairs as candidates for physical interactions, and the use of functional meta-data suggests that some of them could contribute to the in vivo molecular cross-talk between pathogen and host that regulates the course of the infection. Further, we present numerical data for Pfam domain families that highlights interaction specificity on the domain level. Not every instance of a pair of domains, for which interaction evidence has been found in a few instances (i.e. structures), is likely to functionally interact. Our sorting approach scores candidates according to how "distant" they are in sequence space from known examples of DDIs (templates). Thus, it provides a natural way to deal with the heterogeneity in domain-level interactions. Our method represents a more informed application of local alignment to the sequence-based search for potential human-microbial interactions that uses available PPI data as a prior. Our approach is somewhat limited in its sensitivity by the restricted size and

  1. Detection and localisation of protein-protein interactions in Saccharomyces cerevisiae using a split-GFP method.

    PubMed

    Barnard, Emma; McFerran, Neil V; Trudgett, Alan; Nelson, John; Timson, David J

    2008-05-01

    An alternative method for monitoring protein-protein interactions in Saccharomyces cerevisiae has been developed. It relies on the ability of two fragments of enhanced green fluorescent protein (EGFP) to reassemble and fluoresce when fused to interacting proteins. Since this fluorescence can be detected in living cells, simultaneous detection and localisation of interacting pairs is possible. DNA sequences encoding N- and C-terminal EGFP fragments flanked by sequences from the genes of interest were transformed into S. cerevisiae JPY5 cells and homologous recombination into the genome verified by PCR. The system was evaluated by testing known interacting proteins: labelling of the phosphofructokinase subunits, Pfk1p and Pfk2p, with N- and C-terminal EGFP fragments, respectively, resulted in green fluorescence in the cytoplasm. The system works in other cellular compartments: labelling of Idh1p and Idh2p (mitochondrial matrix), Sdh3p and Sdh4p (mitochondrial membrane) and Pap2p and Mtr4p (nucleus) all resulted in fluorescence in the appropriate cellular compartment.

  2. RHIM-based protein:protein interactions in anti-microbial defence against programmed cell death by necroptosis.

    PubMed

    Baker, Max O D G; Shanmugam, Nirukshan; Pham, Chi L L; Strange, Merryn; Steain, Megan; Sunde, Margaret

    2018-05-05

    The Receptor-interacting protein kinase Homotypic Interaction Motif (RHIM) is an amino acid sequence that mediates multiple protein:protein interactions in the mammalian programmed cell death pathway known as necroptosis. At least one key RHIM-based complex has been shown to have a functional amyloid fibril structure, which provides a stable hetero-oligomeric platform for downstream signaling. RHIMs and related motifs are present in immunity-related proteins across nature, from viruses to fungi to metazoans. Necroptosis is a hallmark feature of cellular clearance of infection. For this reason, numerous pathogens, including viruses and bacteria, have developed varied methods to modulate necroptosis, focusing on inhibiting RHIM:RHIM interactions, and thus their downstream cell death effects. This review will discuss current understanding of RHIM:RHIM interactions in normal cellular activation of necroptosis, from a structural and cell biology perspective. It will compare the mechanisms by which pathogens subvert these interactions in order to maintain their replicative and infective cycles and consider the similarities between RHIMs and other functional amyloid-forming proteins associated with cell death and innate immunity. It will discuss the implications of the heteromeric nature and structure of RHIM-based amyloid complexes in the context of other functional amyloids. Copyright © 2018. Published by Elsevier Ltd.

  3. Using neighborhood cohesiveness to infer interactions between protein domains.

    PubMed

    Segura, Joan; Sorzano, C O S; Cuenca-Alba, Jesus; Aloy, Patrick; Carazo, J M

    2015-08-01

    In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps, or interactomes, for a number of relevant organisms, including human. However, current interactomes provide a somehow limited picture of the molecular details involving protein interactions, mostly because essential experimental information, especially structural data, is lacking. Indeed, the gap between structural and interactomics information is enlarging and thus, for most interactions, key experimental information is missing. We elaborate on the observation that many interactions between proteins involve a pair of their constituent domains and, thus, the knowledge of how protein domains interact adds very significant information to any interactomic analysis. In this work, we describe a novel use of the neighborhood cohesiveness property to infer interactions between protein domains given a protein interaction network. We have shown that some clustering coefficients can be extended to measure a degree of cohesiveness between two sets of nodes within a network. Specifically, we used the meet/min coefficient to measure the proportion of interacting nodes between two sets of nodes and the fraction of common neighbors. This approach extends previous works where homolog coefficients were first defined around network nodes and later around edges. The proposed approach substantially increases both the number of predicted domain-domain interactions as well as its accuracy as compared with current methods. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

    PubMed Central

    2018-01-01

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

  5. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    PubMed

    Jelínek, Jan; Škoda, Petr; Hoksza, David

    2017-12-06

    Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

  6. Optimization of protein-protein docking for predicting Fc-protein interactions.

    PubMed

    Agostino, Mark; Mancera, Ricardo L; Ramsland, Paul A; Fernández-Recio, Juan

    2016-11-01

    The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc. Copyright © 2016 John Wiley & Sons, Ltd.

  7. Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

    PubMed Central

    Johnson, David K.; Karanicolas, John

    2016-01-01

    Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased “pocket optimization” simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its “exemplar”: a perfect, but non-physical, pseudo-ligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 minutes on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables

  8. Characterization of the interactions between protein and carbon black.

    PubMed

    Chen, Tzu-Tao; Chuang, Kai-Jen; Chiang, Ling-Ling; Chen, Chun-Chao; Yeh, Chi-Tai; Wang, Liang-Shun; Gregory, Clive; Jones, Tim; BéruBé, Kelly; Lee, Chun-Nin; Chuang, Hsiao-Chi; Cheng, Tsun-Jen

    2014-01-15

    A considerable amount of studies have been conducted to investigate the interactions of biological fluids with nanoparticle surfaces, which exhibit a high affinity for proteins and particles. However, the mechanisms underlying these interactions have not been elucidated, particularly as they relate to human health. Using bovine serum albumin (BSA) and mice bronchoalveolar lavage fluid (BALF) as models for protein-particle conjugates, we characterized the physicochemical modifications of carbon blacks (CB) with 23nm or 65nm in diameter after protein treatment. Adsorbed BALF-containing proteins were quantified and identified by pathways, biological analyses and protein classification. Significant modifications of the physicochemistry of CB were induced by the addition of BSA. Enzyme modulators and hydrolase predominately interacted with CB, with protein-to-CB interactions that were associated with the coagulation pathways. Additionally, our results revealed that an acute-phase response could be activated by these proteins. With regard to human health, the present study revealed that the CB can react with proteins (∼55kDa and 70kDa) after inhalation and may modify the functional structures of lung proteins, leading to the activation of acute-inflammatory responses in the lungs. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Multiplex single-molecule interaction profiling of DNA barcoded proteins

    PubMed Central

    Gu, Liangcai; Li, Chao; Aach, John; Hill, David E.; Vidal, Marc; Church, George M.

