Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus.

PubMed

Grunert, Steffen; Labudde, Dirk

2015-01-01

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.

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

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

Determination and Quantification of Molecular Interactions in Protein Films: A Review.

PubMed

Hammann, Felicia; Schmid, Markus

2014-12-10

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.

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

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

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.

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis.

PubMed

Tsuchiya, Megumi; Karim, M Rezaul; Matsumoto, Taro; Ogawa, Hidesato; Taniguchi, Hiroaki

2017-01-24

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

Instrumental biosensors: new perspectives for the analysis of biomolecular interactions.

PubMed

Nice, E C; Catimel, B

1999-04-01

The use of instrumental biosensors in basic research to measure biomolecular interactions in real time is increasing exponentially. Applications include protein-protein, protein-peptide, DNA-protein, DNA-DNA, and lipid-protein interactions. Such techniques have been applied to, for example, antibody-antigen, receptor-ligand, signal transduction, and nuclear receptor studies. This review outlines the principles of two of the most commonly used instruments and highlights specific operating parameters that will assist in optimising experimental design, data generation, and analysis.

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform.

PubMed

Gupta, Surya; De Puysseleyr, Veronic; Van der Heyden, José; Maddelein, Davy; Lemmens, Irma; Lievens, Sam; Degroeve, Sven; Tavernier, Jan; Martens, Lennart

2017-05-01

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. jan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

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.

A web server for analysis, comparison and prediction of protein ligand binding sites.

PubMed

Singh, Harinder; Srivastava, Hemant Kumar; Raghava, Gajendra P S

2016-03-25

One of the major challenges in the field of system biology is to understand the interaction between a wide range of proteins and ligands. In the past, methods have been developed for predicting binding sites in a protein for a limited number of ligands. In order to address this problem, we developed a web server named 'LPIcom' to facilitate users in understanding protein-ligand interaction. Analysis, comparison and prediction modules are available in the "LPIcom' server to predict protein-ligand interacting residues for 824 ligands. Each ligand must have at least 30 protein binding sites in PDB. Analysis module of the server can identify residues preferred in interaction and binding motif for a given ligand; for example residues glycine, lysine and arginine are preferred in ATP binding sites. Comparison module of the server allows comparing protein-binding sites of multiple ligands to understand the similarity between ligands based on their binding site. This module indicates that ATP, ADP and GTP ligands are in the same cluster and thus their binding sites or interacting residues exhibit a high level of similarity. Propensity-based prediction module has been developed for predicting ligand-interacting residues in a protein for more than 800 ligands. In addition, a number of web-based tools have been integrated to facilitate users in creating web logo and two-sample between ligand interacting and non-interacting residues. In summary, this manuscript presents a web-server for analysis of ligand interacting residue. This server is available for public use from URL http://crdd.osdd.net/raghava/lpicom .

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

PubMed

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

2011-12-01

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

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

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 different groups of proteins and interactions. We conclude that it is a useful and important measure to be used in concert with gene co-expression correlation for further insights into the characteristics of proteins in the context of their interaction network. PMID:22369639

Effective Identification of Akt Interacting Proteins by Two-Step Chemical Crosslinking, Co-Immunoprecipitation and Mass Spectrometry

PubMed Central

Huang, Bill X.; Kim, Hee-Yong

2013-01-01

Akt is a critical protein for cell survival and known to interact with various proteins. However, Akt binding partners that modulate or regulate Akt activation have not been fully elucidated. Identification of Akt-interacting proteins has been customarily achieved by co-immunoprecipitation combined with western blot and/or MS analysis. An intrinsic problem of the method is loss of interacting proteins during procedures to remove non-specific proteins. Moreover, antibody contamination often interferes with the detection of less abundant proteins. Here, we developed a novel two-step chemical crosslinking strategy to overcome these problems which resulted in a dramatic improvement in identifying Akt interacting partners. Akt antibody was first immobilized on protein A/G beads using disuccinimidyl suberate and allowed to bind to cellular Akt along with its interacting proteins. Subsequently, dithiobis[succinimidylpropionate], a cleavable crosslinker, was introduced to produce stable complexes between Akt and binding partners prior to the SDS-PAGE and nanoLC-MS/MS analysis. This approach enabled identification of ten Akt partners from cell lysates containing as low as 1.5 mg proteins, including two new potential Akt interacting partners. None of these but one protein was detectable without crosslinking procedures. The present method provides a sensitive and effective tool to probe Akt-interacting proteins. This strategy should also prove useful for other protein interactions, particularly those involving less abundant or weakly associating partners. PMID:23613850

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.

Network organization of the human autophagy system.

PubMed

Behrends, Christian; Sowa, Mathew E; Gygi, Steven P; Harper, J Wade

2010-07-01

Autophagy, the process by which proteins and organelles are sequestered in autophagosomal vesicles and delivered to the lysosome/vacuole for degradation, provides a primary route for turnover of stable and defective cellular proteins. Defects in this system are linked with numerous human diseases. Although conserved protein kinase, lipid kinase and ubiquitin-like protein conjugation subnetworks controlling autophagosome formation and cargo recruitment have been defined, our understanding of the global organization of this system is limited. Here we report a proteomic analysis of the autophagy interaction network in human cells under conditions of ongoing (basal) autophagy, revealing a network of 751 interactions among 409 candidate interacting proteins with extensive connectivity among subnetworks. Many new autophagy interaction network components have roles in vesicle trafficking, protein or lipid phosphorylation and protein ubiquitination, and affect autophagosome number or flux when depleted by RNA interference. The six ATG8 orthologues in humans (MAP1LC3/GABARAP proteins) interact with a cohort of 67 proteins, with extensive binding partner overlap between family members, and frequent involvement of a conserved surface on ATG8 proteins known to interact with LC3-interacting regions in partner proteins. These studies provide a global view of the mammalian autophagy interaction landscape and a resource for mechanistic analysis of this critical protein homeostasis pathway.

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.

The RSV F and G glycoproteins interact to form a complex on the surface of infected cells

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

Low, Kit-Wei; Tan, Timothy; Ng, Ken

2008-02-08

In this study, the interaction between the respiratory syncytial virus (RSV) fusion (F) protein, attachment (G) protein, and small hydrophobic (SH) proteins was examined. Immunoprecipitation analysis suggested that the F and G proteins exist as a protein complex on the surface of RSV-infected cells, and this conclusion was supported by ultracentrifugation analysis that demonstrated co-migration of surface-expressed F and G proteins. Although our analysis provided evidence for an interaction between the G and SH proteins, no evidence was obtained for a single protein complex involving all three of the virus proteins. These data suggest the existence of multiple virus glycoproteinmore » complexes within the RSV envelope. Although the stimulus that drives RSV-mediated membrane fusion is unknown, the association between the G and F proteins suggest an indirect role for the G protein in this process.« less

Mass Spectrometry Analysis of Spatial Protein Networks by Colocalization Analysis (COLA).

PubMed

Mardakheh, Faraz K

2017-01-01

A major challenge in systems biology is comprehensive mapping of protein interaction networks. Crucially, such interactions are often dynamic in nature, necessitating methods that can rapidly mine the interactome across varied conditions and treatments to reveal change in the interaction networks. Recently, we described a fast mass spectrometry-based method to reveal functional interactions in mammalian cells on a global scale, by revealing spatial colocalizations between proteins (COLA) (Mardakheh et al., Mol Biosyst 13:92-105, 2017). As protein localization and function are inherently linked, significant colocalization between two proteins is a strong indication for their functional interaction. COLA uses rapid complete subcellular fractionation, coupled with quantitative proteomics to generate a subcellular localization profile for each protein quantified by the mass spectrometer. Robust clustering is then applied to reveal significant similarities in protein localization profiles, indicative of colocalization.

An integrative system biology approach to unravel potential drug candidates for multiple age related disorders.

PubMed

Srivastava, Isha; Khurana, Pooja; Yadav, Mohini; Hasija, Yasha

2017-12-01

Aging, though an inevitable part of life, is becoming a worldwide social and economic problem. Healthy aging is usually marked by low probability of age related disorders. Good therapeutic approaches are still in need to cure age related disorders. Occurrence of more than one ARD in an individual, expresses the need of discovery of such target proteins, which can affect multiple ARDs. Advanced scientific and medical research technologies throughout last three decades have arrived to the point where lots of key molecular determinants affect human disorders can be examined thoroughly. In this study, we designed and executed an approach to prioritize drugs that may target multiple age related disorders. Our methodology, focused on the analysis of biological pathways and protein protein interaction networks that may contribute to the pharmacology of age related disorders, included various steps such as retrieval and analysis of data, protein-protein interaction network analysis, and statistical and comparative analysis of topological coefficients, pathway, and functional enrichment analysis, and identification of drug-target proteins. We assume that the identified molecular determinants may be prioritized for further screening as novel drug targets to cure multiple ARDs. Based on the analysis, an online tool named as 'ARDnet' has been developed to construct and demonstrate ARD interactions at the level of PPI, ARDs and ARDs protein interaction, ARDs pathway interaction and drug-target interaction. The tool is freely made available at http://genomeinformatics.dtu.ac.in/ARDNet/Index.html. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrative Analysis of Subcellular Quantitative Proteomics Studies Reveals Functional Cytoskeleton Membrane-Lipid Raft Interactions in Cancer.

PubMed

Shah, Anup D; Inder, Kerry L; Shah, Alok K; Cristino, Alexandre S; McKie, Arthur B; Gabra, Hani; Davis, Melissa J; Hill, Michelle M

2016-10-07

Lipid rafts are dynamic membrane microdomains that orchestrate molecular interactions and are implicated in cancer development. To understand the functions of lipid rafts in cancer, we performed an integrated analysis of quantitative lipid raft proteomics data sets modeling progression in breast cancer, melanoma, and renal cell carcinoma. This analysis revealed that cancer development is associated with increased membrane raft-cytoskeleton interactions, with ∼40% of elevated lipid raft proteins being cytoskeletal components. Previous studies suggest a potential functional role for the raft-cytoskeleton in the action of the putative tumor suppressors PTRF/Cavin-1 and Merlin. To extend the observation, we examined lipid raft proteome modulation by an unrelated tumor suppressor opioid binding protein cell-adhesion molecule (OPCML) in ovarian cancer SKOV3 cells. In agreement with the other model systems, quantitative proteomics revealed that 39% of OPCML-depleted lipid raft proteins are cytoskeletal components, with microfilaments and intermediate filaments specifically down-regulated. Furthermore, protein-protein interaction network and simulation analysis showed significantly higher interactions among cancer raft proteins compared with general human raft proteins. Collectively, these results suggest increased cytoskeleton-mediated stabilization of lipid raft domains with greater molecular interactions as a common, functional, and reversible feature of cancer cells.

Building biochips: a protein production pipeline

NASA Astrophysics Data System (ADS)

de Carvalho-Kavanagh, Marianne G. S.; Albala, Joanna S.

2004-06-01

Protein arrays are emerging as a practical format in which to study proteins in high-throughput using many of the same techniques as that of the DNA microarray. The key advantage to array-based methods for protein study is the potential for parallel analysis of thousands of samples in an automated, high-throughput fashion. Building protein arrays capable of this analysis capacity requires a robust expression and purification system capable of generating hundreds to thousands of purified recombinant proteins. We have developed a method to utilize LLNL-I.M.A.G.E. cDNAs to generate recombinant protein libraries using a baculovirus-insect cell expression system. We have used this strategy to produce proteins for analysis of protein/DNA and protein/protein interactions using protein microarrays in order to understand the complex interactions of proteins involved in homologous recombination and DNA repair. Using protein array techniques, a novel interaction between the DNA repair protein, Rad51B, and histones has been identified.

Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.

PubMed

Engin, H Billur; Kreisberg, Jason F; Carter, Hannah

2016-01-01

Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10(-4)) and oncogenes (Odds Ratio 1.17, P-value < 10(-3)). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10(-8)). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.

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.

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 sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/ PMID:24339765

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.

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.

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

Detecting Coevolution in Mammalian Sperm–Egg Fusion Proteins

PubMed Central

CLAW, KATRINA G.; GEORGE, RENEE D.; SWANSON, WILLIE J.

2018-01-01

SUMMARY Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm–egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm–egg proteins; interacting sperm–egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm–egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm–egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm–egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm–egg protein pairs. PMID:24644026

Detecting coevolution in mammalian sperm-egg fusion proteins.

PubMed

Claw, Katrina G; George, Renee D; Swanson, Willie J

2014-06-01

Interactions between sperm and egg proteins can occur physically between gamete surface-binding proteins, and genetically between gamete proteins that work in complementary pathways in which they may not physically interact. Physically interacting sperm-egg proteins have been functionally identified in only a few species, and none have been verified within mammals. Candidate genes on both the sperm and egg surfaces exist, but gene deletion studies do not support functional interactions between these sperm-egg proteins; interacting sperm-egg proteins thus remain elusive. Cooperative gamete proteins undergo rapid evolution, and it is predicted that these sperm-egg proteins will also have correlated evolutionary rates due to compensatory changes on both the sperm and egg. To explore potential physical and genetic interactions in sperm-egg proteins, we sequenced four candidate genes from diverse primate species, and used regression and likelihood methods to test for signatures of coevolution between sperm-egg gene pairs. With both methods, we found that the egg protein CD9 coevolves with the sperm protein IZUMO1, suggesting a physical or genetic interaction occurs between them. With regression analysis, we found that CD9 and CRISP2 have correlated rates of evolution, and with likelihood analysis, that CD9 and CRISP1 have correlated rates. This suggests that the different tests may reflect different levels of interaction, be it physical or genetic. Coevolution tests thus provide an exploratory method for detecting potentially interacting sperm-egg protein pairs. © 2014 Wiley Periodicals, Inc.

Ecto-Fc MS identifies ligand-receptor interactions through extracellular domain Fc fusion protein baits and shotgun proteomic analysis

PubMed Central

Savas, Jeffrey N.; De Wit, Joris; Comoletti, Davide; Zemla, Roland; Ghosh, Anirvan

2015-01-01

Ligand-receptor interactions represent essential biological triggers which regulate many diverse and important cellular processes. We have developed a discovery-based proteomic biochemical protocol which couples affinity purification with multidimensional liquid chromatographic tandem mass spectrometry (LCLC-MS/MS) and bioinformatic analysis. Compared to previous approaches, our analysis increases sensitivity, shortens analysis duration, and boosts comprehensiveness. In this protocol, receptor extracellular domains are fused with the Fc region of IgG to generate fusion proteins that are purified from transfected HEK293T cells. These “ecto-Fcs” are coupled to protein A beads and serve as baits for binding assays with prey proteins extracted from rodent brain. After capture, the affinity purified proteins are digested into peptides and comprehensively analyzed by LCLC-MS/MS with ion trap mass spectrometers. In four working days, this protocol can generate shortlists of candidate ligand-receptor protein-protein interactions. Our “Ecto-Fc MS” approach outperforms antibody-based approaches and provides a reproducible and robust framework to identify extracellular ligand – receptor interactions. PMID:25101821

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.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

PubMed

Serçinoglu, Onur; Ozbek, Pemra

2018-05-25

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

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 interactions. (c) 2009 Wiley Periodicals, Inc.

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.

Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks

PubMed Central

Saha, Sudipto; Dazard, Jean-Eudes; Xu, Hua; Ewing, Rob M.

2013-01-01

Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks. PMID:22845868

Cell Division in genus Corynebacterium: protein-protein interaction and molecular docking of SepF and FtsZ in the understanding of cytokinesis in pathogenic species.

PubMed

Oliveira, Alberto F; Folador, Edson L; Gomide, Anne C P; Goes-Neto, Aristóteles; Azevedo, Vasco A C; Wattam, Alice R

2018-02-15

The genus Corynebacterium includes species of great importance in medical, veterinary and biotechnological fields. The genus-specific families (PLfams) from PATRIC have been used to observe conserved proteins associated to all species. Our results showed a large number of conserved proteins that are associated with the cellular division process. Was not observe in our results other proteins like FtsA and ZapA that interact with FtsZ. Our findings point that SepF overlaps the function of this proteins explored by molecular docking, protein-protein interaction and sequence analysis. Transcriptomic analysis showed that these two (Sepf and FtsZ) proteins can be expressed in different conditions together. The work presents novelties on molecules participating in the cell division event, from the interaction of FtsZ and SepF, as new therapeutic targets.