    2014-01-01

    In contrast with advances in massively parallel DNA sequencing1, high-throughput protein analyses2-4 are often limited by ensemble measurements, individual analyte purification and hence compromised quality and cost-effectiveness. Single-molecule (SM) protein detection achieved using optical methods5 is limited by the number of spectrally nonoverlapping chromophores. Here, we introduce a single molecular interaction-sequencing (SMI-Seq) technology for parallel protein interaction profiling leveraging SM advantages. DNA barcodes are attached to proteins collectively via ribosome display6 or individually via enzymatic conjugation. Barcoded proteins are assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide (PAA) thin film to construct a random SM array, where barcoding DNAs are amplified into in situ polymerase colonies (polonies)7 and analyzed by DNA sequencing. This method allows precise quantification of various proteins with a theoretical maximum array density of over one million polonies per square millimeter. Furthermore, protein interactions can be measured based on the statistics of colocalized polonies arising from barcoding DNAs of interacting proteins. Two demanding applications, G-protein coupled receptor (GPCR) and antibody binding profiling, were demonstrated. SMI-Seq enables “library vs. library” screening in a one-pot assay, simultaneously interrogating molecular binding affinity and specificity. PMID:25252978

  10. Free-Energy Landscape of Protein-Ligand Interactions Coupled with Protein Structural Changes.

    PubMed

    Moritsugu, Kei; Terada, Tohru; Kidera, Akinori

    2017-02-02

    Protein-ligand interactions are frequently coupled with protein structural changes. Focusing on the coupling, we present the free-energy surface (FES) of the ligand-binding process for glutamine-binding protein (GlnBP) and its ligand, glutamine, in which glutamine binding accompanies large-scale domain closure. All-atom simulations were performed in explicit solvents by multiscale enhanced sampling (MSES), which adopts a multicopy and multiscale scheme to achieve enhanced sampling of systems with a large number of degrees of freedom. The structural ensemble derived from the MSES simulation yielded the FES of the coupling, described in terms of both the ligand's and protein's degrees of freedom at atomic resolution, and revealed the tight coupling between the two degrees of freedom. The derived FES led to the determination of definite structural states, which suggested the dominant pathways of glutamine binding to GlnBP: first, glutamine migrates via diffusion to form a dominant encounter complex with Arg75 on the large domain of GlnBP, through strong polar interactions. Subsequently, the closing motion of GlnBP occurs to form ligand interactions with the small domain, finally completing the native-specific complex structure. The formation of hydrogen bonds between glutamine and the small domain is considered to be a rate-limiting step, inducing desolvation of the protein-ligand interface to form the specific native complex. The key interactions to attain high specificity for glutamine, the "door keeper" existing between the two domains (Asp10-Lys115) and the "hydrophobic sandwich" formed between the ligand glutamine and Phe13/Phe50, have been successfully mapped on the pathway derived from the FES.

  11. Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

    PubMed

    You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

    2014-01-01

    Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

  12. STUDIES OF METABOLITE-PROTEIN INTERACTIONS: A REVIEW

    PubMed Central

    Matsuda, Ryan; Bi, Cong; Anguizola, Jeanethe; Sobansky, Matthew; Rodriquez, Elliot; Badilla, John Vargas; Zheng, Xiwei; Hage, Benjamin; Hage, David S.

    2014-01-01

    The study of metabolomics can provide valuable information about biochemical pathways and processes at the molecular level. There have been many reports that have examined the structure, identity and concentrations of metabolites in biological systems. However, the binding of metabolites with proteins is also of growing interest. This review examines past reports that have looked at the binding of various types of metabolites with proteins. An overview of the techniques that have been used to characterize and study metabolite-protein binding is first provided. This is followed by examples of studies that have investigated the binding of hormones, fatty acids, drugs or other xenobiotics, and their metabolites with transport proteins and receptors. These examples include reports that have considered the structure of the resulting solute-protein complexes, the nature of the binding sites, the strength of these interactions, the variations in these interactions with solute structure, and the kinetics of these reactions. The possible effects of metabolic diseases on these processes, including the impact of alterations in the structure and function of proteins, are also considered. PMID:24321277

  13. Huntingtin-associated protein-1 (HAP1) regulates endocytosis and interacts with multiple trafficking-related proteins.

    PubMed

    Mackenzie, Kimberly D; Lim, Yoon; Duffield, Michael D; Chataway, Timothy; Zhou, Xin-Fu; Keating, Damien J

    2017-07-01

    Huntingtin-associated protein 1 (HAP1) was initially identified as a binding partner of huntingtin, mutations in which underlie Huntington's disease. Subcellular localization and protein interaction data indicate that HAP1 may be important in vesicle trafficking, cell signalling and receptor internalization. In this study, a proteomics approach was used for the identification of novel HAP1-interacting partners to attempt to shed light on the physiological function of HAP1. Using affinity chromatography with HAP1-GST protein fragments bound to Sepharose columns, this study identified a number of trafficking-related proteins that bind to HAP1. Interestingly, many of the proteins that were identified by mass spectrometry have trafficking-related functions and include the clathrin light chain B and Sec23A, an ER to Golgi trafficking vesicle coat component. Using co-immunoprecipitation and GST-binding assays the association between HAP1 and clathrin light chain B has been validated in vitro. This study also finds that HAP1 co-localizes with clathrin light chain B. In line with a physiological function of the HAP1-clathrin interaction this study detected a dramatic reduction in vesicle retrieval and endocytosis in adrenal chromaffin cells. Furthermore, through examination of transferrin endocytosis in HAP1 -/- cortical neurons, this study has determined that HAP1 regulates neuronal endocytosis. In this study, the interaction between HAP1 and Sec23A was also validated through endogenous co-immunoprecipitation in rat brain homogenate. Through the identification of novel HAP1 binding partners, many of which have putative trafficking roles, this study provides us with new insights into the mechanisms underlying the important physiological function of HAP1 as an intracellular trafficking protein through its protein-protein interactions. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Structural and functional analysis of VQ motif-containing proteins in Arabidopsis as interacting proteins of WRKY transcription factors.

    PubMed

    Cheng, Yuan; Zhou, Yuan; Yang, Yan; Chi, Ying-Jun; Zhou, Jie; Chen, Jian-Ye; Wang, Fei; Fan, Baofang; Shi, Kai; Zhou, Yan-Hong; Yu, Jing-Quan; Chen, Zhixiang

    2012-06-01

    WRKY transcription factors are encoded by a large gene superfamily with a broad range of roles in plants. Recently, several groups have reported that proteins containing a short VQ (FxxxVQxLTG) motif interact with WRKY proteins. We have recently discovered that two VQ proteins from Arabidopsis (Arabidopsis thaliana), SIGMA FACTOR-INTERACTING PROTEIN1 and SIGMA FACTOR-INTERACTING PROTEIN2, act as coactivators of WRKY33 in plant defense by specifically recognizing the C-terminal WRKY domain and stimulating the DNA-binding activity of WRKY33. In this study, we have analyzed the entire family of 34 structurally divergent VQ proteins from Arabidopsis. Yeast (Saccharomyces cerevisiae) two-hybrid assays showed that Arabidopsis VQ proteins interacted specifically with the C-terminal WRKY domains of group I and the sole WRKY domains of group IIc WRKY proteins. Using site-directed mutagenesis, we identified structural features of these two closely related groups of WRKY domains that are critical for interaction with VQ proteins. Quantitative reverse transcription polymerase chain reaction revealed that expression of a majority of Arabidopsis VQ genes was responsive to pathogen infection and salicylic acid treatment. Functional analysis using both knockout mutants and overexpression lines revealed strong phenotypes in growth, development, and susceptibility to pathogen infection. Altered phenotypes were substantially enhanced through cooverexpression of genes encoding interacting VQ and WRKY proteins. These findings indicate that VQ proteins play an important role in plant growth, development, and response to environmental conditions, most likely by acting as cofactors of group I and IIc WRKY transcription factors.

  15. Understanding Protein-Protein Interactions: Essential Players in (Patho)physiology (Part 1).

    PubMed

    Wilson, Andrew J; Gunning, Patrick T

    2016-04-15

    This is the first of a two-part Editorial by the Guest Editors of the ChemBioChem and ChemMedChem joint Special Issue on Protein-Protein Interactions. Part 2 can be accessed via http://dx.doi.org/10.1002/cmdc.201600158; the complete issue can be viewed here: bit.ly/cbcVIppi. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.

    PubMed

    Hao, Tong; Zeng, Zheng; Wang, Bin; Zhang, Yichen; Liu, Yichen; Geng, Xuyun; Sun, Jinsheng

    2014-03-27

    The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system. In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network. The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary

  17. Differential Occurrence of Interactions and Interaction Domains in Proteins Containing Homopolymeric Amino Acid Repeats

    PubMed Central

    Pelassa, Ilaria; Fiumara, Ferdinando

    2015-01-01

    Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as “junk” sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures, and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the sets of proteins that contain repeats of any one of the 20 amino acids, as well as control sets of proteins chosen at random in the proteome. We then analyzed the connectivity between the proteins of the AAR-containing protein sets and we compared it with that observed in the corresponding control networks. We find evidence for different degrees of connectivity in the different AAR-containing protein networks. Indeed, networks of proteins containing polyglutamine, polyglutamate, polyproline, and other AARs show significantly increased levels of connectivity, whereas networks containing polyleucine and other hydrophobic repeats show lower degrees of connectivity. Furthermore, we observed that numerous protein-protein, -nucleic acid, and -lipid interaction domains are significantly enriched in specific AAR protein groups. These findings support the notion of a generalized, combinatorial role of AARs, together with conventional protein interaction domains, in

  18. Network representation of protein interactions: Theory of graph description and analysis.

    PubMed

    Kurzbach, Dennis

    2016-09-01

    A methodological framework is presented for the graph theoretical interpretation of NMR data of protein interactions. The proposed analysis generalizes the idea of network representations of protein structures by expanding it to protein interactions. This approach is based on regularization of residue-resolved NMR relaxation times and chemical shift data and subsequent construction of an adjacency matrix that represents the underlying protein interaction as a graph or network. The network nodes represent protein residues. Two nodes are connected if two residues are functionally correlated during the protein interaction event. The analysis of the resulting network enables the quantification of the importance of each amino acid of a protein for its interactions. Furthermore, the determination of the pattern of correlations between residues yields insights into the functional architecture of an interaction. This is of special interest for intrinsically disordered proteins, since the structural (three-dimensional) architecture of these proteins and their complexes is difficult to determine. The power of the proposed methodology is demonstrated at the example of the interaction between the intrinsically disordered protein osteopontin and its natural ligand heparin. © 2016 The Protein Society.

  19. MPact: the MIPS protein interaction resource on yeast.

    PubMed

    Güldener, Ulrich; Münsterkötter, Martin; Oesterheld, Matthias; Pagel, Philipp; Ruepp, Andreas; Mewes, Hans-Werner; Stümpflen, Volker

    2006-01-01

    In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein-protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called a gold standard because of its quality and comprehensiveness [H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107-1118]. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog [A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539-5545] is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.

  20. Hydrophobic interactions of sucralose with protein structures.

    PubMed

    Shukla, Nimesh; Pomarico, Enrico; Hecht, Cody J S; Taylor, Erika A; Chergui, Majed; Othon, Christina M

    2018-02-01

    Sucralose is a commonly employed artificial sweetener that appears to destabilize protein native structures. This is in direct contrast to the bio-preservative nature of its natural counterpart, sucrose, which enhances the stability of biomolecules against environmental stress. We have further explored the molecular interactions of sucralose as compared to sucrose to illuminate the origin of the differences in their bio-preservative efficacy. We show that the mode of interactions of sucralose and sucrose in bulk solution differ subtly through the use of hydration dynamics measurement and computational simulation. Sucralose does not appear to disturb the native state of proteins for moderate concentrations (<0.2 M) at room temperature. However, as the concentration increases, or in the thermally stressed state, sucralose appears to differ in its interactions with protein leading to the reduction of native state stability. This difference in interaction appears weak. We explored the difference in the preferential exclusion model using time-resolved spectroscopic techniques and observed that both molecules appear to be effective reducers of bulk hydration dynamics. However, the chlorination of sucralose appears to slightly enhance the hydrophobicity of the molecule, which reduces the preferential exclusion of sucralose from the protein-water interface. The weak interaction of sucralose with hydrophobic pockets on the protein surface differs from the behavior of sucrose. We experimentally followed up upon the extent of this weak interaction using isothermal titration calorimetry (ITC) measurements. We propose this as a possible origin for the difference in their bio-preservative properties. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Large-scale protein-protein interaction analysis in Arabidopsis mesophyll protoplasts by split firefly luciferase complementation.

    PubMed

    Li, Jian-Feng; Bush, Jenifer; Xiong, Yan; Li, Lei; McCormack, Matthew

    2011-01-01

    Protein-protein interactions (PPIs) constitute the regulatory network that coordinates diverse cellular functions. There are growing needs in plant research for creating protein interaction maps behind complex cellular processes and at a systems biology level. However, only a few approaches have been successfully used for large-scale surveys of PPIs in plants, each having advantages and disadvantages. Here we present split firefly luciferase complementation (SFLC) as a highly sensitive and noninvasive technique for in planta PPI investigation. In this assay, the separate halves of a firefly luciferase can come into close proximity and transiently restore its catalytic activity only when their fusion partners, namely the two proteins of interest, interact with each other. This assay was conferred with quantitativeness and high throughput potential when the Arabidopsis mesophyll protoplast system and a microplate luminometer were employed for protein expression and luciferase measurement, respectively. Using the SFLC assay, we could monitor the dynamics of rapamycin-induced and ascomycin-disrupted interaction between Arabidopsis FRB and human FKBP proteins in a near real-time manner. As a proof of concept for large-scale PPI survey, we further applied the SFLC assay to testing 132 binary PPIs among 8 auxin response factors (ARFs) and 12 Aux/IAA proteins from Arabidopsis. Our results demonstrated that the SFLC assay is ideal for in vivo quantitative PPI analysis in plant cells and is particularly powerful for large-scale binary PPI screens.