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

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Quantitative assessment of RNA-protein interactions with high-throughput sequencing-RNA affinity profiling.

PubMed

Ozer, Abdullah; Tome, Jacob M; Friedman, Robin C; Gheba, Dan; Schroth, Gary P; Lis, John T

2015-08-01

Because RNA-protein interactions have a central role in a wide array of biological processes, methods that enable a quantitative assessment of these interactions in a high-throughput manner are in great demand. Recently, we developed the high-throughput sequencing-RNA affinity profiling (HiTS-RAP) assay that couples sequencing on an Illumina GAIIx genome analyzer with the quantitative assessment of protein-RNA interactions. This assay is able to analyze interactions between one or possibly several proteins with millions of different RNAs in a single experiment. We have successfully used HiTS-RAP to analyze interactions of the EGFP and negative elongation factor subunit E (NELF-E) proteins with their corresponding canonical and mutant RNA aptamers. Here we provide a detailed protocol for HiTS-RAP that can be completed in about a month (8 d hands-on time). This includes the preparation and testing of recombinant proteins and DNA templates, clustering DNA templates on a flowcell, HiTS and protein binding with a GAIIx instrument, and finally data analysis. We also highlight aspects of HiTS-RAP that can be further improved and points of comparison between HiTS-RAP and two other recently developed methods, quantitative analysis of RNA on a massively parallel array (RNA-MaP) and RNA Bind-n-Seq (RBNS), for quantitative analysis of RNA-protein interactions.

EMSA Analysis of DNA Binding By Rgg Proteins

PubMed Central

LaSarre, Breah; Federle, Michael J.

2016-01-01

In bacteria, interaction of various proteins with DNA is essential for the regulation of specific target gene expression. Electrophoretic mobility shift assay (EMSA) is an in vitro approach allowing for the visualization of these protein-DNA interactions. Rgg proteins comprise a family of transcriptional regulators widespread among the Firmicutes. Some of these proteins function independently to regulate target gene expression, while others have now been demonstrated to function as effectors of cell-to-cell communication, having regulatory activities that are modulated via direct interaction with small signaling peptides. EMSA analysis can be used to assess DNA binding of either type of Rgg protein. EMSA analysis of Rgg protein activity has facilitated in vitro confirmation of regulatory targets, identification of precise DNA binding sites via DNA probe mutagenesis, and characterization of the mechanism by which some cognate signaling peptides modulate Rgg protein function (e.g. interruption of DNA-binding in some cases). PMID:27430004

EMSA Analysis of DNA Binding By Rgg Proteins.

PubMed

LaSarre, Breah; Federle, Michael J

2013-08-20

In bacteria, interaction of various proteins with DNA is essential for the regulation of specific target gene expression. Electrophoretic mobility shift assay (EMSA) is an in vitro approach allowing for the visualization of these protein-DNA interactions. Rgg proteins comprise a family of transcriptional regulators widespread among the Firmicutes. Some of these proteins function independently to regulate target gene expression, while others have now been demonstrated to function as effectors of cell-to-cell communication, having regulatory activities that are modulated via direct interaction with small signaling peptides. EMSA analysis can be used to assess DNA binding of either type of Rgg protein. EMSA analysis of Rgg protein activity has facilitated in vitro confirmation of regulatory targets, identification of precise DNA binding sites via DNA probe mutagenesis, and characterization of the mechanism by which some cognate signaling peptides modulate Rgg protein function ( e.g. interruption of DNA-binding in some cases).

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.

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.

Structural basis of a rationally rewired protein-protein interface critical to bacterial signaling

PubMed Central

Podgornaia, Anna I.; Casino, Patricia; Marina, Alberto; Laub, Michael T.

2013-01-01

Summary Two-component signal transduction systems typically involve a sensor histidine kinase that specifically phosphorylates a single, cognate response regulator. This protein-protein interaction relies on molecular recognition via a small set of residues in each protein. To better understand how these residues determine the specificity of kinase-substrate interactions, we rationally rewired the interaction interface of a Thermotoga maritima two-component system, HK853-RR468, to match that found in a different two-component system, E. coli PhoR-PhoB. The rewired proteins interacted robustly with each other, but no longer interacted with the parent proteins. Analysis of the crystal structures of the wild-type and mutant protein complexes, along with a systematic mutagenesis study, reveals how individual mutations contribute to the rewiring of interaction specificity. Our approach and conclusions have implications for studies of other protein-protein interactions, protein evolution, and the design of novel protein interfaces. PMID:23954504

Protein structure similarity from Principle Component Correlation analysis.

PubMed

Zhou, Xiaobo; Chou, James; Wong, Stephen T C

2006-01-25

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Dual-Color Click Beetle Luciferase Heteroprotein Fragment Complementation Assays

PubMed Central

Villalobos, Victor; Naik, Snehal; Bruinsma, Monique; Dothager, Robin S.; Pan, Mei-Hsiu; Samrakandi, Mustapha; Moss, Britney; Elhammali, Adnan; Piwnica-Worms, David

2010-01-01

Summary Understanding the functional complexity of protein interactions requires mapping biomolecular complexes within the cellular environment over biologically-relevant time scales. Herein we describe a novel set of reversible, multicolored heteroprotein complementation fragments based on various firefly and click beetle luciferases that utilize the same substrate, D-luciferin. Luciferase heteroprotein fragment complementation systems enabled dual-color quantification of two discreet pairs of interacting proteins simultaneously or two distinct proteins interacting with a third shared protein in live cells. Using real-time analysis of click beetle green and click beetle red luciferase heteroprotein fragment complementation applied to β-TrCP, an E3-ligase common to the regulation of both β-catenin and IκBα, GSK3β was identified as a novel candidate kinase regulating IκBα processing. These dual-color protein interaction switches may enable directed dynamic analysis of a variety of protein interactions in living cells. PMID:20851351

Cloning, expression analysis, and chromosomal localization of HIP1R, an isolog of huntingtin interacting protein (HIP1).

PubMed

Seki, N; Muramatsu, M; Sugano, S; Suzuki, Y; Nakagawara, A; Ohhira, M; Hayashi, A; Hori, T; Saito, T

1998-01-01

Huntington disease (HD) is an inherited neurodegenerative disorder which is associated with CAG expansion in the coding region of the gene for huntingtin protein. Recently, a huntingtin interacting protein, HIP1, was isolated by the yeast two-hybrid system. Here we report the isolation of a cDNA clone for HIP1R (huntingtin interacting protein-1 related), which encodes a predicted protein product sharing a striking homology with HIP1. RT-PCR analysis showed that the messenger RNA was ubiquitously expressed in various human tissues. Based on PCR-assisted analysis of a radiation hybrid panel and fluorescence in situ hybridization, HIP1R was localized to the q24 region of chromosome 12.

Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome.

PubMed

Chang, Dong W; Hayashi, Shinichi; Gharib, Sina A; Vaisar, Tomas; King, S Trevor; Tsuchiya, Mitsuhiro; Ruzinski, John T; Park, David R; Matute-Bello, Gustavo; Wurfel, Mark M; Bumgarner, Roger; Heinecke, Jay W; Martin, Thomas R

2008-10-01

Acute lung injury causes complex changes in protein expression in the lungs. Whereas most prior studies focused on single proteins, newer methods allowing the simultaneous study of many proteins could lead to a better understanding of pathogenesis and new targets for treatment. The purpose of this study was to examine the changes in protein expression in the bronchoalveolar lavage fluid (BALF) of patients during the course of the acute respiratory distress syndrome (ARDS). Using two-dimensional difference gel electrophoresis (DIGE), the expression of proteins in the BALF from patients on Days 1 (n = 7), 3 (n = 8), and 7 (n = 5) of ARDS were compared with findings in normal volunteers (n = 9). The patterns of protein expression were analyzed using principal component analysis (PCA). Biological processes that were enriched in the BALF proteins of patients with ARDS were identified using Gene Ontology (GO) analysis. Protein networks that model the protein interactions in the BALF were generated using Ingenuity Pathway Analysis. An average of 991 protein spots were detected using DIGE. Of these, 80 protein spots, representing 37 unique proteins in all of the fluids, were identified using mass spectrometry. PCA confirmed important differences between the proteins in the ARDS and normal samples. GO analysis showed that these differences are due to the enrichment of proteins involved in inflammation, infection, and injury. The protein network analysis showed that the protein interactions in ARDS are complex and redundant, and revealed unexpected central components in the protein networks. Proteomics and protein network analysis reveals the complex nature of lung protein interactions in ARDS. The results provide new insights about protein networks in injured lungs, and identify novel mediators that are likely to be involved in the pathogenesis and progression of acute lung injury.

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

Amino Acid Interaction (INTAA) web server.

PubMed

Galgonek, Jakub; Vymetal, Jirí; Jakubec, David; Vondrášek, Jirí

2017-07-03

Large biomolecules-proteins and nucleic acids-are composed of building blocks which define their identity, properties and binding capabilities. In order to shed light on the energetic side of interactions of amino acids between themselves and with deoxyribonucleotides, we present the Amino Acid Interaction web server (http://bioinfo.uochb.cas.cz/INTAA/). INTAA offers the calculation of the residue Interaction Energy Matrix for any protein structure (deposited in Protein Data Bank or submitted by the user) and a comprehensive analysis of the interfaces in protein-DNA complexes. The Interaction Energy Matrix web application aims to identify key residues within protein structures which contribute significantly to the stability of the protein. The application provides an interactive user interface enhanced by 3D structure viewer for efficient visualization of pairwise and net interaction energies of individual amino acids, side chains and backbones. The protein-DNA interaction analysis part of the web server allows the user to view the relative abundance of various configurations of amino acid-deoxyribonucleotide pairs found at the protein-DNA interface and the interaction energies corresponding to these configurations calculated using a molecular mechanical force field. The effects of the sugar-phosphate moiety and of the dielectric properties of the solvent on the interaction energies can be studied for the various configurations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data

PubMed Central

Schweppe, Devin K.; Zheng, Chunxiang; Chavez, Juan D.; Navare, Arti T.; Wu, Xia; Eng, Jimmy K.; Bruce, James E.

2016-01-01

Motivation: Large-scale chemical cross-linking with mass spectrometry (XL-MS) analyses are quickly becoming a powerful means for high-throughput determination of protein structural information and protein–protein interactions. Recent studies have garnered thousands of cross-linked interactions, yet the field lacks an effective tool to compile experimental data or access the network and structural knowledge for these large scale analyses. We present XLinkDB 2.0 which integrates tools for network analysis, Protein Databank queries, modeling of predicted protein structures and modeling of docked protein structures. The novel, integrated approach of XLinkDB 2.0 enables the holistic analysis of XL-MS protein interaction data without limitation to the cross-linker or analytical system used for the analysis. Availability and Implementation: XLinkDB 2.0 can be found here, including documentation and help: http://xlinkdb.gs.washington.edu/. Contact: jimbruce@uw.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153666

R Script Approach to Infer Toxoplasma Infection Mechanisms From Microarrays and Domain-Domain Protein Interactions

PubMed Central

Arenas, Ailan F; Salcedo, Gladys E; Gomez-Marin, Jorge E

2017-01-01

Pathogen-host protein-protein interaction systems examine the interactions between the protein repertoires of 2 distinct organisms. Some of these pathogen proteins interact with the host protein system and may manipulate it for their own advantages. In this work, we designed an R script by concatenating 2 functions called rowDM and rowCVmed to infer pathogen-host interaction using previously reported microarray data, including host gene enrichment analysis and the crossing of interspecific domain-domain interactions. We applied this script to the Toxoplasma-host system to describe pathogen survival mechanisms from human, mouse, and Toxoplasma Gene Expression Omnibus series. Our outcomes exhibited similar results with previously reported microarray analyses, but we found other important proteins that could contribute to toxoplasma pathogenesis. We observed that Toxoplasma ROP38 is the most differentially expressed protein among toxoplasma strains. Enrichment analysis and KEGG mapping indicated that the human retinal genes most affected by Toxoplasma infections are those related to antiapoptotic mechanisms. We suggest that proteins PIK3R1, PRKCA, PRKCG, PRKCB, HRAS, and c-JUN could be the possible substrates for differentially expressed Toxoplasma kinase ROP38. Likewise, we propose that Toxoplasma causes overexpression of apoptotic suppression human genes. PMID:29317802

The Interaction Properties of the Human Rab GTPase Family – A Comparative Analysis Reveals Determinants of Molecular Binding Selectivity

PubMed Central

Stein, Matthias; Pilli, Manohar; Bernauer, Sabine; Habermann, Bianca H.; Zerial, Marino; Wade, Rebecca C.

2012-01-01

Background Rab GTPases constitute the largest subfamily of the Ras protein superfamily. Rab proteins regulate organelle biogenesis and transport, and display distinct binding preferences for effector and activator proteins, many of which have not been elucidated yet. The underlying molecular recognition motifs, binding partner preferences and selectivities are not well understood. Methodology/Principal Findings Comparative analysis of the amino acid sequences and the three-dimensional electrostatic and hydrophobic molecular interaction fields of 62 human Rab proteins revealed a wide range of binding properties with large differences between some Rab proteins. This analysis assists the functional annotation of Rab proteins 12, 14, 26, 37 and 41 and provided an explanation for the shared function of Rab3 and 27. Rab7a and 7b have very different electrostatic potentials, indicating that they may bind to different effector proteins and thus, exert different functions. The subfamily V Rab GTPases which are associated with endosome differ subtly in the interaction properties of their switch regions, and this may explain exchange factor specificity and exchange kinetics. Conclusions/Significance We have analysed conservation of sequence and of molecular interaction fields to cluster and annotate the human Rab proteins. The analysis of three dimensional molecular interaction fields provides detailed insight that is not available from a sequence-based approach alone. Based on our results, we predict novel functions for some Rab proteins and provide insights into their divergent functions and the determinants of their binding partner selectivity. PMID:22523562

SCOWLP classification: Structural comparison and analysis of protein binding regions

PubMed Central

Teyra, Joan; Paszkowski-Rogacz, Maciej; Anders, Gerd; Pisabarro, M Teresa

2008-01-01

Background Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design. Description Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed. We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions. The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family. Conclusion The current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at . PMID:18182098

Robust co-regulation of tyrosine phosphorylation sites on proteins reveals novel protein interactions†

PubMed Central

Naegle, Kristen M.; White, Forest M.; Lauffenburger, Douglas A.; Yaffe, Michael B.

2012-01-01

Cell signaling networks propagate information from extracellular cues via dynamic modulation of protein–protein interactions in a context-dependent manner. Networks based on receptor tyrosine kinases (RTKs), for example, phosphorylate intracellular proteins in response to extracellular ligands, resulting in dynamic protein–protein interactions that drive phenotypic changes. Most commonly used methods for discovering these protein–protein interactions, however, are optimized for detecting stable, longer-lived complexes, rather than the type of transient interactions that are essential components of dynamic signaling networks such as those mediated by RTKs. Substrate phosphorylation downstream of RTK activation modifies substrate activity and induces phospho-specific binding interactions, resulting in the formation of large transient macromolecular signaling complexes. Since protein complex formation should follow the trajectory of events that drive it, we reasoned that mining phosphoproteomic datasets for highly similar dynamic behavior of measured phosphorylation sites on different proteins could be used to predict novel, transient protein–protein interactions that had not been previously identified. We applied this method to explore signaling events downstream of EGFR stimulation. Our computational analysis of robustly co-regulated phosphorylation sites, based on multiple clustering analysis of quantitative time-resolved mass-spectrometry phosphoproteomic data, not only identified known sitewise-specific recruitment of proteins to EGFR, but also predicted novel, a priori interactions. A particularly intriguing prediction of EGFR interaction with the cytoskeleton-associated protein PDLIM1 was verified within cells using co-immunoprecipitation and in situ proximity ligation assays. Our approach thus offers a new way to discover protein–protein interactions in a dynamic context- and phosphorylation site-specific manner. PMID:22851037

Restricted mobility of side chains on concave surfaces of solenoid proteins may impart heightened potential for intermolecular interactions.

PubMed

Ramya, L; Gautham, N; Chaloin, Laurent; Kajava, Andrey V

2015-09-01

Significant progress has been made in the determination of the protein structures with their number today passing over a hundred thousand structures. The next challenge is the understanding and prediction of protein-protein and protein-ligand interactions. In this work we address this problem by analyzing curved solenoid proteins. Many of these proteins are considered as "hub molecules" for their high potential to interact with many different molecules and to be a scaffold for multisubunit protein machineries. Our analysis of these structures through molecular dynamics simulations reveals that the mobility of the side-chains on the concave surfaces of the solenoids is lower than on the convex ones. This result provides an explanation to the observed preferential binding of the ligands, including small and flexible ligands, to the concave surface of the curved solenoid proteins. The relationship between the landscapes and dynamic properties of the protein surfaces can be further generalized to the other types of protein structures and eventually used in the computer algorithms, allowing prediction of protein-ligand interactions by analysis of protein surfaces. © 2015 Wiley Periodicals, Inc.

AFAL: a web service for profiling amino acids surrounding ligands in proteins

NASA Astrophysics Data System (ADS)

Arenas-Salinas, Mauricio; Ortega-Salazar, Samuel; Gonzales-Nilo, Fernando; Pohl, Ehmke; Holmes, David S.; Quatrini, Raquel

2014-11-01

With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.

AFAL: a web service for profiling amino acids surrounding ligands in proteins.

PubMed

Arenas-Salinas, Mauricio; Ortega-Salazar, Samuel; Gonzales-Nilo, Fernando; Pohl, Ehmke; Holmes, David S; Quatrini, Raquel

2014-11-01

With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html ). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.

Topology association analysis in weighted protein interaction network for gene prioritization

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Gene fusion analysis in the battle against the African endemic sleeping sickness.

PubMed

Trimpalis, Philip; Koumandou, Vassiliki Lila; Pliakou, Evangelia; Anagnou, Nicholas P; Kossida, Sophia

2013-01-01

The protozoan Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance (BLAST e-value, domain length etc.), (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.

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/.

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

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 reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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.

Characterizing natural colloidal/particulate-protein interactions using fluorescence-based techniques and principal component analysis.

PubMed

Peiris, Ramila H; Ignagni, Nicholas; Budman, Hector; Moresoli, Christine; Legge, Raymond L

2012-09-15

Characterization of the interactions between natural colloidal/particulate- and protein-like matter is important for understanding their contribution to different physiochemical phenomena like membrane fouling, adsorption of bacteria onto surfaces and various applications of nanoparticles in nanomedicine and nanotoxicology. Precise interpretation of the extent of such interactions is however hindered due to the limitations of most characterization methods to allow rapid, sensitive and accurate measurements. Here we report on a fluorescence-based excitation-emission matrix (EEM) approach in combination with principal component analysis (PCA) to extract information related to the interaction between natural colloidal/particulate- and protein-like matter. Surface plasmon resonance (SPR) analysis and fiber-optic probe based surface fluorescence measurements were used to confirm that the proposed approach can be used to characterize colloidal/particulate-protein interactions at the physical level. This method has potential to be a fundamental measurement of these interactions with the advantage that it can be performed rapidly and with high sensitivity. Copyright © 2012 Elsevier B.V. All rights reserved.

webPIPSA: a web server for the comparison of protein interaction properties

PubMed Central

Richter, Stefan; Wenzel, Anne; Stein, Matthias; Gabdoulline, Razif R.; Wade, Rebecca C.

2008-01-01

Protein molecular interaction fields are key determinants of protein functionality. PIPSA (Protein Interaction Property Similarity Analysis) is a procedure to compare and analyze protein molecular interaction fields, such as the electrostatic potential. PIPSA may assist in protein functional assignment, classification of proteins, the comparison of binding properties and the estimation of enzyme kinetic parameters. webPIPSA is a web server that enables the use of PIPSA to compare and analyze protein electrostatic potentials. While PIPSA can be run with downloadable software (see http://projects.eml.org/mcm/software/pipsa), webPIPSA extends and simplifies a PIPSA run. This allows non-expert users to perform PIPSA for their protein datasets. With input protein coordinates, the superposition of protein structures, as well as the computation and analysis of electrostatic potentials, is automated. The results are provided as electrostatic similarity matrices from an all-pairwise comparison of the proteins which can be subjected to clustering and visualized as epograms (tree-like diagrams showing electrostatic potential differences) or heat maps. webPIPSA is freely available at: http://pipsa.eml.org. PMID:18420653

Application of linker technique to trap transiently interacting protein complexes for structural studies

PubMed Central

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

2016-01-01

Protein-protein interactions are key events controlling several biological processes. We have developed and employed a method to trap transiently interacting protein complexes for structural studies using glycine-rich linkers to fuse interacting partners, one of which is unstructured. Initial steps involve isothermal titration calorimetry to identify the minimum binding region of the unstructured protein in its interaction with its stable binding partner. This is followed by computational analysis to identify the approximate site of the interaction and to design an appropriate linker length. Subsequently, fused constructs are generated and characterized using size exclusion chromatography and dynamic light scattering experiments. The structure of the chimeric protein is then solved by crystallization, and validated both in vitro and in vivo by substituting key interacting residues of the full length, unlinked proteins with alanine. This protocol offers the opportunity to study crucial and currently unattainable transient protein interactions involved in various biological processes. PMID:26985443

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 protein interaction reporter (PIR) technology to illustrate how viruses exploit host proteins during plant infection. PIR technology enabled our team to precisely describe the sites of functional virus-virus, virus-host, and host-host protein interactions using a mass spectrometry analysis that takes just a few hours. Applications of PIR technology in host-pathogen interactions will enable researchers studying recalcitrant pathogens, such as animal pathogens where host proteins are incorporated directly into the infectious agents, to investigate how proteins interact during infection and transmission as well as develop new tools for interdiction and therapy. PMID:26656710

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 obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.

SIRIUS. An automated method for the analysis of the preferred packing arrangements between protein groups.

PubMed

Singh, J; Thornton, J M

1990-02-05

Automated methods have been developed to determine the preferred packing arrangement between interacting protein groups. A suite of FORTRAN programs, SIRIUS, is described for calculating and analysing the geometries of interacting protein groups using crystallographically derived atomic co-ordinates. The programs involved in calculating the geometries search for interacting pairs of protein groups using a distance criterion, and then calculate the spatial disposition and orientation of the pair. The second set of programs is devoted to analysis. This involves calculating the observed and expected distributions of the angles and assessing the statistical significance of the difference between the two. A database of the geometries of the 400 combinations of side-chain to side-chain interaction has been created. The approach used in analysing the geometrical information is illustrated here with specific examples of interactions between side-chains, peptide groups and particular types of atom. At the side-chain level, an analysis of aromatic-amino interactions, and the interactions of peptide carbonyl groups with arginine residues is presented. At the atomic level the analyses include the spatial disposition of oxygen atoms around tyrosine residues, and the frequency and type of contact between carbon, nitrogen and oxygen atoms. This information is currently being applied to the modelling of protein interactions.

[Identification of the interacting proteins with S100A8 or S100A9 by affinity purification and mass spectrometry].

PubMed

Wang, Jing; Zhang, Xuemei; Li, Zheng; Li, Xiayu; Ma, Jian; Shen, Shourong

2017-04-28

To identify the interacting proteins with S100A8 or S100A9 in HEK293 cell line by flag-tag affinity purification and liquid chromatography mass spectrometry/mass spectrometry (LC-MS/MS).
 Methods: The p3×Flag-CMV-S100A8 and p3×Flag-CMV-S100A9 expression vectors were constructed by inserting S100A8 or S100A9 coding sequence. The recombinant plasmids were then transfected into HEK293 cells. Affinity purification and LC-MS/MS were applied to identify the proteins interacting with S100A8 or S100A9. Bioinformatics analysis was used to seek the gene ontology of the interacting proteins. Co-immunoprecipitation (Co-IP) was applied to confirm the proteins interacted with S100A8 or S100A9.
 Results: Fourteen proteins including pyruvate kinase, muscle (PKM), nucleophosmin (NPM1) and eukaryotic translation initiation factor 5A (EIF5A), which potentially interacted with S100A8, were successfully identified by Flag-tag affinity purification followed by LC-MS/MS analysis. Six proteins, such as tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein epsilon (14-3-3ε) and PKM, which potentially interacted with S100A9, were successfully identified. Gene ontology analysis of the identified proteins suggested that proteins interacted with S100A8 or S100A9 were involved in several biological pathways, including canonical glycolysis, positive regulation of NF-κB transcription factor activity, negative regulation of apoptotic process, cell-cell adhesion, etc. Co-IP experiment confirmed that PKM2 can interact with both S100A8 and S100A9, and 14-3-3ε can interact with S100A8.
 Conclusion: PKM2 is identified to interact with both S100A8 and S100A9, while 14-3-3ε can interact with S100A9. These results may provide a new clue for the role of S100A8 or S100A9 in the progression of colitis-associated colorectal cancer.

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.

Structural modeling and molecular simulation analysis of HvAP2/EREBP from barley.

PubMed

Pandey, Bharati; Sharma, Pradeep; Tyagi, Chetna; Goyal, Sukriti; Grover, Abhinav; Sharma, Indu

2016-06-01

AP2/ERF transcription factors play a critical role in plant development and stress adaptation. This study reports the three-dimensional ab initio-based model of AP2/EREBP protein of barley and its interaction with DNA. Full-length coding sequence of HvAP2/EREBP gene isolated from two Indian barley cultivars, RD 2503 and RD 31, was used to model the protein. Of five protein models obtained, the one with lowest C-score was chosen for further analysis. The N- and C-terminal regions of HvAP2 protein were found to be highly disordered. The dynamic properties of AP2/EREBP and its interaction with DNA were investigated by molecular dynamics simulation. Analysis of trajectories from simulation yielded the equilibrated conformation between 2-10ns for protein and 7-15ns for protein-DNA complex. We established relationship between DNA having GCC box and DNA-binding domain of HvAP2/EREBP was established by modeling 11-base-pair-long nucleotide sequence and HvAP2/EREBP protein using ab initio method. Analysis of protein-DNA interaction showed that a β-sheet motif constituting amino acid residues THR105, ARG100, ARG93, and ARG83 seems to play important role in stabilizing the complex as they form strong hydrogen bond interactions with the DNA motif. Taken together, this study provides first-hand comprehensive information detailing structural conformation and interactions of HvAP2/EREBP proteins in barley. The study intensifies the role of computational approaches for preliminary examination of unknown proteins in the absence of experimental information. It also provides molecular insight into protein-DNA binding for understanding and enhancing abiotic stress resistance for improving the water use efficiency in crop plants.

Molecular dynamics simulations and statistical coupling analysis reveal functional coevolution network of oncogenic mutations in the CDKN2A-CDK6 complex.

PubMed

Wang, Jingwen; Zhao, Yuqi; Wang, Yanjie; Huang, Jingfei

2013-01-16

Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this study, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on the CDK6-CDKN2A protein complex to evaluate coevolution between proteins. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37 interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in the CDK6-CDKN2A complex can be helpful for designing protein engineering experiments. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Influence of the R823W mutation on the interaction of the ANKS6-ANKS3: insights from molecular dynamics simulation and free energy analysis.

PubMed

Kan, Wei; Fang, Fengqin; Chen, Lin; Wang, Ruige; Deng, Qigang

2016-05-01

The sterile alpha motif (SAM) domain of the protein ANKS6, a protein-protein interaction domain, is responsible for autosomal dominant polycystic kidney disease. Although the disease is the result of the R823W point mutation in the SAM domain of the protein ANKS6, the molecular details are still unclear. We applied molecular dynamics simulations, the principal component analysis, and the molecular mechanics Poisson-Boltzmann surface area binding free energy calculation to explore the structural and dynamic effects of the R823W point mutation on the complex ANKS6-ANKS3 (PDB ID: 4NL9) in comparison to the wild proteins. The energetic analysis presents that the wild type has a more stable structure than the mutant. The R823W point mutation not only disrupts the structure of the ANKS6 SAM domain but also negatively affects the interaction of the ANKS6-ANKS3. These results further clarify the previous experiments to understand the ANKS6-ANKS3 interaction comprehensively. In summary, this study would provide useful suggestions to understand the interaction of these proteins and their fatal action on mediating kidney function.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

PubMed

Lambert, Jean-Philippe; Ivosev, Gordana; Couzens, Amber L; Larsen, Brett; Taipale, Mikko; Lin, Zhen-Yuan; Zhong, Quan; Lindquist, Susan; Vidal, Marc; Aebersold, Ruedi; Pawson, Tony; Bonner, Ron; Tate, Stephen; Gingras, Anne-Claude

2013-12-01

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

An automated real-time microscopy system for analysis of fluorescence resonance energy transfer

NASA Astrophysics Data System (ADS)

Bernardini, André; Wotzlaw, Christoph; Lipinski, Hans-Gerd; Fandrey, Joachim

2010-05-01

Molecular imaging based on Fluorescence Resonance Energy Transfer (FRET) is widely used in cellular physiology both for protein-protein interaction analysis and detecting conformational changes of single proteins, e.g. during activation of signaling cascades. However, getting reliable results from FRET measurements is still hampered by methodological problems such as spectral bleed through, chromatic aberration, focal plane shifts and false positive FRET. Particularly false positive FRET signals caused by random interaction of the fluorescent dyes can easily lead to misinterpretation of the data. This work introduces a Nipkow Disc based FRET microscopy system, that is easy to operate without expert knowledge of FRET. The system automatically accounts for all relevant sources of errors and provides various result presentations of two, three and four dimensional FRET data. Two examples are given to demonstrate the scope of application. An interaction analysis of the two subunits of the hypoxia-inducible transcription factor 1 demonstrates the use of the system as a tool for protein-protein interaction analysis. As an example for time lapse observations, the conformational change of the fluorophore labeled heat shock protein 33 in the presence of oxidant stress is shown.

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.

Design and implementation of bimolecular fluorescence complementation (BiFC) assays for the visualization of protein interactions in living cells.

PubMed

Kerppola, Tom K

2006-01-01

Bimolecular fluorescence complementation (BiFC) analysis enables direct visualization of protein interactions in living cells. The BiFC assay is based on the discoveries that two non-fluorescent fragments of a fluorescent protein can form a fluorescent complex and that the association of the fragments can be facilitated when they are fused to two proteins that interact with each other. BiFC must be confirmed by parallel analysis of proteins in which the interaction interface has been mutated. It is not necessary for the interaction partners to juxtapose the fragments within a specific distance of each other because they can associate when they are tethered to a complex with flexible linkers. It is also not necessary for the interaction partners to form a complex with a long half-life or a high occupancy since the fragments can associate in a transient complex and un-associated fusion proteins do not interfere with detection of the complex. Many interactions can be visualized when the fusion proteins are expressed at levels comparable to their endogenous counterparts. The BiFC assay has been used for the visualization of interactions between many types of proteins in different subcellular locations and in different cell types and organisms. It is technically straightforward and can be performed using a regular fluorescence microscope and standard molecular biology and cell culture reagents.

Two-dimensional blue native/SDS-PAGE analysis of whole cell lysate protein complexes of rice in response to salt stress.