  2. Genetically encoded releasable photo-cross-linking strategies for studying protein-protein interactions in living cells.

    PubMed

    Yang, Yi; Song, Haiping; He, Dan; Zhang, Shuai; Dai, Shizhong; Xie, Xiao; Lin, Shixian; Hao, Ziyang; Zheng, Huangtao; Chen, Peng R

    2017-10-01

    Although protein-protein interactions (PPIs) have crucial roles in virtually all cellular processes, the identification of more transient interactions in their biological context remains challenging. Conventional photo-cross-linking strategies can be used to identify transient interactions, but these approaches often suffer from high background due to the cross-linked bait proteins. To solve the problem, we have developed membrane-permeable releasable photo-cross-linkers that allow for prey-bait separation after protein complex isolation and can be installed in proteins of interest (POIs) as unnatural amino acids. Here we describe the procedures for using two releasable photo-cross-linkers, DiZSeK and DiZHSeC, in both living Escherichia coli and mammalian cells. A cleavage after protein photo-cross-linking (CAPP ) strategy based on the photo-cross-linker DiZSeK is described, in which the prey protein pool is released from a POI after affinity purification. Prey proteins are analyzed using mass spectrometry or 2D gel electrophoresis for global comparison of interactomes from different experimental conditions. An in situ cleavage and mass spectrometry (MS)-label transfer after protein photo-cross-linking (IMAPP) strategy based on the photo-cross-linker DiZHSeC is also described. This strategy can be used for the identification of cross-linking sites to allow detailed characterization of PPI interfaces. The procedures for photo-cross-linker incorporation, photo-cross-linking of interaction partners and affinity purification of cross-linked complexes are similar for the two photo-cross-linkers. The final section of the protocol describes prey-bait separation (for CAPP) and MS-label transfer and identification (for IMAPP). After plasmid construction, the CAPP and IMAPP strategies can be completed within 6 and 7 d, respectively.

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

    PubMed Central

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

    2013-01-01

    Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis. PMID:24278371

  4. A coarse grain model for protein-surface interactions

    NASA Astrophysics Data System (ADS)

    Wei, Shuai; Knotts, Thomas A.

    2013-09-01

    The interaction of proteins with surfaces is important in numerous applications in many fields—such as biotechnology, proteomics, sensors, and medicine—but fundamental understanding of how protein stability and structure are affected by surfaces remains incomplete. Over the last several years, molecular simulation using coarse grain models has yielded significant insights, but the formalisms used to represent the surface interactions have been rudimentary. We present a new model for protein surface interactions that incorporates the chemical specificity of both the surface and the residues comprising the protein in the context of a one-bead-per-residue, coarse grain approach that maintains computational efficiency. The model is parameterized against experimental adsorption energies for multiple model peptides on different types of surfaces. The validity of the model is established by its ability to quantitatively and qualitatively predict the free energy of adsorption and structural changes for multiple biologically-relevant proteins on different surfaces. The validation, done with proteins not used in parameterization, shows that the model produces remarkable agreement between simulation and experiment.

  5. Novel protein-protein interaction between spermidine synthase and S-adenosylmethionine decarboxylase from Leishmania donovani.

    PubMed

    Mishra, Arjun K; Agnihotri, Pragati; Srivastava, Vijay Kumar; Pratap, J Venkatesh

    2015-01-09

    Polyamine biosynthesis pathway has long been considered an essential drug target for trypanosomatids including Leishmania. S-adenosylmethionine decarboxylase (AdoMetDc) and spermidine synthase (SpdSyn) are enzymes of this pathway that catalyze successive steps, with the product of the former, decarboxylated S-adenosylmethionine (dcSAM), acting as an aminopropyl donor for the latter enzyme. Here we have explored the possibility of and identified the protein-protein interaction between SpdSyn and AdoMetDc. The protein-protein interaction has been identified using GST pull down assay. Isothermal titration calorimetry reveals that the interaction is thermodynamically favorable. Fluorescence spectroscopy studies also confirms the interaction, with SpdSyn exhibiting a change in tertiary structure with increasing concentrations of AdoMetDc. Size exclusion chromatography suggests the presence of the complex as a hetero-oligomer. Taken together, these results suggest that the enzymes indeed form a heteromer. Computational analyses suggest that this complex differs significantly from the corresponding human complex, implying that this complex could be a better therapeutic target than the individual enzymes. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes.

    PubMed

    Uchikoga, Nobuyuki; Hirokawa, Takatsugu

    2010-05-11

    Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles. To test for the applicability of this combined method, various CaM-ligand complexes were reconstructed from the NMR structures of unbound CaM. For the purpose of reconstruction, we used three known CaM-ligands, namely, the CaM-binding peptides of cyclic nucleotide gateway (CNG), CaM kinase kinase (CaMKK) and the plasma membrane Ca2+ ATPase pump (PMCA), and thirty-one structurally diverse CaM conformations. For each ligand, 62000 CaM-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and RMSD. Near-native clusters were obtained in the case of CNG and CaMKK. The interaction fingerprint method discriminated near-native structures better than the RMSD method in cluster analysis. We showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases.

  7. Determination Quantification of Molecular Interactions in Protein Films: A Review

    PubMed Central

    Hammann, Felicia; Schmid, Markus

    2014-01-01

    Protein based films are nowadays also prepared with the aim of replacing expensive, crude oil-based polymers as environmentally friendly and renewable alternatives. The protein structure determines the ability of protein chains to form intra- and intermolecular bonds, whereas the degree of cross-linking depends on the amino acid composition and molecular weight of the protein, besides the conditions used in film preparation and processing. The functionality varies significantly depending on the type of protein and affects the resulting film quality and properties. This paper reviews the methods used in examination of molecular interactions in protein films and discusses how these intermolecular interactions can be quantified. The qualitative determination methods can be distinguished by structural analysis of solutions (electrophoretic analysis, size exclusion chromatography) and analysis of solid films (spectroscopy techniques, X-ray scattering methods). To quantify molecular interactions involved, two methods were found to be the most suitable: protein film swelling and solubility. The importance of non-covalent and covalent interactions in protein films can be investigated using different solvents. The research was focused on whey protein, whereas soy protein and wheat gluten were included as further examples of proteins. PMID:28788285

  8. Engineering Archeal Surrogate Systems for the Development of Protein-Protein Interaction Inhibitors against Human RAD51.