PubMed

Hashemi, Amenehsadat; Gharechahi, Javad; Nematzadeh, Ghorbanali; Shekari, Faezeh; Hosseini, Seyed Abdollah; Salekdeh, Ghasem Hosseini

2016-08-01

To understand the biology of a plant in response to stress, insight into protein-protein interactions, which almost define cell behavior, is thought to be crucial. Here, we provide a comparative complexomics analysis of leaf whole cell lysate of two rice genotypes with contrasting responses to salt using two-dimensional blue native/SDS-PAGE (2D-BN/SDS-PAGE). We aimed to identify changes in subunit composition and stoichiometry of protein complexes elicited by salt. Using mild detergent for protein complex solubilization, we were able to identify 9 protein assemblies as hetero-oligomeric and 30 as homo-oligomeric complexes. A total of 20 proteins were identified as monomers in the 2D-BN/SDS-PAGE gels. In addition to identifying known protein complexes that confirm the technical validity of our analysis, we were also able to discover novel protein-protein interactions. Interestingly, an interaction was detected for glycolytic enzymes enolase (ENO1) and triosephosphate isomerase (TPI) and also for a chlorophyll a-b binding protein and RuBisCo small subunit. To show changes in subunit composition and stoichiometry of protein assemblies during salt stress, the differential abundance of interacting proteins was compared between salt-treated and control plants. A detailed exploration of some of the protein complexes provided novel insight into the function, composition, stoichiometry and dynamics of known and previously uncharacterized protein complexes in response to salt stress. Copyright © 2016 Elsevier GmbH. All rights reserved.

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.

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

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

Use of high-throughput mass spectrometry to elucidate host pathogen interactions in Salmonella

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

Rodland, Karin D.; Adkins, Joshua N.; Ansong, Charles

Capabilities in mass spectrometry are evolving rapidly, with recent improvements in sensitivity, data analysis, and most important, from the standpoint of this review, much higher throughput allowing analysis of many samples in a single day. This short review describes how these improvements in mass spectrometry can be used to dissect host-pathogen interactions using Salmonella as a model system. This approach enabled direct identification of the majority of annotated Salmonella proteins, quantitation of expression changes under various in vitro growth conditions, and new insights into virulence and expression of Salmonella proteins within host cell cells. One of the most significant findingsmore » is that a very high percentage of the all annotated genes (>20%) in Salmonella are regulated post-transcriptionally. In addition, new and unexpected interactions have been identified for several Salmonella virulence regulators that involve protein-protein interactions, suggesting additional functions of these regulators in coordinating virulence expression. Overall high throughput mass spectrometry provides a new view of pathogen-host interactions emphasizing the protein products and defining how protein interactions determine the outcome of infection.« less

Structural and Functional Analysis of VQ Motif-Containing Proteins in Arabidopsis as Interacting Proteins of WRKY Transcription Factors1[W][OA

PubMed Central

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

2012-01-01

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. PMID:22535423

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.

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/.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs.

PubMed

Chakraborty, Chiranjib; Mallick, Bidyut; Sharma, Ashish Ranjan; Sharma, Garima; Jagga, Supriya; Doss, C George Priya; Nam, Ju-Suk; Lee, Sang-Soo

2017-01-01

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (E VHD ) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Emory University: High-Throughput Protein-Protein Interaction Analysis for Hippo Pathway Profiling | Office of Cancer Genomics

Cancer.gov

The CTD2 Center at Emory University used high-throughput protein-protein interaction (PPI) mapping for Hippo signaling pathway profiling to rapidly unveil promising PPIs as potential therapeutic targets and advance functional understanding of signaling circuitry in cells. Read the abstract.

Development of a gluten-free rice noodle by utilizing protein-polyphenol interaction between soy protein isolate and extract of Acanthopanax sessiliflorus.

PubMed

Lee, Da-Som; Kim, Yang; Song, Youngwoon; Lee, Ji-Hye; Lee, Suyong; Yoo, Sang-Ho

2016-02-01

The potential of the protein-polyphenol interaction was applied to crosslinking reinforced protein networks in gluten-free rice noodles. Specifically, inter-component interaction between soy protein isolate and extract of Acanthopanax sessiliflorus fruit (ogaja) was examined with a view to improving its quality. In a components-interacting model system, a mixture of soy protein isolate (SPI) and ogaja extract (OE) induced a drastic increase in absorbance at 660 nm by haze formation, while the major anthocyanin of ogaja, cyanidin-3-O-sambubioside, sparsely interacted with SPI or gelatin. Individual or combined treatment of SPI and OE on rice dough decreased all the viscosity parameters in rapid visco analysis. However, SPI-OE treatment significantly increased all the texture parameters of rice dough derived from Mixolab(®) analysis (P < 0.05). Incorporation of SPI in rice dough significantly reduced endothermic ΔH, and SPI-OE treatment further decreased this value. SPI-OE interaction significantly increased the tensile properties of cooked noodle and decreased 53.7% of cooking loss compared to the untreated rice noodle. SPI-OE treatment caused a considerable reinforcement of the network as shown by reducing cooking loss and suggested the potential for utilizing protein-polyphenol interaction for gluten-free rice noodle production. © 2015 Society of Chemical Industry.

Applications of hydrophilic interaction chromatography to amino acids, peptides, and proteins.

PubMed

Periat, Aurélie; Krull, Ira S; Guillarme, Davy

2015-02-01

This review summarizes the recent advances in the analysis of amino acids, peptides, and proteins using hydrophilic interaction chromatography. Various reports demonstrate the successful analysis of amino acids under such conditions. However, a baseline resolution of the 20 natural amino acids has not yet been published and for this reason, there is often a need to use mass spectrometry for detection to further improve selectivity. Hydrophilic interaction chromatography is also recognized as a powerful technique for peptide analysis, and there are a lot of papers showing its applicability for proteomic applications (peptide mapping). It is expected that its use for peptide mapping will continue to grow in the future, particularly because this analytical strategy can be combined with reversed-phase liquid chromatography, in a two-dimensional setup, to reach very high resolving power. Finally, the interest in hydrophilic interaction chromatography for intact proteins analysis is less evident due to possible solubility issues and a lack of suitable hydrophilic interaction chromatography stationary phases. To date, it has been successfully employed only for the characterization of membrane proteins, histones, and the separation of glycosylated isoforms of an intact glycoprotein. From our point of view, the number of hydrophilic interaction chromatography columns compatible with intact proteins (higher upper temperature limit, large pore size, etc.) is still too limited. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sequence co-evolution gives 3D contacts and structures of protein complexes

PubMed Central

Hopf, Thomas A; Schärfe, Charlotta P I; Rodrigues, João P G L M; Green, Anna G; Kohlbacher, Oliver; Sander, Chris; Bonvin, Alexandre M J J; Marks, Debora S

2014-01-01

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.001 PMID:25255213

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.

A Viral-Human Interactome Based on Structural Motif-Domain Interactions Captures the Human Infectome

PubMed Central

Guo, Xianwu; Rodríguez-Pérez, Mario A.

2013-01-01

Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets. PMID:23951184

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 to analyze population-averaged NMR quantities. Essentially, to apply NMR successfully, both the type of experiment and equation to fit the data must be carefully and specifically chosen for the protein-ligand interaction under analysis. In this review, we first explain the exchange regimes and kinetic models of protein-ligand interactions, and then describe the NMR methods that quantitatively analyze these specific interactions. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity Mechanisms

PubMed Central

Memišević, Vesna; Zavaljevski, Nela; Rajagopala, Seesandra V.; Kwon, Keehwan; Pieper, Rembert; DeShazer, David; Reifman, Jaques; Wallqvist, Anders

2015-01-01

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread. PMID:25738731

Mining host-pathogen protein interactions to characterize Burkholderia mallei infectivity mechanisms.

PubMed

Memišević, Vesna; Zavaljevski, Nela; Rajagopala, Seesandra V; Kwon, Keehwan; Pieper, Rembert; DeShazer, David; Reifman, Jaques; Wallqvist, Anders

2015-03-01

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.

PubMed

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database in which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. This database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.

Large-Scale Interaction Profiling of Protein Domains Through Proteomic Peptide-Phage Display Using Custom Peptidomes.

PubMed

Seo, Moon-Hyeong; Nim, Satra; Jeon, Jouhyun; Kim, Philip M

2017-01-01

Protein-protein interactions are essential to cellular functions and signaling pathways. We recently combined bioinformatics and custom oligonucleotide arrays to construct custom-made peptide-phage libraries for screening peptide-protein interactions, an approach we call proteomic peptide-phage display (ProP-PD). In this chapter, we describe protocols for phage display for the identification of natural peptide binders for a given protein. We finally describe deep sequencing for the analysis of the proteomic peptide-phage display.

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

Analysis of Structural Features Contributing to Weak Affinities of Ubiquitin/Protein Interactions.

PubMed

Cohen, Ariel; Rosenthal, Eran; Shifman, Julia M

2017-11-10

Ubiquitin is a small protein that enables one of the most common post-translational modifications, where the whole ubiquitin molecule is attached to various target proteins, forming mono- or polyubiquitin conjugations. As a prototypical multispecific protein, ubiquitin interacts non-covalently with a variety of proteins in the cell, including ubiquitin-modifying enzymes and ubiquitin receptors that recognize signals from ubiquitin-conjugated substrates. To enable recognition of multiple targets and to support fast dissociation from the ubiquitin modifying enzymes, ubiquitin/protein interactions are characterized with low affinities, frequently in the higher μM and lower mM range. To determine how structure encodes low binding affinity of ubiquitin/protein complexes, we analyzed structures of more than a hundred such complexes compiled in the Ubiquitin Structural Relational Database. We calculated various structure-based features of ubiquitin/protein binding interfaces and compared them to the same features of general protein-protein interactions (PPIs) with various functions and generally higher affinities. Our analysis shows that ubiquitin/protein binding interfaces on average do not differ in size and shape complementarity from interfaces of higher-affinity PPIs. However, they contain fewer favorable hydrogen bonds and more unfavorable hydrophobic/charge interactions. We further analyzed how binding interfaces change upon affinity maturation of ubiquitin toward its target proteins. We demonstrate that while different features are improved in different experiments, the majority of the evolved complexes exhibit better shape complementarity and hydrogen bond pattern compared to wild-type complexes. Our analysis helps to understand how low-affinity PPIs have evolved and how they could be converted into high-affinity PPIs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta.

PubMed

Combs, Steven A; Mueller, Benjamin K; Meiler, Jens

2018-05-29

Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.

Role of DISC1 interacting proteins in schizophrenia risk from genome-wide analysis of missense SNPs.

PubMed

Costas, Javier; Suárez-Rama, Jose Javier; Carrera, Noa; Paz, Eduardo; Páramo, Mario; Agra, Santiago; Brenlla, Julio; Ramos-Ríos, Ramón; Arrojo, Manuel

2013-11-01

A balanced translocation affecting DISC1 cosegregates with several psychiatric disorders, including schizophrenia, in a Scottish family. DISC1 is a hub protein of a network of protein-protein interactions involved in multiple developmental pathways within the brain. Gene set-based analysis has been proposed as an alternative to individual analysis of single nucleotide polymorphisms (SNPs) to get information from genome-wide association studies. In this work, we tested for an overrepresentation of the DISC1 interacting proteins within the top results of our ranked list of genes based on our previous genome-wide association study of missense SNPs in schizophrenia. Our data set consisted of 5100 common missense SNPs genotyped in 476 schizophrenic patients and 447 control subjects from Galicia, NW Spain. We used a modification of the Gene Set Enrichment Analysis adapted for SNPs, as implemented in the GenGen software. The analysis detected an overrepresentation of the DISC1 interacting proteins (permuted P-value=0.0158), indicative of the role of this gene set in schizophrenia risk. We identified seven leading-edge genes, MACF1, UTRN, DST, DISC1, KIF3A, SYNE1, and AKAP9, responsible for the overrepresentation. These genes are involved in neuronal cytoskeleton organization and intracellular transport through the microtubule cytoskeleton, suggesting that these processes may be impaired in schizophrenia. © 2013 John Wiley & Sons Ltd/University College London.

Detection of Protein Interactions in T3S Systems Using Yeast Two-Hybrid Analysis.

PubMed

Nilles, Matthew L

2017-01-01

Two-hybrid systems, sometimes termed interaction traps, are genetic systems designed to find and analyze interactions between proteins. The most common systems are yeast based (commonly Saccharomyces cerevisae) and rely on the functional reconstitution of the GAL4 transcriptional activator. Reporter genes, such as the lacZ gene of Escherichia coli (encodes β-galactosidase), are placed under GAL4-dependent transcriptional control to provide quick and reliable detection of protein interactions. In this method the use of a yeast-based two-hybrid system is described to study protein interactions between components of type III secretion systems.

Re-analysis of protein data reveals the germination pathway and up accumulation mechanism of cell wall hydrolases during the radicle protrusion step of seed germination in Podophyllum hexandrum- a high altitude plant

PubMed Central

Dogra, Vivek; Bagler, Ganesh; Sreenivasulu, Yelam

2015-01-01

Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein–protein interaction network (AtPIN). The developed PGN is comprised of a giant cluster with 1028 proteins having 10,519 interactions and a few small clusters with relevant gene ontological signatures. In this analysis, a germination pathway related cluster which is also central to the topology and information dynamics of PGN was obtained with a set of 60 key proteins. Among these, eight proteins which are known to be involved in signaling, metabolism, protein modification, cell wall modification, and cell cycle regulation processes were found commonly highlighted in both the proteomic and interactome analysis. The systems-level analysis of PGN identified the key proteins involved in radicle protrusion step of seed germination in Podophyllum. PMID:26579141

Stacking interactions in PUF-RNA complexes

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

Yiling Koh, Yvonne; Wang, Yeming; Qiu, Chen

2012-07-02

Stacking interactions between amino acids and bases are common in RNA-protein interactions. Many proteins that regulate mRNAs interact with single-stranded RNA elements in the 3' UTR (3'-untranslated region) of their targets. PUF proteins are exemplary. Here we focus on complexes formed between a Caenorhabditis elegans PUF protein, FBF, and its cognate RNAs. Stacking interactions are particularly prominent and involve every RNA base in the recognition element. To assess the contribution of stacking interactions to formation of the RNA-protein complex, we combine in vivo selection experiments with site-directed mutagenesis, biochemistry, and structural analysis. Our results reveal that the identities of stackingmore » amino acids in FBF affect both the affinity and specificity of the RNA-protein interaction. Substitutions in amino acid side chains can restrict or broaden RNA specificity. We conclude that the identities of stacking residues are important in achieving the natural specificities of PUF proteins. Similarly, in PUF proteins engineered to bind new RNA sequences, the identity of stacking residues may contribute to 'target' versus 'off-target' interactions, and thus be an important consideration in the design of proteins with new specificities.« less

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

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.

Intuitive Density Functional Theory-Based Energy Decomposition Analysis for Protein-Ligand Interactions.

PubMed

Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

2017-04-11

First-principles quantum mechanical calculations with methods such as density functional theory (DFT) allow the accurate calculation of interaction energies between molecules. These interaction energies can be dissected into chemically relevant components such as electrostatics, polarization, and charge transfer using energy decomposition analysis (EDA) approaches. Typically EDA has been used to study interactions between small molecules; however, it has great potential to be applied to large biomolecular assemblies such as protein-protein and protein-ligand interactions. We present an application of EDA calculations to the study of ligands that bind to the thrombin protein, using the ONETEP program for linear-scaling DFT calculations. Our approach goes beyond simply providing the components of the interaction energy; we are also able to provide visual representations of the changes in density that happen as a result of polarization and charge transfer, thus pinpointing the functional groups between the ligand and protein that participate in each kind of interaction. We also demonstrate with this approach that we can focus on studying parts (fragments) of ligands. The method is relatively insensitive to the protocol that is used to prepare the structures, and the results obtained are therefore robust. This is an application to a real protein drug target of a whole new capability where accurate DFT calculations can produce both energetic and visual descriptors of interactions. These descriptors can be used to provide insights for tailoring interactions, as needed for example in drug design.

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.

Exploring pathway interactions in insulin resistant mouse liver

PubMed Central

2011-01-01

Background Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset. Results We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar. Conclusions Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well. PMID:21843341

An "on-matrix" digestion procedure for AP-MS experiments dissects the interplay between complex-conserved and serotype-specific reactivities in Dengue virus-human plasma interactome.

PubMed

Ramos, Yassel; Huerta, Vivian; Martín, Dayron; Palomares, Sucel; Yero, Alexis; Pupo, Dianne; Gallien, Sebastien; Martín, Alejandro M; Pérez-Riverol, Yasset; Sarría, Mónica; Guirola, Osmany; Chinea, Glay; Domon, Bruno; González, Luis Javier

2017-07-13

The interactions between the four Dengue virus (DENV) serotypes and plasma proteins are crucial in the initial steps of viral infection to humans. Affinity purification combined with quantitative mass spectrometry analysis, has become one of the most powerful tools for the investigation on novel protein-protein interactions. Using this approach, we report here that a significant number of bait-interacting proteins do not dissociate under standard elution conditions, i.e. acid pH and chaotropic agents, and that this problem can be circumvented by using the "on-matrix" digestion procedure described here. This procedure enabled the identification of 16 human plasma proteins interacting with domain III from the envelope protein of DENV serotypes 1, 3 and 4 that would have not been detected otherwise and increased the known DIIIE interactors in human plasma to 59 proteins. Selected Reaction Monitoring analysis evidenced DENV interactome in human plasma is rather conserved although significant differences on the reactivity of viral serotypes with specific proteins do exist. A comparison between the serotype-dependent profile of reactivity and the conservation pattern of amino acid residues suggests an evolutionary selection of highly conserved interactions with the host and other interactions mediated for surface regions of higher variability. False negative results on the identification of interacting proteins in pull-down experiments compromise the subsequent interpretation of results and the formulation of a working hypothesis for the derived future work. In this study we demonstrate the presence of bait-interacting proteins reluctant to dissociate under elution conditions of acid pH and presence of chaotropics. We propose the direct proteolytic digestion of proteins while still bound to the affinity matrix ("on-matrix" digestion) and evaluate the impact of this methodology in the comparative study of the interactome of the four serotypes of Dengue virus mediated by the domain III of the viral envelope glycoprotein. Fifty nine proteins were identified as putative interaction partners of Dengue virus (IPs) either due to direct binding or by co-isolation with interacting proteins. Collectively the IPs identified from the pull-down with the recombinant domain III proteins representing the four viral serotypes, 29% were identified only after "on-matrix" digestion which demonstrate the usefulness of this method of recovering bait-bound proteins. Results highlight a particular importance of "on-matrix" digestion procedure for comparative studies where a stronger interaction with one of the interest baits could prevent a bound protein to elute under standard conditions thus leading to misinterpretation as absent in the interactome of this particular bait. The analysis of the Interaction Network indicates that Dengue virus interactome mediated by the domain III of the envelope protein is rather conserved in the viral complex suggesting a key role of these interactions for viral infection thus making candidates to explore for potential biomarkers of clinical outcome in DENV-caused disease. Interestingly, some particular IPs exhibit significant differences in the strength of the interaction with the viral serotypes representing interactions that involve more variable regions in the surface of the domain III. Since such variable regions are the consequence of the interaction with antibodies generated by human immune response; this result relates the interaction with proteins from human plasma with the interplay of the virus and the human immune system. Copyright © 2017 Elsevier B.V. All rights reserved.

Comprehensive Characterization of Minichromosome Maintenance Complex (MCM) Protein Interactions Using Affinity and Proximity Purifications Coupled to Mass Spectrometry.

PubMed

Dubois, Marie-Line; Bastin, Charlotte; Lévesque, Dominique; Boisvert, François-Michel

2016-09-02

The extensive identification of protein-protein interactions under different conditions is an important challenge to understand the cellular functions of proteins. Here we use and compare different approaches including affinity purification and purification by proximity coupled to mass spectrometry to identify protein complexes. We explore the complete interactome of the minichromosome maintenance (MCM) complex by using both approaches for all of the different MCM proteins. Overall, our analysis identified unique and shared interaction partners and proteins enriched for distinct biological processes including DNA replication, DNA repair, and cell cycle regulation. Furthermore, we mapped the changes in protein interactions of the MCM complex in response to DNA damage, identifying a new role for this complex in DNA repair. In summary, we demonstrate the complementarity of these approaches for the characterization of protein interactions within the MCM complex.

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

Identification and analysis of host proteins that interact with the 3'-untranslated region of tick-borne encephalitis virus genomic RNA.

PubMed

Muto, Memi; Kamitani, Wataru; Sakai, Mizuki; Hirano, Minato; Kobayashi, Shintaro; Kariwa, Hiroaki; Yoshii, Kentaro

2018-04-02

Tick-borne encephalitis virus (TBEV) causes severe neurological disease, but the pathogenetic mechanism is unclear. The conformational structure of the 3'-untranslated region (UTR) of TBEV is associated with its virulence. We tried to identify host proteins interacting with the 3'-UTR of TBEV. Cellular proteins of HEK293T cells were co-precipitated with biotinylated RNAs of the 3'-UTR of low- and high-virulence TBEV strains and subjected to mass spectrometry analysis. Fifteen host proteins were found to bind to the 3'-UTR of TBEV, four of which-cold shock domain containing-E1 (CSDE1), spermatid perinuclear RNA binding protein (STRBP), fragile X mental retardation protein (FMRP), and interleukin enhancer binding factor 3 (ILF3)-bound specifically to that of the low-virulence strain. An RNA immunoprecipitation and pull-down assay confirmed the interactions of the complete 3'-UTRs of TBEV genomic RNA with CSDE1, FMRP, and ILF3. Partial deletion of the stem loop (SL) 3 to SL 5 structure of the variable region of the 3'-UTR did not affect interactions with the host proteins, but the interactions were markedly suppressed by deletion of the complete SL 3, 4, and 5 structures, as in the high-virulence TBEV strain. Further analysis of the roles of host proteins in the neurologic pathogenicity of TBEV is warranted. Copyright © 2018 Elsevier B.V. All rights reserved.

Proteome-wide characterization of the RNA-binding protein RALY-interactome using the in vivo-biotinylation-pulldown-quant (iBioPQ) approach.

PubMed

Tenzer, Stefan; Moro, Albertomaria; Kuharev, Jörg; Francis, Ashwanth Christopher; Vidalino, Laura; Provenzani, Alessandro; Macchi, Paolo

2013-06-07

RALY is a member of the heterogeneous nuclear ribonucleoproteins, a family of RNA-binding proteins generally involved in many processes of mRNA metabolism. No quantitative proteomic analysis of RALY-containing ribonucleoparticles (RNPs) has been performed so far, and the biological role of RALY remains elusive. Here, we present a workflow for the characterization of RALY's interaction partners, termed iBioPQ, that involves in vivo biotinylation of biotin acceptor peptide (BAP)-fused protein in the presence of the prokaryotic biotin holoenzyme synthetase of BirA so that it can be purified using streptavidin-coated magnetic beads, circumventing the need for specific antibodies and providing efficient pulldowns. Protein eluates were subjected to tryptic digestion and identified using data-independent acquisition on an ion-mobility enabled high-resolution nanoUPLC-QTOF system. Using label-free quantification, we identified 143 proteins displaying at least 2-fold difference in pulldown compared to controls. Gene Ontology overrepresentation analysis revealed an enrichment of proteins involved in mRNA metabolism and translational control. Among the most abundant interacting proteins, we confirmed RNA-dependent interactions of RALY with MATR3, PABP1 and ELAVL1. Comparative analysis of pulldowns after RNase treatment revealed a protein-protein interaction of RALY with eIF4AIII, FMRP, and hnRNP-C. Our data show that RALY-containing RNPs are much more heterogeneous than previously hypothesized.

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

Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae.

PubMed

Uthe, Henriette; Vanselow, Jens T; Schlosser, Andreas

2017-02-27

Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15 N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis.

Clinicopathologic significance of minichromosome maintenance protein 2 and Tat-interacting protein 30 expression in benign and malignant lesions of the gallbladder.

PubMed

Liu, Dong-cai; Yang, Zhu-lin

2011-11-01

Gallbladder cancers are aggressive tumors with a poor prognosis and high mortality rate. To find specific biological markers for early diagnosis and prognosis and to develop possible alternative treatment strategies, we examined minichromosome maintenance protein 2 (MCM2) and Tat-interacting protein 30 (TIP30) expression in 108 gallbladder adenocarcinomas, 15 gallbladder polyps, 35 chronic cholecystitis tissues, and 46 peritumoral tissues using immunohistochemistry. Expression of MCM2 was significantly higher in adenocarcinomas than in peritumoral tissues (χ² = 8.41; P < .01), adenomatous polyps (χ² = 6.81; P < .01), and chronic cholecystitis (χ² = 21.00; P < .01). In contrast, Tat-interacting protein 30 expression was significantly less in adenocarcinomas than in peritumoral tissues (χ² = 13.26; P < .01), adenomatous polyps (χ² = 4.76; P < .05), and chronic cholecystitis (χ² = 18.93; P < .01). The benign lesions in gallbladder epithelium with positive MCM2 or negative Tat-interacting protein 30 expression showed moderate to severe atypical hyperplasia. Expression of MCM2 and absence of Tat-interacting protein 30 were significantly associated with poor differentiation, large tumor mass, lymph node metastasis, and invasion of adenocarcinoma. Univariate Kaplan-Meier analysis showed that either elevated MCM2 (P = .006) or lowered Tat-interacting protein 30 (P = .006) expression was closely associated with shorter overall survival. Multivariate Cox regression analysis revealed that expression of MCM2 (P = .007) or nonexpression of Tat-interacting protein 30 (P = .009) was an independent predictor of a poor prognosis in adenocarcinoma. Our results suggest that overexpression of MCM2 or loss of expression of Tat-interacting protein 30 is closely related to carcinogenesis, progression, biological behavior, and prognosis of gallbladder adenocarcinoma. Copyright © 2011 Elsevier Inc. All rights reserved.

Quantifying why urea is a protein denaturant, whereas glycine betaine is a protein stabilizer

PubMed Central

Guinn, Emily J.; Pegram, Laurel M.; Capp, Michael W.; Pollock, Michelle N.; Record, M. Thomas

2011-01-01

To explain the large, opposite effects of urea and glycine betaine (GB) on stability of folded proteins and protein complexes, we quantify and interpret preferential interactions of urea with 45 model compounds displaying protein functional groups and compare with a previous analysis of GB. This information is needed to use urea as a probe of coupled folding in protein processes and to tune molecular dynamics force fields. Preferential interactions between urea and model compounds relative to their interactions with water are determined by osmometry or solubility and dissected using a unique coarse-grained analysis to obtain interaction potentials quantifying the interaction of urea with each significant type of protein surface (aliphatic, aromatic hydrocarbon (C); polar and charged N and O). Microscopic local-bulk partition coefficients Kp for the accumulation or exclusion of urea in the water of hydration of these surfaces relative to bulk water are obtained. Kp values reveal that urea accumulates moderately at amide O and weakly at aliphatic C, whereas GB is excluded from both. These results provide both thermodynamic and molecular explanations for the opposite effects of urea and glycine betaine on protein stability, as well as deductions about strengths of amide NH—amide O and amide NH—amide N hydrogen bonds relative to hydrogen bonds to water. Interestingly, urea, like GB, is moderately accumulated at aromatic C surface. Urea m-values for protein folding and other protein processes are quantitatively interpreted and predicted using these urea interaction potentials or Kp values. PMID:21930943

Quantifying why urea is a protein denaturant, whereas glycine betaine is a protein stabilizer.

PubMed

Guinn, Emily J; Pegram, Laurel M; Capp, Michael W; Pollock, Michelle N; Record, M Thomas

2011-10-11

To explain the large, opposite effects of urea and glycine betaine (GB) on stability of folded proteins and protein complexes, we quantify and interpret preferential interactions of urea with 45 model compounds displaying protein functional groups and compare with a previous analysis of GB. This information is needed to use urea as a probe of coupled folding in protein processes and to tune molecular dynamics force fields. Preferential interactions between urea and model compounds relative to their interactions with water are determined by osmometry or solubility and dissected using a unique coarse-grained analysis to obtain interaction potentials quantifying the interaction of urea with each significant type of protein surface (aliphatic, aromatic hydrocarbon (C); polar and charged N and O). Microscopic local-bulk partition coefficients K(p) for the accumulation or exclusion of urea in the water of hydration of these surfaces relative to bulk water are obtained. K(p) values reveal that urea accumulates moderately at amide O and weakly at aliphatic C, whereas GB is excluded from both. These results provide both thermodynamic and molecular explanations for the opposite effects of urea and glycine betaine on protein stability, as well as deductions about strengths of amide NH--amide O and amide NH--amide N hydrogen bonds relative to hydrogen bonds to water. Interestingly, urea, like GB, is moderately accumulated at aromatic C surface. Urea m-values for protein folding and other protein processes are quantitatively interpreted and predicted using these urea interaction potentials or K(p) values.

Large-scale De Novo Prediction of Physical Protein-Protein Association*

PubMed Central

Elefsinioti, Antigoni; Saraç, Ömer Sinan; Hegele, Anna; Plake, Conrad; Hubner, Nina C.; Poser, Ina; Sarov, Mihail; Hyman, Anthony; Mann, Matthias; Schroeder, Michael; Stelzl, Ulrich; Beyer, Andreas

2011-01-01

Information about the physical association of proteins is extensively used for studying cellular processes and disease mechanisms. However, complete experimental mapping of the human interactome will remain prohibitively difficult in the near future. Here we present a map of predicted human protein interactions that distinguishes functional association from physical binding. Our network classifies more than 5 million protein pairs predicting 94,009 new interactions with high confidence. We experimentally tested a subset of these predictions using yeast two-hybrid analysis and affinity purification followed by quantitative mass spectrometry. Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network. These independent experiments address potential issues of circular reasoning and are a distinctive feature of this work. Analysis of the physical interactome unravels subnetworks mediating between different functional and physical subunits of the cell. Finally, we demonstrate the utility of the network for the analysis of molecular mechanisms of complex diseases by applying it to genome-wide association studies of neurodegenerative diseases. This analysis provides new evidence implying TOMM40 as a factor involved in Alzheimer's disease. The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular. Our interactome is available via the hPRINT web server at: www.print-db.org. PMID:21836163

Structure-wise discrimination of cytosine, thymine, and uracil by proteins in terms of their nonbonded interactions.

PubMed

Usha, S; Selvaraj, S

2014-01-01

The molecular recognition and discrimination of very similar ligand moieties by proteins are important subjects in protein-ligand interaction studies. Specificity in the recognition of molecules is determined by the arrangement of protein and ligand atoms in space. The three pyrimidine bases, viz. cytosine, thymine, and uracil, are structurally similar, but the proteins that bind to them are able to discriminate them and form interactions. Since nonbonded interactions are responsible for molecular recognition processes in biological systems, our work attempts to understand some of the underlying principles of such recognition of pyrimidine molecular structures by proteins. The preferences of the amino acid residues to contact the pyrimidine bases in terms of nonbonded interactions; amino acid residue-ligand atom preferences; main chain and side chain atom contributions of amino acid residues; and solvent-accessible surface area of ligand atoms when forming complexes are analyzed. Our analysis shows that the amino acid residues, tyrosine and phenyl alanine, are highly involved in the pyrimidine interactions. Arginine prefers contacts with the cytosine base. The similarities and differences that exist between the interactions of the amino acid residues with each of the three pyrimidine base atoms in our analysis provide insights that can be exploited in designing specific inhibitors competitive to the ligands.

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

PubMed

Havugimana, Pierre C; Hu, Pingzhao; Emili, Andrew

2017-10-01

Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and for functional annotation of previously uncharacterized proteins (via guilt-by-association or -correlation). After a decade in the field, we felt it timely to document our own experiences in the systematic analysis of protein interaction networks. Areas covered: Researchers worldwide have contributed innovative experimental and computational approaches that have driven the rapidly evolving field of 'functional proteomics'. These include mass spectrometry-based methods to characterize macromolecular complexes on a global-scale and sophisticated data analysis tools - most notably machine learning - that allow for the generation of high-quality protein association maps. Expert commentary: Here, we recount some key lessons learned, with an emphasis on successful workflows, and challenges, arising from our own and other groups' ongoing efforts to generate, interpret and report proteome-scale interaction networks in increasingly diverse biological contexts.