    PubMed

    Moschetti, Tommaso; Sharpe, Timothy; Fischer, Gerhard; Marsh, May E; Ng, Hong Kin; Morgan, Matthew; Scott, Duncan E; Blundell, Tom L; R Venkitaraman, Ashok; Skidmore, John; Abell, Chris; Hyvönen, Marko

    2016-11-20

    Protein-protein interactions (PPIs) are increasingly important targets for drug discovery. Efficient fragment-based drug discovery approaches to tackle PPIs are often stymied by difficulties in the production of stable, unliganded target proteins. Here, we report an approach that exploits protein engineering to "humanise" thermophilic archeal surrogate proteins as targets for small-molecule inhibitor discovery and to exemplify this approach in the development of inhibitors against the PPI between the recombinase RAD51 and tumour suppressor BRCA2. As human RAD51 has proved impossible to produce in a form that is compatible with the requirements of fragment-based drug discovery, we have developed a surrogate protein system using RadA from Pyrococcus furiosus. Using a monomerised RadA as our starting point, we have adopted two parallel and mutually instructive approaches to mimic the human enzyme: firstly by mutating RadA to increase sequence identity with RAD51 in the BRC repeat binding sites, and secondly by generating a chimeric archaeal human protein. Both approaches generate proteins that interact with a fourth BRC repeat with affinity and stoichiometry comparable to human RAD51. Stepwise humanisation has also allowed us to elucidate the determinants of RAD51 binding to BRC repeats and the contributions of key interacting residues to this interaction. These surrogate proteins have enabled the development of biochemical and biophysical assays in our ongoing fragment-based small-molecule inhibitor programme and they have allowed us to determine hundreds of liganded structures in support of our structure-guided design process, demonstrating the feasibility and advantages of using archeal surrogates to overcome difficulties in handling human proteins. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  9. Self diffusion of interacting membrane proteins.

    PubMed Central

    Abney, J R; Scalettar, B A; Owicki, J C

    1989-01-01

    A two-dimensional version of the generalized Smoluchowski equation is used to analyze the time (or distance) dependent self diffusion of interacting membrane proteins in concentrated membrane systems. This equation provides a well established starting point for descriptions of the diffusion of particles that interact through both direct and hydrodynamic forces; in this initial work only the effects of direct interactions are explicitly considered. Data describing diffusion in the presence of hard-core repulsions, soft repulsions, and soft repulsions with weak attractions are presented. The effect that interactions have on the self-diffusion coefficient of a real protein molecule from mouse liver gap junctions is also calculated. The results indicate that self diffusion is always inhibited by direct interactions; this observation is interpreted in terms of the caging that will exist at finite protein concentration. It is also noted that, over small distance scales, the diffusion coefficient is determined entirely by the very strong Brownian forces; therefore, as a function of displacement the self-diffusion coefficient decays (rapidly) from its value at infinite dilution to its steady-state interaction-averaged value. The steady-state self-diffusion coefficient describes motion over distance scales that range from approximately 10 nm to cellular dimensions and is the quantity measured in fluorescence recovery after photobleaching experiments. The short-ranged behavior of the diffusion coefficient is important on the interparticle-distance scale and may therefore influence the rate at which nearest-neighbor collisional processes take place. The hard-disk theoretical results presented here are in excellent agreement with lattice Monte-Carlo results obtained by other workers. The concentration dependence of experimentally measured diffusion coefficients of antibody-hapten complexes bound to the membrane surface is consistent with that predicted by the theory. The

  10. Profiling lethal factor interacting proteins from human stomach using T7 phage display screening.

    PubMed

    Cardona-Correa, Albin; Rios-Velazquez, Carlos

    2016-05-01

    The anthrax lethal factor (LF) is a zinc dependent metalloproteinase that cleaves the majority of mitogen-activated protein kinase kinases and a member of NOD-like receptor proteins, inducing cell apoptosis. Despite efforts to fully understand the Bacillus anthracis toxin components, the gastrointestinal (GI) anthrax mechanisms have not been fully elucidated. Previous studies demonstrated gastric ulceration, and a substantial bacterial growth rate in Peyer's patches. However, the complete molecular pathways of the disease that results in tissue damage by LF proteolytic activity remains unclear. In the present study, to identify the profile of the proteins potentially involved in GI anthrax, protein‑protein interactions were investigated using human stomach T7 phage display (T7PD) cDNA libraries. T7PD is a high throughput technique that allows the expression of cloned DNA sequences as peptides on the phage surface, enabling the selection and identification of protein ligands. A wild type and mutant LF (E687A) were used to differentiate interaction sites. A total of 124 clones were identified from 194 interacting‑phages, at both the DNA and protein level, by in silico analysis. Databases revealed that the selected candidates were proteins from different families including lipase, peptidase‑A1 and cation transport families, among others. Furthermore, individual T7PD candidates were tested against LF in order to detect their specificity to the target molecule, resulting in 10 LF‑interacting peptides. With a minimum concentration of LF for interaction at 1 µg/ml, the T7PD isolated pepsin A3 pre‑protein (PAP) demonstrated affinity to both types of LF. In addition, PAP was isolated in various lengths for the same protein, exhibiting common regions following PRALINE alignment. These findings will help elucidate and improve the understanding of the molecular pathogenesis of GI anthrax, and aid in the development of potential therapeutic agents.

  11. Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

    PubMed

    An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

    2016-10-01

    Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future

  12. Assembling a protein-protein interaction map of the SSU processome from existing datasets.

    PubMed

    Lim, Young H; Charette, J Michael; Baserga, Susan J

    2011-03-10

    The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear. We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome. We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis.

  13. Assembling a Protein-Protein Interaction Map of the SSU Processome from Existing Datasets

    PubMed Central

    Baserga, Susan J.

    2011-01-01

    Background The small subunit (SSU) processome is a large ribonucleoprotein complex involved in small ribosomal subunit assembly. It consists of the U3 snoRNA and ∼72 proteins. While most of its components have been identified, the protein-protein interactions (PPIs) among them remain largely unknown, and thus the assembly, architecture and function of the SSU processome remains unclear. Methodology We queried PPI databases for SSU processome proteins to quantify the degree to which the three genome-wide high-throughput yeast two-hybrid (HT-Y2H) studies, the genome-wide protein fragment complementation assay (PCA) and the literature-curated (LC) datasets cover the SSU processome interactome. Conclusions We find that coverage of the SSU processome PPI network is remarkably sparse. Two of the three HT-Y2H studies each account for four and six PPIs between only six of the 72 proteins, while the third study accounts for as little as one PPI and two proteins. The PCA dataset has the highest coverage among the genome-wide studies with 27 PPIs between 25 proteins. The LC dataset was the most extensive, accounting for 34 proteins and 38 PPIs, many of which were validated by independent methods, thereby further increasing their reliability. When the collected data were merged, we found that at least 70% of the predicted PPIs have yet to be determined and 26 proteins (36%) have no known partners. Since the SSU processome is conserved in all Eukaryotes, we also queried HT-Y2H datasets from six additional model organisms, but only four orthologues and three previously known interologous interactions were found. This provides a starting point for further work on SSU processome assembly, and spotlights the need for a more complete genome-wide Y2H analysis. PMID:21423703

  14. Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences.

    PubMed

    Pitre, S; North, C; Alamgir, M; Jessulat, M; Chan, A; Luo, X; Green, J R; Dumontier, M; Dehne, F; Golshani, A

    2008-08-01

    Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

  15. The auxin-inducible degradation (AID) system enables versatile conditional protein depletion in C. elegans

    PubMed Central

    Zhang, Liangyu; Ward, Jordan D.; Cheng, Ze; Dernburg, Abby F.