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.

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.

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

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.

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.

Label-free, real-time interaction and adsorption analysis 1: surface plasmon resonance.

PubMed

Fee, Conan J

2013-01-01

A key requirement for the development of proteins for use in nanotechnology is an understanding of how individual proteins bind to other molecules as they assemble into larger structures. The introduction of labels to enable the detection of biomolecules brings the inherent risk that the labels themselves will influence the nature of biomolecular interactions. Thus, there is a need for label-free interaction and adsorption analysis. In this and the following chapter, two biosensor techniques are reviewed: surface plasmon resonance (SPR) and the quartz crystal microbalance (QCM). Both allow real-time analysis of biomolecular interactions and both are label-free. The first of these, SPR, is an optical technique that is highly sensitive to the changes in refractive index that occur with protein (or other molecule) accumulation near an illuminated gold surface. Unlike QCM ( Chapter 18 ) SPR is not affected by the water that may be associated with the adsorbed layer nor by conformational changes in the adsorbed species. SPR thus provides unique information about the interaction of a protein with its binding partners.

Study and selection of in vivo protein interactions by coupling bimolecular fluorescence complementation and flow cytometry.

PubMed

Morell, Montse; Espargaro, Alba; Aviles, Francesc Xavier; Ventura, Salvador

2008-01-01

We present a high-throughput approach to study weak protein-protein interactions by coupling bimolecular fluorescent complementation (BiFC) to flow cytometry (FC). In BiFC, the interaction partners (bait and prey) are fused to two rationally designed fragments of a fluorescent protein, which recovers its function upon the binding of the interacting proteins. For weak protein-protein interactions, the detected fluorescence is proportional to the interaction strength, thereby allowing in vivo discrimination between closely related binders with different affinity for the bait protein. FC provides a method for high-speed multiparametric data acquisition and analysis; the assay is simple, thousands of cells can be analyzed in seconds and, if required, selected using fluorescence-activated cell sorting (FACS). The combination of both methods (BiFC-FC) provides a technically straightforward, fast and highly sensitive method to validate weak protein interactions and to screen and identify optimal ligands in biologically synthesized libraries. Once plasmids encoding the protein fusions have been obtained, the evaluation of a specific interaction, the generation of a library and selection of active partners using BiFC-FC can be accomplished in 5 weeks.

Application of the fragment molecular orbital method analysis to fragment-based drug discovery of BET (bromodomain and extra-terminal proteins) inhibitors.

PubMed

Ozawa, Motoyasu; Ozawa, Tomonaga; Ueda, Kazuyoshi

2017-06-01

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε-N-acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein-protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Kinase Substrate Sensor (KISS), a Mammalian In Situ Protein Interaction Sensor*

PubMed Central

Lievens, Sam; Gerlo, Sarah; Lemmens, Irma; De Clercq, Dries J. H.; Risseeuw, Martijn D. P.; Vanderroost, Nele; De Smet, Anne-Sophie; Ruyssinck, Elien; Chevet, Eric; Van Calenbergh, Serge; Tavernier, Jan

2014-01-01

Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent β-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges. PMID:25154561

Conformational dynamics of L-lysine, L-arginine, L-ornithine binding protein reveals ligand-dependent plasticity.

PubMed

Silva, Daniel-Adriano; Domínguez-Ramírez, Lenin; Rojo-Domínguez, Arturo; Sosa-Peinado, Alejandro

2011-07-01

The molecular basis of multiple ligand binding affinity for amino acids in periplasmic binding proteins (PBPs) and in the homologous domain for class C G-protein coupled receptors is an unsolved question. Here, using unrestrained molecular dynamic simulations, we studied the ligand binding mechanism present in the L-lysine, L-arginine, L-ornithine binding protein. We developed an analysis based on dihedral angles for the description of the conformational changes upon ligand binding. This analysis has an excellent correlation with each of the two main movements described by principal component analysis (PCA) and it's more convenient than RMSD measurements to describe the differences in the conformational ensembles observed. Furthermore, an analysis of hydrogen bonds showed specific interactions for each ligand studied as well as the ligand interaction with the aromatic residues Tyr-14 and Phe-52. Using uncharged histidine tautomers, these interactions are not observed. On the basis of these results, we propose a model in which hydrogen bond interactions place the ligand in the correct orientation to induce a cation-π interaction with Tyr-14 and Phe-52 thereby stabilizing the closed state. Our results also show that this protein adopts slightly different closed conformations to make available specific hydrogen bond interactions for each ligand thus, allowing a single mechanism to attain multiple ligand specificity. These results shed light on the experimental evidence for ligand-dependent conformational plasticity not explained by the previous crystallographic data. Copyright © 2011 Wiley-Liss, Inc.

Comparative interactomics: analysis of arabidopsis 14-3-3 complexes reveals highly conserved 14-3-3 interactions between humans and plants.

PubMed

Paul, Anna-Lisa; Liu, Li; McClung, Scott; Laughner, Beth; Chen, Sixue; Ferl, Robert J

2009-04-01

As a first step in the broad characterization of plant 14-3-3 multiprotein complexes in vivo, stringent and specific antibody affinity purification was used to capture 14-3-3s together with their interacting proteins from extracts of Arabidopsis cell suspension cultures. Approximately 120 proteins were identified as potential in vivo 14-3-3 interacting proteins by mass spectrometry of the recovered complexes. Comparison of the proteins in this data set with the 14-3-3 interacting proteins from a similar study in human embryonic kidney cell cultures revealed eight interacting proteins that likely represent reasonably abundant, fundamental 14-3-3 interaction complexes that are highly conserved across all eukaryotes. The Arabidopsis 14-3-3 interaction data set was also compared to a yeast in vivo 14-3-3 interaction data set. Four 14-3-3 interacting proteins are conserved in yeast, humans, and Arabidopsis. Comparisons of the data sets based on biochemical function revealed many additional similarities in the human and Arabidopsis data sets that represent conserved functional interactions, while also leaving many proteins uniquely identified in either Arabidopsis or human cells. In particular, the Arabidopsis interaction data set is enriched for proteins involved in metabolism.

Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy

PubMed Central

Greco, Todd M.; Guise, Amanda J.; Cristea, Ileana M.

2016-01-01

In biological systems, proteins catalyze the fundamental reactions that underlie all cellular functions, including metabolic processes and cell survival and death pathways. These biochemical reactions are rarely accomplished alone. Rather, they involve a concerted effect from many proteins that may operate in a directed signaling pathway and/or may physically associate in a complex to achieve a specific enzymatic activity. Therefore, defining the composition and regulation of protein complexes is critical for understanding cellular functions. In this chapter, we describe an approach that uses quantitative mass spectrometry (MS) to assess the specificity and the relative stability of protein interactions. Isolation of protein complexes from mammalian cells is performed by rapid immunoaffinity purification, and followed by in-solution digestion and high-resolution mass spectrometry analysis. We employ complementary quantitative MS workflows to assess the specificity of protein interactions using label-free MS and statistical analysis, and the relative stability of the interactions using a metabolic labeling technique. For each candidate protein interaction, scores from the two workflows can be correlated to minimize nonspecific background and profile protein complex composition and relative stability. PMID:26867737

Analysis of A549 cell proteome alteration in response to recombinant influenza A virus nucleoprotein and its interaction with cellular proteins, a preliminary study.

PubMed

Kumar, D; Tiwari, K; Rajala, M S

Influenza A virus undergoes frequent changes of antigenicity and contributes to seasonal epidemics or unpredictable pandemics. Nucleoprotein, encoded by gene segment 5, is an internal protein of the virus and is conserved among strains of different host origins. In the current study, we analyzed the differentially expressed proteins in A549 cells transiently transfected with the recombinant nucleoprotein of influenza A virus by 2D gel electrophoresis. The resolved protein spots on gel were identified by MALDI-TOF/Mass spectrometry analysis. The majority of the host proteins detected to be differentially abundant in recombinant nucleoprotein-expressing cells as compared to vector-transfected cells are the proteins of metabolic pathways, glycolytic enzymes, molecular chaperones and cytoskeletal proteins. We further demonstrated the interaction of virus nucleoprotein with some of the identified host cellular proteins. In vitro binding assay carried out using the purified recombinant nucleoprotein (pET29a+NP-His) and A549 cell lysate confirmed the interaction between nucleoprotein and host proteins, such as alpha enolase 1, pyruvate kinase and β-actin. The preliminary data of our study provides the information on virus nucleoprotein interaction with proteins involved in glycolysis. However, studies are ongoing to understand the significance of these interactions in modulating the host factors during virus replication.

Target identification in Fusobacterium nucleatum by subtractive genomics approach and enrichment analysis of host-pathogen protein-protein interactions.

PubMed

Kumar, Amit; Thotakura, Pragna Lakshmi; Tiwary, Basant Kumar; Krishna, Ramadas

2016-05-12

Fusobacterium nucleatum, a well studied bacterium in periodontal diseases, appendicitis, gingivitis, osteomyelitis and pregnancy complications has recently gained attention due to its association with colorectal cancer (CRC) progression. Treatment with berberine was shown to reverse F. nucleatum-induced CRC progression in mice by balancing the growth of opportunistic pathogens in tumor microenvironment. Intestinal microbiota imbalance and the infections caused by F. nucleatum might be regulated by therapeutic intervention. Hence, we aimed to predict drug target proteins in F. nucleatum, through subtractive genomics approach and host-pathogen protein-protein interactions (HP-PPIs). We also carried out enrichment analysis of host interacting partners to hypothesize the possible mechanisms involved in CRC progression due to F. nucleatum. In subtractive genomics approach, the essential, virulence and resistance related proteins were retrieved from RefSeq proteome of F. nucleatum by searching against Database of Essential Genes (DEG), Virulence Factor Database (VFDB) and Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT) tool respectively. A subsequent hierarchical screening to identify non-human homologous, metabolic pathway-independent/pathway-specific and druggable proteins resulted in eight pathway-independent and 27 pathway-specific druggable targets. Co-aggregation of F. nucleatum with host induces proinflammatory gene expression thereby potentiates tumorigenesis. Hence, proteins from IBDsite, a database for inflammatory bowel disease (IBD) research and those involved in colorectal adenocarcinoma as interpreted from The Cancer Genome Atlas (TCGA) were retrieved to predict drug targets based on HP-PPIs with F. nucleatum proteome. Prediction of HP-PPIs exhibited 186 interactions contributed by 103 host and 76 bacterial proteins. Bacterial interacting partners were accounted as putative targets. And enrichment analysis of host interacting partners showed statistically enriched terms that were in positive correlation with CRC, atherosclerosis, cardiovascular, osteoporosis, Alzheimer's and other diseases. Subtractive genomics analysis provided a set of target proteins suggested to be indispensable for survival and pathogenicity of F. nucleatum. These target proteins might be considered for designing potent inhibitors to abrogate F. nucleatum infections. From enrichment analysis, it was hypothesized that F. nucleatum infection might enhance CRC progression by simultaneously regulating multiple signaling cascades which could lead to up-regulation of proinflammatory responses, oncogenes, modulation of host immune defense mechanism and suppression of DNA repair system.

Evaluation of a Bead-Free Coimmunoprecipitation Technique for Identification of Virus-Host Protein Interactions Using High-Resolution Mass Spectrometry.

PubMed

DeBlasio, Stacy L; Bereman, Michael S; Mahoney, Jaclyn; Thannhauser, Theodore W; Gray, Stewart M; MacCoss, Michael J; Cilia Heck, Michelle

2017-09-01

Protein interactions between virus and host are essential for viral propagation and movement, as viruses lack most of the proteins required to thrive on their own. Precision methods aimed at disrupting virus-host interactions represent new approaches to disease management but require in-depth knowledge of the identity and binding specificity of host proteins within these interaction networks. Protein coimmunoprecipitation (co-IP) coupled with mass spectrometry (MS) provides a high-throughput way to characterize virus-host interactomes in a single experiment. Common co-IP methods use antibodies immobilized on agarose or magnetic beads to isolate virus-host complexes in solutions of host tissue homogenate. Although these workflows are well established, they can be fairly laborious and expensive. Therefore, we evaluated the feasibility of using antibody-coated microtiter plates coupled with MS analysis as an easy, less expensive way to identify host proteins that interact with Potato leafroll virus (PLRV), an insect-borne RNA virus that infects potatoes. With the use of the bead-free platform, we were able to detect 36 plant and 1 nonstructural viral protein significantly coimmunoprecipitating with PLRV. Two of these proteins, a 14-3-3 signal transduction protein and malate dehydrogenase 2 (mMDH2), were detected as having a weakened or lost association with a structural mutant of the virus, demonstrating that the bead-free method is sensitive enough to detect quantitative differences that can be used to pin-point domains of interaction. Collectively, our analysis shows that the bead-free platform is a low-cost alternative that can be used by core facilities and other investigators to identify plant and viral proteins interacting with virions and/or the viral structural proteins.

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

Analysis of solute-protein interactions and solute-solute competition by zonal elution affinity chromatography.

PubMed

Tao, Pingyang; Poddar, Saumen; Sun, Zuchen; Hage, David S; Chen, Jianzhong

2018-02-02

Many biological processes involve solute-protein interactions and solute-solute competition for protein binding. One method that has been developed to examine these interactions is zonal elution affinity chromatography. This review discusses the theory and principles of zonal elution affinity chromatography, along with its general applications. Examples of applications that are examined include the use of this method to estimate the relative extent of solute-protein binding, to examine solute-solute competition and displacement from proteins, and to measure the strength of these interactions. It is also shown how zonal elution affinity chromatography can be used in solvent and temperature studies and to characterize the binding sites for solutes on proteins. In addition, several alternative applications of zonal elution affinity chromatography are discussed, which include the analysis of binding by a solute with a soluble binding agent and studies of allosteric effects. Other recent applications that are considered are the combined use of immunoextraction and zonal elution for drug-protein binding studies, and binding studies that are based on immobilized receptors or small targets. Copyright © 2018 Elsevier Inc. All rights reserved.

Rapid analysis of protein interactions: On-chip micropurification of recombinant protein expressed in Esherichia coli.

PubMed

Natsume, Tohru; Taoka, Masato; Manki, Hiroshi; Kume, Shouen; Isobe, Toshiaki; Mikoshiba, Katsuhiko

2002-09-01

We describe a rapid analysis of interactions between antibodies and a recombinant protein present in total cell lysates. Using a surface plasmon resonance biosensor, a low concentration of glutathione-S-transferase (GST) fused protein expressed in small scale Esherichia coli culture was purified on an anti-GST antibody immobilized sensor chip. The 'on-chip purification' was verified using matrix-assisted laser desorption/ionization-time of flight mass spectrometry by measuring the molecular masses of recombinant proteins purified on the sensor chip. The specific binding of monoclonal antibodies for the on-chip micropurified recombinant proteins can then be monitored, thus enabling kinetic analysis and epitope mapping of the bound antibodies. This approach reduced time, resources and sample consumption by avoiding conventional steps related to concentration and purification.

Structural and energetic study of cation-π-cation interactions in proteins.

PubMed

Pinheiro, Silvana; Soteras, Ignacio; Gelpí, Josep Lluis; Dehez, François; Chipot, Christophe; Luque, F Javier; Curutchet, Carles

2017-04-12

Cation-π interactions of aromatic rings and positively charged groups are among the most important interactions in structural biology. The role and energetic characteristics of these interactions are well established. However, the occurrence of cation-π-cation interactions is an unexpected motif, which raises intriguing questions about its functional role in proteins. We present a statistical analysis of the occurrence, composition and geometrical preferences of cation-π-cation interactions identified in a set of non-redundant protein structures taken from the Protein Data Bank. Our results demonstrate that this structural motif is observed at a small, albeit non-negligible frequency in proteins, and suggest a preference to establish cation-π-cation motifs with Trp, followed by Tyr and Phe. Furthermore, we have found that cation-π-cation interactions tend to be highly conserved, which supports their structural or functional role. Finally, we have performed an energetic analysis of a representative subset of cation-π-cation complexes combining quantum-chemical and continuum solvation calculations. Our results point out that the protein environment can strongly screen the cation-cation repulsion, leading to an attractive interaction in 64% of the complexes analyzed. Together with the high degree of conservation observed, these results suggest a potential stabilizing role in the protein fold, as demonstrated recently for a miniature protein (Craven et al., J. Am. Chem. Soc. 2016, 138, 1543). From a computational point of view, the significant contribution of non-additive three-body terms challenges the suitability of standard additive force fields for describing cation-π-cation motifs in molecular simulations.

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

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.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

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

2011-01-01

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

Molecular dynamics simulations of β2-microglobulin interaction with hydrophobic surfaces.

PubMed

Dongmo Foumthuim, Cedrix J; Corazza, Alessandra; Esposito, Gennaro; Fogolari, Federico

2017-11-21

Hydrophobic surfaces are known to adsorb and unfold proteins, a process that has been studied only for a few proteins. Here we address the interaction of β2-microglobulin, a paradigmatic protein for the study of amyloidogenesis, with hydrophobic surfaces. A system with 27 copies of the protein surrounded by a model cubic hydrophobic box is studied by implicit solvent molecular dynamics simulations. Most proteins adsorb on the walls of the box without major distortions in local geometry, whereas free molecules maintain proper structures and fluctuations as observed in explicit solvent molecular dynamics simulations. The major conclusions from the simulations are as follows: (i) the adopted implicit solvent model is adequate to describe protein dynamics and thermodynamics; (ii) adsorption occurs readily and is irreversible on the simulated timescale; (iii) the regions most involved in molecular encounters and stable interactions with the walls are the same as those that are important in protein-protein and protein-nanoparticle interactions; (iv) unfolding following adsorption occurs at regions found to be flexible by both experiments and simulations; (v) thermodynamic analysis suggests a very large contribution from van der Waals interactions, whereas unfavorable electrostatic interactions are not found to contribute much to adsorption energy. Surfaces with different degrees of hydrophobicity may occur in vivo. Our simulations show that adsorption is a fast and irreversible process which is accompanied by partial unfolding. The results and the thermodynamic analysis presented here are consistent with and rationalize previous experimental work.

Pressor mechanism evaluation for phytochemical compounds using in silico compound-protein interaction prediction.

PubMed

He, Min; Cao, Dong-Sheng; Liang, Yi-Zeng; Li, Ya-Ping; Liu, Ping-Le; Xu, Qing-Song; Huang, Ren-Bin

2013-10-01

In this study, a method was applied to evaluate pressor mechanisms through compound-protein interactions. Our method assumed that the compounds with different pressor mechanisms should bind to different target proteins, and thereby these mechanisms could be differentiated using compound-protein interactions. Twenty-six phytochemical components and 46 tested target proteins related to blood pressure (BP) elevation were collected. Then, in silico compound-protein interactions prediction probabilities were calculated using a random forest model, which have been implemented in a web server, and the credibility was judged using related literature and other methods. Further, a heat map was constructed, it clearly showed different prediction probabilities accompanied with hierarchical clustering analysis results. Followed by a compound-protein interaction network was depicted according to the results, we can see the connectivity layout of phytochemical components with different target proteins within the BP elevation network, which guided the hypothesis generation of poly-pharmacology. Lastly, principal components analysis (PCA) was carried out upon the prediction probabilities, and pressor targets could be divided into three large classes: neurotransmitter receptors, hormones receptors and monoamine oxidases. In addition, steroid glycosides seem to be close to the region of hormone receptors, and a weak difference existed between them. This work explored the possibility for pharmacological or toxicological mechanism classification using compound-protein interactions. Such approaches could also be used to deduce pharmacological or toxicological mechanisms for uncharacterized compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

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.

The interaction between NOLC1 and IAV NS1 protein promotes host cell apoptosis and reduces virus replication.

PubMed

Zhu, Chunyu; Zheng, Fangliang; Zhu, Junfeng; Liu, Meichen; Liu, Na; Li, Xue; Zhang, Li; Deng, Zaidong; Zhao, Qi; Liu, Hongsheng

2017-11-07

NS1 of the influenza virus plays an important role in the infection ability of the influenza virus. Our previous research found that NS1 protein interacts with the NOLC1 protein of host cells, however, the function of the interaction is unknown. In the present study, the role of the interaction between the two proteins in infection was further studied. Several analyses, including the use of a pull-down assay, Co-IP, western blot analysis, overexpression, RNAi, flow cytometry, etc., were used to demonstrate that the NS1 protein of H3N2 influenza virus interacts with host protein NOLC1 and reduces the quantity of NOLC1. The interaction also promotes apoptosis in A549 host cells, while the suppression of NOLC1 protein reduces the proliferation of the H3N2 virus. Based on these data, it was concluded that during the process of infection, NS1 protein interacts with NOLC1 protein, reducing the level of NOLC1, and that the interaction between the two proteins promotes apoptosis of host cells, thus reducing the proliferation of the virus. These findings provide new information on the biological function of the interaction between NS1 and NOLC1.

STATIC AND KINETIC SITE-SPECIFIC PROTEIN-DNA PHOTOCROSSLINKING: ANALYSIS OF BACTERIAL TRANSCRIPTION INITIATION COMPLEXES

PubMed Central

Naryshkin, Nikolai; Druzhinin, Sergei; Revyakin, Andrei; Kim, Younggyu; Mekler, Vladimir; Ebright, Richard H.

2009-01-01

Static site-specific protein-DNA photocrosslinking permits identification of protein-DNA interactions within multiprotein-DNA complexes. Kinetic site-specific protein-DNA photocrosslinking--involving rapid-quench-flow mixing and pulsed-laser irradiation--permits elucidation of pathways and kinetics of formation of protein-DNA interactions within multiprotein-DNA complexes. We present detailed protocols for application of static and kinetic site-specific protein-DNA photocrosslinking to bacterial transcription initiation complexes. PMID:19378179

Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors.

PubMed

Wallqvist, Anders; Memišević, Vesna; Zavaljevski, Nela; Pieper, Rembert; Rajagopala, Seesandra V; Kwon, Keehwan; Yu, Chenggang; Hoover, Timothy A; Reifman, Jaques

2015-12-29

Francisella tularensis is a select bio-threat agent and one of the most virulent intracellular pathogens known, requiring just a few organisms to establish an infection. Although several virulence factors are known, we lack an understanding of virulence factors that act through host-pathogen protein interactions to promote infection. To address these issues in the highly infectious F. tularensis subsp. tularensis Schu S4 strain, we deployed a combined in silico, in vitro, and in vivo analysis to identify virulence factors and their interactions with host proteins to characterize bacterial infection mechanisms. We initially used comparative genomics and literature to identify and select a set of 49 putative and known virulence factors for analysis. Each protein was then subjected to proteome-scale yeast two-hybrid (Y2H) screens with human and murine cDNA libraries to identify potential host-pathogen protein-protein interactions. Based on the bacterial protein interaction profile with both hosts, we selected seven novel putative virulence factors for mutant construction and animal validation experiments. We were able to create five transposon insertion mutants and used them in an intranasal BALB/c mouse challenge model to establish 50 % lethal dose estimates. Three of these, ΔFTT0482c, ΔFTT1538c, and ΔFTT1597, showed attenuation in lethality and can thus be considered novel F. tularensis virulence factors. The analysis of the accompanying Y2H data identified intracellular protein trafficking between the early endosome to the late endosome as an important component in virulence attenuation for these virulence factors. Furthermore, we also used the Y2H data to investigate host protein binding of two known virulence factors, showing that direct protein binding was a component in the modulation of the inflammatory response via activation of mitogen-activated protein kinases and in the oxidative stress response. Direct interactions with specific host proteins and the ability to influence interactions among host proteins are important components for F. tularensis to avoid host-cell defense mechanisms and successfully establish an infection. Although direct host-pathogen protein-protein binding is only one aspect of Francisella virulence, it is a critical component in directly manipulating and interfering with cellular processes in the host cell.

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 otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology.

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 by utilizing expectation scores of single domain interactions.

Atopic dermatitis-associated protein interaction network lead to new insights in chronic sulfur mustard skin lesion mechanisms.

PubMed

Amiri, Mojtaba; Jafari, Mohieddin; Azimzadeh Jamalkandi, Sadegh; Davoodi, Seyed-Masoud

2013-10-01

Chronic sulfur mustard skin lesions (CSMSLs) are the most common complications of sulfur mustard exposure; however, its mechanism is not completely understood.According to clinical signs, there are similarities between CSMSL and atopic dermatitis (AD). In this study, proteomic results of AD were reviewed and the AD-associated protein-protein interaction network (PIN) was analyzed. According to centrality measurements, 16 proteins were designated as pivotal elements in AD mechanisms. Interestingly, most of these proteins had been reported in some sulfur mustard-related studies in late and acute phases separately. Based on the gene enrichment analysis, aging, cell response to stress, cancer, Toll- and NOD-like receptor and apoptosis signaling pathways have the greatest impact on the disease. By the analysis of directed protein interaction networks, it is concluded that TNF, IL-6, AKT1, NOS3 and CDKN1A are the most important proteins. It is possible that these proteins play role in the shared complications of AD and CSMSL including xerosis and itching.

An automated wide-field time-gated optically sectioning fluorescence lifetime imaging multiwell plate reader for high-content analysis of protein-protein interactions

NASA Astrophysics Data System (ADS)

Alibhai, Dominic; Kumar, Sunil; Kelly, Douglas; Warren, Sean; Alexandrov, Yuriy; Munro, Ian; McGinty, James; Talbot, Clifford; Murray, Edward J.; Stuhmeier, Frank; Neil, Mark A. A.; Dunsby, Chris; French, Paul M. W.

2011-03-01

We describe an optically-sectioned FLIM multiwell plate reader that combines Nipkow microscopy with wide-field time-gated FLIM, and its application to high content analysis of FRET. The system acquires sectioned FLIM images in <10 s/well, requiring only ~11 minutes to read a 96 well plate of live cells expressing fluorescent protein. It has been applied to study the formation of immature HIV virus like particles (VLPs) in live cells by monitoring Gag-Gag protein interactions using FLIM FRET of HIV-1 Gag transfected with CFP or YFP. VLP formation results in FRET between closely packed Gag proteins, as confirmed by our FLIM analysis that includes automatic image segmentation.

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.

Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

PubMed

Podder, Avijit; Jatana, Nidhi; Latha, N

2014-09-21

Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Identification and analysis of the proteins interacted with Prestin in cochlear outer hair cells of guinea pig].

PubMed

Luo, X; Wang, J Y; Zhang, F L; Xia, Y

2018-01-07

Objective: To explore the regulation and mechanism of Prestin protein by identifying the proteins interacted with Prestin in cochlear outer hair cell(OHC) and analyzing their biological function. Methods: Co-immunoprecipitation combined mass spectrometry technology was used to isolate and identify the proteins interacted with Prestin protein of OHC, bioinformatics was used to construct Prestin protein interaction network. The proteins interacted with Prestin in OHC of guinea pig were determined by matching primary interaction mass spectrometry with protein interaction network, and annotated their functions. Results: The results of co-immunoprecipitation combined with mass spectrometry showed that 116 kinds of credible proteins could interact with Prestin. By constructing Prestin protein interaction network, matching the results of mass spectrometry and analyzing of sub-cellular localization, eight kinds of proteins were confirmed that they interacted with Prestin directly, namely EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1, and ERH, respectively, which were mainly involved in the synthesis and transportation, transmembrane folding and localization, structural stability and signal transduction of Prestin protein. Conclusion: EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1 and ERH provide molecular basis for sensory amplification function of OHCs by participating in biotransformation, transmembrane folding and localization, signal transduction and other biological processes of Prestin protein.

Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone.

PubMed

Rodriguez-Rivas, Juan; Marsili, Simone; Juan, David; Valencia, Alfonso

2016-12-27

Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

d-Omix: a mixer of generic protein domain analysis tools.

PubMed

Wichadakul, Duangdao; Numnark, Somrak; Ingsriswang, Supawadee

2009-07-01

Domain combination provides important clues to the roles of protein domains in protein function, interaction and evolution. We have developed a web server d-Omix (a Mixer of Protein Domain Analysis Tools) aiming as a unified platform to analyze, compare and visualize protein data sets in various aspects of protein domain combinations. With InterProScan files for protein sets of interest provided by users, the server incorporates four services for domain analyses. First, it constructs protein phylogenetic tree based on a distance matrix calculated from protein domain architectures (DAs), allowing the comparison with a sequence-based tree. Second, it calculates and visualizes the versatility, abundance and co-presence of protein domains via a domain graph. Third, it compares the similarity of proteins based on DA alignment. Fourth, it builds a putative protein network derived from domain-domain interactions from DOMINE. Users may select a variety of input data files and flexibly choose domain search tools (e.g. hmmpfam, superfamily) for a specific analysis. Results from the d-Omix could be interactively explored and exported into various formats such as SVG, JPG, BMP and CSV. Users with only protein sequences could prepare an InterProScan file using a service provided by the server as well. The d-Omix web server is freely available at http://www.biotec.or.th/isl/Domix.

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%).

KFC Server: interactive forecasting of protein interaction hot spots.

PubMed

Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

2008-07-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

Comprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiae

PubMed Central

Reguly, Teresa; Breitkreutz, Ashton; Boucher, Lorrie; Breitkreutz, Bobby-Joe; Hon, Gary C; Myers, Chad L; Parsons, Ainslie; Friesen, Helena; Oughtred, Rose; Tong, Amy; Stark, Chris; Ho, Yuen; Botstein, David; Andrews, Brenda; Boone, Charles; Troyanskya, Olga G; Ideker, Trey; Dolinski, Kara; Batada, Nizar N; Tyers, Mike

2006-01-01

Background The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. Results We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications. This literature-curated (LC) dataset contains 33,311 interactions, on the order of all extant HTP datasets combined. Surprisingly, HTP protein-interaction datasets currently achieve only around 14% coverage of the interactions in the literature. The LC network nevertheless shares attributes with HTP networks, including scale-free connectivity and correlations between interactions, abundance, localization, and expression. We find that essential genes or proteins are enriched for interactions with other essential genes or proteins, suggesting that the global network may be functionally unified. This interconnectivity is supported by a substantial overlap of protein and genetic interactions in the LC dataset. We show that the LC dataset considerably improves the predictive power of network-analysis approaches. The full LC dataset is available at the BioGRID () and SGD () databases. Conclusion Comprehensive datasets of biological interactions derived from the primary literature provide critical benchmarks for HTP methods, augment functional prediction, and reveal system-level attributes of biological networks. PMID:16762047

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.

Interaction Network of Proteins Associated with Human Cytomegalovirus IE2-p86 Protein during Infection: A Proteomic Analysis

PubMed Central

Du, Guixin; Stinski, Mark F.

2013-01-01

Human cytomegalovirus protein IE2-p86 exerts its functions through interaction with other viral and cellular proteins. To further delineate its protein interaction network, we generated a recombinant virus expressing SG-tagged IE2-p86 and used tandem affinity purification coupled with mass spectrometry. A total of 9 viral proteins and 75 cellular proteins were found to associate with IE2-p86 protein during the first 48 hours of infection. The protein profile at 8, 24, and 48 h post infection revealed that UL84 tightly associated with IE2-p86, and more viral and cellular proteins came into association with IE2-p86 with the progression of virus infection. A computational analysis of the protein-protein interaction network indicated that all of the 9 viral proteins and most of the cellular proteins identified in the study are interconnected to varying degrees. Of the cellular proteins that were confirmed to associate with IE2-p86 by immunoprecipitation, C1QBP was further shown to be upregulated by HCMV infection and colocalized with IE2-p86, UL84 and UL44 in the virus replication compartment of the nucleus. The IE2-p86 interactome network demonstrated the temporal development of stable and abundant protein complexes that associate with IE2-p86 and provided a framework to benefit future studies of various protein complexes during HCMV infection. PMID:24358118

Signatures of Pleiotropy, Economy and Convergent Evolution in a Domain-Resolved Map of Human–Virus Protein–Protein Interaction Networks

PubMed Central

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 otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology. PMID:24339775

Analysis of the STAT3 interactome using in-situ biotinylation and SILAC.