    2015-01-01

    Experimental manipulation of protein abundance in living cells or organisms is an essential strategy for investigation of biological regulatory mechanisms. Whereas powerful techniques for protein expression have been developed in Caenorhabditis elegans, existing tools for conditional disruption of protein function are far more limited. To address this, we have adapted the auxin-inducible degradation (AID) system discovered in plants to enable conditional protein depletion in C. elegans. We report that expression of a modified Arabidopsis TIR1 F-box protein mediates robust auxin-dependent depletion of degron-tagged targets. We document the effectiveness of this system for depletion of nuclear and cytoplasmic proteins in diverse somatic and germline tissues throughout development. Target proteins were depleted in as little as 20-30 min, and their expression could be re-established upon auxin removal. We have engineered strains expressing TIR1 under the control of various promoter and 3′ UTR sequences to drive tissue-specific or temporally regulated expression. The degron tag can be efficiently introduced by CRISPR/Cas9-based genome editing. We have harnessed this system to explore the roles of dynamically expressed nuclear hormone receptors in molting, and to analyze meiosis-specific roles for proteins required for germ line proliferation. Together, our results demonstrate that the AID system provides a powerful new tool for spatiotemporal regulation and analysis of protein function in a metazoan model organism. PMID:26552885

  16. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

    PubMed Central

    Huber, Roland G.; Bond, Peter J.

    2017-01-01

    An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners. PMID:29016650

  17. Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

    PubMed

    Ivanov, Stefan M; Cawley, Andrew; Huber, Roland G; Bond, Peter J; Warwicker, Jim

    2017-01-01

    An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

  18. Experimental Methods for Protein Interaction Identification and Characterization

    NASA Astrophysics Data System (ADS)

    Uetz, Peter; Titz, Björn; Cagney, Gerard

    There are dozens of methods for the detection of protein-protein interactions but they fall into a few broad categories. Fragment complementation assays such as the yeast two-hybrid (Y2H) system are based on split proteins that are functionally reconstituted by fusions of interacting proteins. Biophysical methods include structure determination and mass spectrometric (MS) identification of proteins in complexes. Biochemical methods include methods such as far western blotting and peptide arrays. Only the Y2H and protein complex purification combined with MS have been used on a larger scale. Due to the lack of data it is still difficult to compare these methods with respect to their efficiency and error rates. Current data does not favor any particular method and thus multiple experimental approaches are necessary to maximally cover the interactome of any target cell or organism.

  19. 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

  20. Computational Methods to Predict Protein Interaction Partners

    NASA Astrophysics Data System (ADS)

    Valencia, Alfonso; Pazos, Florencio

    In the new paradigm for studying biological phenomena represented by Systems Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein interaction networks are among the first objects studied from this new point of view. Deciphering the interactome (the whole network of interactions for a given proteome) has been shown to be a very complex task. Computational techniques for detecting protein interactions have become standard tools for dealing with this problem, helping and complementing their experimental counterparts. Most of these techniques use genomic or sequence features intuitively related with protein interactions and are based on "first principles" in the sense that they do not involve training with examples. There are also other computational techniques that use other sources of information (i.e. structural information or even experimental data) or are based on training with examples.

  1. From protein interaction profile to functional assignment: the human protein Ki-1/57 is associated with pre-mRNA splicing events.

    PubMed

    Bressan, Gustavo Costa; Kobarg, Jörg

    2010-01-01

    The mapping of protein-protein interactions of a determined organism is considered fundamental to assign protein function in the post-genomic era. As part of this effort, screenings for pairwise interactions by yeast two-hybrid system have been used popularly to reveal protein interaction networks in different biological systems. Through the identification of protein interaction partners we have successfully obtained interesting functional clues for Ki-1/57, a human protein with no previous functional annotation, in the context of RNA metabolism. We briefly discuss the way we approached protein-protein interaction data to conduct and interpret further molecular biological and cellular studies as well as structural analyses on this protein. Our data suggest that Ki-1/57 belongs to the family of intrinsically unstructured proteins and that the structural flexibility may be crucial for its capacity to interact with many different proteins. A large fraction of these proteins are involved in pre-mRNA splicing control. Finally, Ki-1/57 is localized to several subnuclear domains, all of which have been described to splicing and other RNA processing events.

  2. Mass spectrometric identification of proteins that interact through specific domains of the poly(A) binding protein

    PubMed Central

    Zhang, Chongxu; Nielsen, Maria E. O.; Chiang, Yueh-Chin; Kierkegaard, Morten; Wang, Xin; Lee, Darren J.; Andersen, Jens S.; Yao, Gang

    2013-01-01

    Poly(A) binding protein (PAB1) is involved in a number of RNA metabolic functions in eukaryotic cells and correspondingly is suggested to associate with a number of proteins. We have used mass spectrometric analysis to identify 55 non-ribosomal proteins that specifically interact with PAB1 from Saccharomyces cerevisiae. Because many of these factors may associate only indirectly with PAB1 by being components of the PAB1-mRNP structure, we additionally conducted mass spectrometric analyses on seven metabolically defined PAB1 deletion derivatives to delimit the interactions between these proteins and PAB1. These latter analyses identified 13 proteins whose associations with PAB1 were reduced by deleting one or another of PAB1’s defined domains. Included in this list of 13 proteins were the translation initiation factors eIF4G1 and eIF4G2, translation termination factor eRF3, and PBP2, all of whose previously known direct interactions with specific PAB1 domains were either confirmed, delimited, or extended. The remaining nine proteins that interacted through a specific PAB1 domain were CBF5, SLF1, UPF1, CBC1, SSD1, NOP77, yGR250c, NAB6, and GBP2. In further study, UPF1, involved in nonsense-mediated decay, was confirmed to interact with PAB1 through the RRM1 domain. We additionally established that while the RRM1 domain of PAB1 was required for UPF1-induced acceleration of deadenylation during nonsense-mediated decay, it was not required for the more critical step of acceleration of mRNA decapping. These results begin to identify the proteins most likely to interact with PAB1 and the domains of PAB1 through which these contacts are made. PMID:22836166

  3. Mass spectrometric identification of proteins that interact through specific domains of the poly(A) binding protein.

    PubMed

    Richardson, Roy; Denis, Clyde L; Zhang, Chongxu; Nielsen, Maria E O; Chiang, Yueh-Chin; Kierkegaard, Morten; Wang, Xin; Lee, Darren J; Andersen, Jens S; Yao, Gang

    2012-09-01

    Poly(A) binding protein (PAB1) is involved in a number of RNA metabolic functions in eukaryotic cells and correspondingly is suggested to associate with a number of proteins. We have used mass spectrometric analysis to identify 55 non-ribosomal proteins that specifically interact with PAB1 from Saccharomyces cerevisiae. Because many of these factors may associate only indirectly with PAB1 by being components of the PAB1-mRNP structure, we additionally conducted mass spectrometric analyses on seven metabolically defined PAB1 deletion derivatives to delimit the interactions between these proteins and PAB1. These latter analyses identified 13 proteins whose associations with PAB1 were reduced by deleting one or another of PAB1's defined domains. Included in this list of 13 proteins were the translation initiation factors eIF4G1 and eIF4G2, translation termination factor eRF3, and PBP2, all of whose previously known direct interactions with specific PAB1 domains were either confirmed, delimited, or extended. The remaining nine proteins that interacted through a specific PAB1 domain were CBF5, SLF1, UPF1, CBC1, SSD1, NOP77, yGR250c, NAB6, and GBP2. In further study, UPF1, involved in nonsense-mediated decay, was confirmed to interact with PAB1 through the RRM1 domain. We additionally established that while the RRM1 domain of PAB1 was required for UPF1-induced acceleration of deadenylation during nonsense-mediated decay, it was not required for the more critical step of acceleration of mRNA decapping. These results begin to identify the proteins most likely to interact with PAB1 and the domains of PAB1 through which these contacts are made.

  4. A facile method to screen inhibitors of protein-protein interactions including MDM2-p53 displayed on T7 phage.

    PubMed

    Ishi, Kazutomo; Sugawara, Fumio

    2008-05-01

    Protein-protein interactions are essential in many biological processes including cell cycle and apoptosis. It is currently of great medical interest to inhibit specific protein-protein interactions in order to treat a variety of disease states. Here, we describe a facile multiwell plate assay method using T7 phage display to screen for candidate inhibitors of protein-protein interactions. Because T7 phage display is an effective method for detecting protein-protein interactions, we aimed to utilize this technique to screen for small-molecule inhibitors that disrupt these types of interaction. We used the well-characterized interaction between p53 and MDM2 and an inhibitor of this interaction, nutlin 3, as a model system to establish a new screening method. Phage particles displaying p53 interacted with GST-MDM2 immobilized on 96-well plates, and the interaction was inhibited by nutlin 3. Multiwell plate assay was then performed using a natural product library, which identified dehydroaltenusin as a candidate inhibitor of the p53-MDM2 interaction. We discuss the potential applications of this novel T7 phage display methodology, which we propose to call 'reverse phage display'.

  5. The potential of protein-nanomaterial interaction for advanced drug delivery.

    PubMed

    Peng, Qiang; Mu, Huiling

    2016-03-10

    Nanomaterials, like nanoparticles, micelles, nano-sheets, nanotubes and quantum dots, have great potentials in biomedical fields. However, their delivery is highly limited by the formation of protein corona upon interaction with endogenous proteins. This new identity, instead of nanomaterial itself, would be the real substance the organs and cells firstly encounter. Consequently, the behavior of nanomaterials in vivo is uncontrollable and some undesired effects may occur, like rapid clearance from blood stream; risk of capillary blockage; loss of targeting capacity; and potential toxicity. Therefore, protein-nanomaterial interaction is a great challenge for nanomaterial systems and should be inhibited. However, this interaction can also be used to functionalize nanomaterials by forming a selected protein corona. Unlike other decoration using exogenous molecules, nanomaterials functionalized by selected protein corona using endogenous proteins would have greater promise for clinical use. In this review, we aim to provide a comprehensive understanding of protein-nanomaterial interaction. Importantly, a discussion about how to use such interaction is launched and some possible applications of such interaction for advanced drug delivery are presented. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

    PubMed

    Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

    2017-01-01

    Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

  7. Mining protein-protein interaction networks: denoising effects

    NASA Astrophysics Data System (ADS)

    Marras, Elisabetta; Capobianco, Enrico

    2009-01-01

    A typical instrument to pursue analysis in complex network studies is the analysis of the statistical distributions. They are usually computed for measures which characterize network topology, and are aimed at capturing both structural and dynamics aspects. Protein-protein interaction networks (PPIN) have also been studied through several measures. It is in general observed that a power law is expected to characterize scale-free networks. However, mixing the original noise cover with outlying information and other system-dependent fluctuations makes the empirical detection of the power law a difficult task. As a result the uncertainty level increases when looking at the observed sample; in particular, one may wonder whether the computed features may be sufficient to explain the interactome. We then address noise problems by implementing both decomposition and denoising techniques that reduce the impact of factors known to affect the accuracy of power law detection.

  8. Protein-lipid interactions at the air/water interface.

    PubMed

    Lad, Mitaben D; Birembaut, Fabrice; Frazier, Richard A; Green, Rebecca J

    2005-10-07

    Surface pressure measurements and external reflection FTIR spectroscopy have been used to probe protein-lipid interactions at the air/water interface. Spread monomolecular layers of stearic acid and phosphocholine were prepared and held at different compressed phase states prior to the introduction of protein to the buffered subphase. Contrasting interfacial behaviour of the proteins, albumin and lysozyme, was observed and revealed the role of both electrostatic and hydrophobic interactions in protein adsorption. The rate of adsorption of lysozyme to the air/water interface increased dramatically in the presence of stearic acid, due to strong electrostatic interactions between the negatively charged stearic acid head group and lysozyme, whose net charge at pH 7 is positive. Introduction of albumin to the subphase resulted in solubilisation of the stearic acid via the formation of an albumin-stearic acid complex and subsequent adsorption of albumin. This observation held for both human and bovine serum albumin. Protein adsorption to a PC layer held at low surface pressure revealed adsorption rates similar to adsorption to the bare air/water interface and suggested very little interaction between the protein and the lipid. For PC layers in their compressed phase state some adsorption of protein occurred after long adsorption times. Structural changes of both lysozyme and albumin were observed during adsorption, but these were dramatically reduced in the presence of a lipid layer compared to that of adsorption to the pure air/water interface.

  9. LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information.

    PubMed

    Zahiri, Javad; Mohammad-Noori, Morteza; Ebrahimpour, Reza; Saadat, Samaneh; Bozorgmehr, Joseph H; Goldberg, Tatyana; Masoudi-Nejad, Ali

    2014-12-01

    Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse. The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Detection of specific protein-protein interactions in nanocages by engineering bipartite FlAsH binding sites.

    PubMed

    Cornell, Thomas A; Fu, Jing; Newland, Stephanie H; Orner, Brendan P

    2013-11-06

    Proteins that form cage-like structures have been of much recent cross-disciplinary interest due to their application to bioconjugate and materials chemistry, their biological functions spanning multiple essential cellular processes, and their complex structure, often defined by highly symmetric protein–protein interactions. Thus, establishing the fundamentals of their formation, through detecting and quantifying important protein–protein interactions, could be crucial to understanding essential cellular machinery, and for further development of protein-based technologies. Herein we describe a method to monitor the assembly of protein cages by detecting specific, oligomerization state dependent, protein–protein interactions. Our strategy relies on engineering protein monomers to include cysteine pairs that are presented proximally if the cage state assembles. These assembled pairs of cysteines act as binding sites for the fluorescent reagent FlAsH, which, once bound, provides a readout for successful oligomerization. As a proof of principle, we applied this technique to the iron storage protein, DNA-binding protein from starved cells from E. coli. Several linker lengths and conformations for the presentation of the cysteine pairs were screened to optimize the engineered binding sites. We confirmed that our designs were successful in both lysates and with purified proteins, and that FlAsH binding was dependent upon cage assembly. Following successful characterization of the assay, its throughput was expanded. A two-dimension matrix of pH and denaturing buffer conditions was screened to optimize nanocage stability. We intend to use this method for the high throughput screening of protein cage libraries and of conditions for the generation of inorganic nanoparticles within the cavity of these and other cage proteins.

  11. A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

    PubMed

    Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

    2013-11-07

    Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper, a sequence-based, fast, and adaptive PPI prediction method is introduced to assign two proteins to an interaction class (yes, no). First, in order to improve the presentation of the sequences, twelve physicochemical properties of amino acid have been used by different representation methods to transform the sequence of protein pairs into different feature vectors. Then, for speeding up the learning process and reducing the effect of noise PPI data, principal component analysis (PCA) is carried out as a proper feature extraction algorithm. Finally, a new and adaptive Learning Vector Quantization (LVQ) predictor is designed to deal with different models of datasets that are classified into balanced and imbalanced datasets. The accuracy of 93.88%, 90.03%, and 89.72% has been found on S. cerevisiae, H. pylori, and independent datasets, respectively. The results of various experiments indicate the efficiency and validity of the method. © 2013 Published by Elsevier Ltd.

  12. An Evolutionarily Conserved Innate Immunity Protein Interaction Network*

    PubMed Central

    De Arras, Lesly; Seng, Amara; Lackford, Brad; Keikhaee, Mohammad R.; Bowerman, Bruce; Freedman, Jonathan H.; Schwartz, David A.; Alper, Scott

    2013-01-01

    The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice. PMID:23209288

  13. Docking and scoring protein interactions: CAPRI 2009.

    PubMed

    Lensink, Marc F; Wodak, Shoshana J

    2010-11-15

    Protein docking algorithms are assessed by evaluating blind predictions performed during 2007-2009 in Rounds 13-19 of the community-wide experiment on critical assessment of predicted interactions (CAPRI). We evaluated the ability of these algorithms to sample docking poses and to single out specific association modes in 14 targets, representing 11 distinct protein complexes. These complexes play important biological roles in RNA maturation, G-protein signal processing, and enzyme inhibition and function. One target involved protein-RNA interactions not previously considered in CAPRI, several others were hetero-oligomers, or featured multiple interfaces between the same protein pair. For most targets, predictions started from the experimentally determined structures of the free (unbound) components, or from models built from known structures of related or similar proteins. To succeed they therefore needed to account for conformational changes and model inaccuracies. In total, 64 groups and 12 web-servers submitted docking predictions of which 4420 were evaluated. Overall our assessment reveals that 67% of the groups, more than ever before, produced acceptable models or better for at least one target, with many groups submitting multiple high- and medium-accuracy models for two to six targets. Forty-one groups including four web-servers participated in the scoring experiment with 1296 evaluated models. Scoring predictions also show signs of progress evidenced from the large proportion of correct models submitted. But singling out the best models remains a challenge, which also adversely affects the ability to correctly rank docking models. With the increased interest in translating abstract protein interaction networks into realistic models of protein assemblies, the growing CAPRI community is actively developing more efficient and reliable docking and scoring methods for everyone to use. © 2010 Wiley-Liss, Inc.

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

    PubMed Central

    Cho, Young-Rae; Hwang, Woochang; Ramanathan, Murali; Zhang, Aidong

    2007-01-01

    Background The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms. Results We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches. Conclusion The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification. PMID:17650343

  15. Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks

    PubMed Central

    2011-01-01

    Background Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks. Results We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered. Conclusions We observe that co-expression stability shows distinct patterns in structurally and functionally

  16. Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.

    PubMed

    Chen, Lei; Zhang, Yu-Hang; Zheng, Mingyue; Huang, Tao; Cai, Yu-Dong

    2016-12-01

    Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

  17. Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

    PubMed

    Dimitrakopoulos, Christos; Theofilatos, Konstantinos; Pegkas, Andreas; Likothanassis, Spiros; Mavroudi, Seferina

    2016-07-01

    Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics. In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster. In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy. In the present manuscript

  18. Re-visiting protein-centric two-tier classification of existing DNA-protein complexes.

    PubMed

    Malhotra, Sony; Sowdhamini, Ramanathan

    2012-07-16

    Precise DNA-protein interactions play most important and vital role in maintaining the normal physiological functioning of the cell, as it controls many high fidelity cellular processes. Detailed study of the nature of these interactions has paved the way for understanding the mechanisms behind the biological processes in which they are involved. Earlier in 2000, a systematic classification of DNA-protein complexes based on the structural analysis of the proteins was proposed at two tiers, namely groups and families. With the advancement in the number and resolution of structures of DNA-protein complexes deposited in the Protein Data Bank, it is important to revisit the existing classification. On the basis of the sequence analysis of DNA binding proteins, we have built upon the protein centric, two-tier classification of DNA-protein complexes by adding new members to existing families and making new families and groups. While classifying the new complexes, we also realised the emergence of new groups and families. The new group observed was where β-propeller was seen to interact with DNA. There were 34 SCOP folds which were observed to be present in the complexes of both old and new classifications, whereas 28 folds are present exclusively in the new complexes. Some new families noticed were NarL transcription factor, Z-α DNA binding proteins, Forkhead transcription factor, AP2 protein, Methyl CpG binding protein etc. Our results suggest that with the increasing number of availability of DNA-protein complexes in Protein Data Bank, the number of families in the classification increased by approximately three fold. The folds present exclusively in newly classified complexes is suggestive of inclusion of proteins with new function in new classification, the most populated of which are the folds responsible for DNA damage repair. The proposed re-visited classification can be used to perform genome-wide surveys in the genomes of interest for the presence of DNA

  19. Quantification of protein interaction kinetics in a micro droplet

    NASA Astrophysics Data System (ADS)

    Yin, L. L.; Wang, S. P.; Shan, X. N.; Zhang, S. T.; Tao, N. J.

    2015-11-01

    Characterization of protein interactions is essential to the discovery of disease biomarkers, the development of diagnostic assays, and the screening for therapeutic drugs. Conventional flow-through kinetic measurements need relative large amount of sample that is not feasible for precious protein samples. We report a novel method to measure protein interaction kinetics in a single droplet with sub microliter or less volume. A droplet in a humidity-controlled environmental chamber is replacing the microfluidic channels as the reactor for the protein interaction. The binding process is monitored by a surface plasmon resonance imaging (SPRi) system. Association curves are obtained from the average SPR image intensity in the center area of the droplet. The washing step required by conventional flow-through SPR method is eliminated in the droplet method. The association and dissociation rate constants and binding affinity of an antigen-antibody interaction are obtained by global fitting of association curves at different concentrations. The result obtained by this method is accurate as validated by conventional flow-through SPR system. This droplet-based method not only allows kinetic studies for proteins with limited supply but also opens the door for high-throughput protein interaction study in a droplet-based microarray format that enables measurement of many to many interactions on a single chip.

  20. Evolutionary diversification of protein–protein interactions by interface add-ons

    PubMed Central

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

    2017-01-01

    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. PMID:28923934