PubMed

Blumert, Conny; Kalkhof, Stefan; Brocke-Heidrich, Katja; Kohajda, Tibor; von Bergen, Martin; Horn, Friedemann

2013-12-06

Signal transducer and activator of transcription 3 (STAT3) is activated by a variety of cytokines and growth factors. To generate a comprehensive data set of proteins interacting specifically with STAT3, we applied stable isotope labeling with amino acids in cell culture (SILAC). For high-affinity pull-down using streptavidin, we fused STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag), which did not affect STAT3 functions. By this approach, 3642 coprecipitated proteins were detected in human embryonic kidney-293 cells. Filtering using statistical and functional criteria finally extracted 136 proteins as putative interaction partners of STAT3. Both, a physical interaction network analysis and the enrichment of known and predicted interaction partners suggested that our filtering criteria successfully enriched true STAT3 interactors. Our approach identified numerous novel interactors, including ones previously predicted to associate with STAT3. By reciprocal coprecipitation, we were able to verify the physical association between STAT3 and selected interactors, including the novel interaction with TOX4, a member of the TOX high mobility group box family. Applying the same method, we next investigated the activation-dependency of the STAT3 interactome. Again, we identified both known and novel interactions. Thus, our approach allows to study protein-protein interaction effectively and comprehensively. The location, activity, function, degradation, and synthesis of proteins are significantly regulated by interactions of proteins with other proteins, biopolymers and small molecules. Thus, the comprehensive characterization of interactions of proteins in a given proteome is the next milestone on the path to understanding the biochemistry of the cell. In order to generate a comprehensive interactome dataset of proteins specifically interacting with a selected bait protein, we fused our bait protein STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag). This bio-tag allows an affinity pull-down using streptavidin but affected neither the activation of STAT3 by tyrosine phosphorylation nor its transactivating potential. We combined SILAC for accurate relative protein quantification, subcellular fractionation to increase the coverage of interacting proteins, high-affinity pull-down and a stringent filtering method to successfully analyze the interactome of STAT3. With our approach we confirmed several already known and identified numerous novel STAT3 interactors. The approach applied provides a rapid and effective method, which is broadly applicable for studying protein-protein interactions and their dependency on post-translational modifications. © 2013. Published by Elsevier B.V. All rights reserved.

Proteomic Analysis of Virus-Host Interactions in an Infectious Context Using Recombinant Viruses*

PubMed Central

Komarova, Anastassia V.; Combredet, Chantal; Meyniel-Schicklin, Laurène; Chapelle, Manuel; Caignard, Grégory; Camadro, Jean-Michel; Lotteau, Vincent; Vidalain, Pierre-Olivier; Tangy, Frédéric

2011-01-01

RNA viruses exhibit small-sized genomes encoding few proteins, but still establish complex networks of interactions with host cell components to achieve replication and spreading. Ideally, these virus-host protein interactions should be mapped directly in infected cell culture, but such a high standard is often difficult to reach when using conventional approaches. We thus developed a new strategy based on recombinant viruses expressing tagged viral proteins to capture both direct and indirect physical binding partners during infection. As a proof of concept, we engineered a recombinant measles virus (MV) expressing one of its virulence factors, the MV-V protein, with a One-STrEP amino-terminal tag. This allowed virus-host protein complex analysis directly from infected cells by combining modified tandem affinity chromatography and mass spectrometry analysis. Using this approach, we established a prosperous list of 245 cellular proteins interacting either directly or indirectly with MV-V, and including four of the nine already known partners of this viral factor. These interactions were highly specific of MV-V because they were not recovered when the nucleoprotein MV-N, instead of MV-V, was tagged. Besides key components of the antiviral response, cellular proteins from mitochondria, ribosomes, endoplasmic reticulum, protein phosphatase 2A, and histone deacetylase complex were identified for the first time as prominent targets of MV-V and the critical role of the later protein family in MV replication was addressed. Most interestingly, MV-V showed some preferential attachment to essential proteins in the human interactome network, as assessed by centrality and interconnectivity measures. Furthermore, the list of MV-V interactors also showed a massive enrichment for well-known targets of other viruses. Altogether, this clearly supports our approach based on reverse genetics of viruses combined with high-throughput proteomics to probe the interaction network that viruses establish in infected cells. PMID:21911578

Analysis of the interaction between membrane proteins and soluble binding partners by surface plasmon resonance.

PubMed

Wu, Zht Cheng; de Keyzer, Jeanine; Kusters, Ilja; Driessen, Arnold J M

2013-01-01

The interaction between membrane proteins and their (protein) ligands is conventionally investigated by nonequilibrium methods such as co-sedimentation or pull-down assays. Surface Plasmon Resonance can be used to monitor such binding events in real-time using isolated membranes immobilized to a surface providing insights in the kinetics of binding under equilibrium conditions. This application provides a fast, automated way to detect interacting species and to determine the kinetics and affinity (Kd) of the interaction.

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.

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

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.

Analysis of molecular assemblies by flow cytometry: determinants of Gi1 and by binding

NASA Astrophysics Data System (ADS)

Sarvazyan, Noune A.; Neubig, Richard R.

1998-05-01

We report here a novel application of flow cytometry for the quantitative analysis of the high affinity interaction between membrane proteins both in detergent solutions and when reconstituted into lipid vesicles. The approach is further advanced to permit the analysis of binding to expressed protein complexes in native cell membranes. The G protein heterotrimer signal transduction function links the extracellularly activated transmembrane receptors and intracellular effectors. Upon activation, (alpha) and (beta) (gamma) subunits of G protein undergo a dissociation/association cycle on the cell membrane interface. The binding parameters of solubilized G protein (alpha) and (beta) (gamma) subunits have been defined but little is known quantitatively about their interactions in the membrane. Using a novel flow cytometry approach, the binding of low nanomolar concentrations of fluorescein-labeled G(alpha) i1 (F- (alpha) ) to (beta) (gamma) both in detergent solution and in a lipid environment was quantitatively compared. Unlabeled (beta) $gama reconstituted in biotinylated phospholipid vesicles bound F-(alpha) tightly (Kd 6 - 12 nM) while the affinity for biotinylated-(beta) (gamma) in Lubrol was even higher (Kd of 2.9 nM). The application of this approach to proteins expressed in native cell membranes will advance our understanding of G protein function in context of receptor and effector interaction. More generally, this approach can be applied to study the interaction of any fluorescently labeled protein with a membrane protein which can be expressed in Sf9 plasma membranes.

Implication of the C terminus of the Prunus necrotic ringspot virus movement protein in cell-to-cell transport and in its interaction with the coat protein.

PubMed

Aparicio, Frederic; Pallás, Vicente; Sánchez-Navarro, Jesús

2010-07-01

The movement protein (MP) of Prunus necrotic ringspot virus (PNRSV) is required for viral transport. Previous analysis with MPs of other members of the family Bromoviridae has shown that the C-terminal part of these MPs plays a critical role in the interaction with the cognate coat protein (CP) and in cell-to-cell transport. Bimolecular fluorescence complementation and overlay analysis confirm an interaction between the C-terminal 38 aa of PNRSV MP and its cognate CP. Mutational analysis of the C-terminal region of the PNRSV MP revealed that its C-terminal 38 aa are dispensable for virus transport, however, the 4 aa preceding the dispensable C terminus are necessary to target the MP to the plasmodesmata and for the functionality of the protein. The capacity of the PNRSV MP to use either a CP-dependent or a CP-independent cell-to-cell transport is discussed.

Computational investigation of the HIV-1 Rev multimerization using molecular dynamics simulations and binding free energy calculations.

PubMed

Venken, Tom; Daelemans, Dirk; De Maeyer, Marc; Voet, Arnout

2012-06-01

The HIV Rev protein mediates the nuclear export of viral mRNA, and is thereby essential for the production of late viral proteins in the replication cycle. Rev forms a large organized multimeric protein-protein complex for proper functioning. Recently, the three-dimensional structures of a Rev dimer and tetramer have been resolved and provide the basis for a thorough structural analysis of the binding interaction. Here, molecular dynamics (MD) and binding free energy calculations were performed to elucidate the forces thriving dimerization and higher order multimerization of the Rev protein. It is found that despite the structural differences between each crystal structure, both display a similar behavior according to our calculations. Our analysis based on a molecular mechanics-generalized Born surface area (MM/GBSA) and a configurational entropy approach demonstrates that the higher order multimerization site is much weaker than the dimerization site. In addition, a quantitative hot spot analysis combined with a mutational analysis reveals the most contributing amino acid residues for protein interactions in agreement with experimental results. Additional residues were found in each interface, which are important for the protein interaction. The investigation of the thermodynamics of the Rev multimerization interactions performed here could be a further step in the development of novel antiretrovirals using structure based drug design. Moreover, the variability of the angle between each Rev monomer as measured during the MD simulations suggests a role of the Rev protein in allowing flexibility of the arginine rich domain (ARM) to accommodate RNA binding. Copyright © 2012 Wiley Periodicals, Inc.

Genome-wide analysis of protein disorder in Arabidopsis thaliana: implications for plant environmental adaptation.

PubMed

Pietrosemoli, Natalia; García-Martín, Juan A; Solano, Roberto; Pazos, Florencio

2013-01-01

Intrinsically disordered proteins/regions (IDPs/IDRs) are currently recognized as a widespread phenomenon having key cellular functions. Still, many aspects of the function of these proteins need to be unveiled. IDPs conformational flexibility allows them to recognize and interact with multiple partners, and confers them larger interaction surfaces that may increase interaction speed. For this reason, molecular interactions mediated by IDPs/IDRs are particularly abundant in certain types of protein interactions, such as those of signaling and cell cycle control. We present the first large-scale study of IDPs in Arabidopsis thaliana, the most widely used model organism in plant biology, in order to get insight into the biological roles of these proteins in plants. The work includes a comparative analysis with the human proteome to highlight the differential use of disorder in both species. Results show that while human proteins are in general more disordered, certain functional classes, mainly related to environmental response, are significantly more enriched in disorder in Arabidopsis. We propose that because plants cannot escape from environmental conditions as animals do, they use disorder as a simple and fast mechanism, independent of transcriptional control, for introducing versatility in the interaction networks underlying these biological processes so that they can quickly adapt and respond to challenging environmental conditions.

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 model organisms becomes available and is readily scalable to a genome-wide application.

A critical analysis of computational protein design with sparse residue interaction graphs

PubMed Central

Georgiev, Ivelin S.

2017-01-01

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies. PMID:28358804

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.

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/.

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

PubMed

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra; Sacquin-Mora, Sophie

2016-10-01

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Interactions between late acting proteins required for peptidoglycan synthesis during sporulation

PubMed Central

Fay, Allison; Meyer, Pablo; Dworkin, Jonathan

2010-01-01

The requirement of peptidoglycan synthesis for growth complicates the analysis of interactions between proteins involved in this pathway. In particular, the later steps that involve membrane-linked substrates have proven largely recalcitrant to in vivo analysis. Here we have taken advantage of the peptidoglycan synthesis that occurs during sporulation in Bacillus subtilis to examine the interactions between SpoVE, a non-essential, sporulation-specific homolog of the well-conserved and essential SEDS proteins, and SpoVD, a non-essential class B penicillin binding protein (PBP). We found that localization of SpoVD is dependent on SpoVE and that SpoVD protects SpoVE from in vivo proteolysis. Co-immunoprecipitations and Fluorescence Resonance Energy Transfer experiments indicated that SpoVE and SpoVD interact and co-affinity purification in E. coli demonstrated that this interaction is direct. Finally, we generated a functional protein consisting of a SpoVE-SpoVD fusion and found that a loss-of-function point mutation in either part of the fusion resulted in a loss of function of the entire fusion that was not complemented by a wild type protein. Thus, SpoVE has a direct and functional interaction with SpoVD and this conclusion will facilitate understanding the essential function SpoVE and related SEDS proteins such as FtsW and RodA play in bacterial growth and division. PMID:20417640

[FANCA gene mutation analysis in Fanconi anemia patients].

PubMed

Chen, Fei; Peng, Guang-Jie; Zhang, Kejian; Hu, Qun; Zhang, Liu-Qing; Liu, Ai-Guo

2005-10-01

To screen the FANCA gene mutation and explore the FANCA protein function in Fanconi anemia (FA) patients. FANCA protein expression and its interaction with FANCF were analyzed using Western blot and immunoprecipitation in 3 cases of FA-A. Genomic DNA was used for MLPA analysis followed by sequencing. FANCA protein was undetectable and FANCA and FANCF protein interaction was impaired in these 3 cases of FA-A. Each case of FA-A contained biallelic pathogenic mutations in FANCA gene. No functional FANCA protein was found in these 3 cases of FA-A, and intragenic deletion, frame shift and splice site mutation were the major pathogenic mutations found in FANCA gene.

Interactome of the hepatitis C virus: Literature mining with ANDSystem.

PubMed

Saik, Olga V; Ivanisenko, Timofey V; Demenkov, Pavel S; Ivanisenko, Vladimir A

2016-06-15

A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, respectively. Thus, ANDSystem, extended with the HCV interactome, can be helpful in a wide range of issues related to analyzing HCV-host interactions in the search for anti-HCV drug targets. The demo version of the extended ANDSystem covered here containing only interactions between human proteins, genes, metabolites, diseases, miRNAs and molecular-genetic pathways, as well as interactions between human proteins/genes and HCV proteins, is freely available at the following web address: http://www-bionet.sscc.ru/psd/andhcv/. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Combining affinity enrichment, cross-linking with photo-amino acids, and mass spectrometry for probing protein kinase D2 interactions.

PubMed

Häupl, Björn; Ihling, Christian H; Sinz, Andrea

2017-04-07

We present a novel approach that relies on the affinity capture of protein interaction partners from a complex mixture, followed by covalent fixation via UV-induced activation of incorporated diazirine photo-reactive amino acids (photo-methionine and photo-leucine). The captured protein complexes are enzymatically digested and interacting proteins are identified and quantified by label-free LC/MS analysis. Using HeLa cell lysates with photo-methionine and photo-leucine-labeled proteins, we were able to capture and preserve protein interactions that are otherwise elusive in conventional pull-down experiments. Our approach is exemplified for mapping the protein interaction network of protein kinase D2, but has the potential be applied to any protein system. Data are available via ProteomeXchange with identifiers PXD005346 (photo-amino acid incorporation) and PXD005349 (enrichment experiments). This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Dengue-2 structural proteins associate with human proteins to produce a coagulation and innate immune response biased interactome.

PubMed

Folly, Brenda B; Weffort-Santos, Almeriane M; Fathman, C G; Soares, Luis R B

2011-01-31

Dengue virus infection is a public health threat to hundreds of millions of individuals in the tropical regions of the globe. Although Dengue infection usually manifests itself in its mildest, though often debilitating clinical form, dengue fever, life-threatening complications commonly arise in the form of hemorrhagic shock and encephalitis. The etiological basis for the virus-induced pathology in general, and the different clinical manifestations in particular, are not well understood. We reasoned that a detailed knowledge of the global biological processes affected by virus entry into a cell might help shed new light on this long-standing problem. A bacterial two-hybrid screen using DENV2 structural proteins as bait was performed, and the results were used to feed a manually curated, global dengue-human protein interaction network. Gene ontology and pathway enrichment, along with network topology and microarray meta-analysis, were used to generate hypothesis regarding dengue disease biology. Combining bioinformatic tools with two-hybrid technology, we screened human cDNA libraries to catalogue proteins physically interacting with the DENV2 virus structural proteins, Env, cap and PrM. We identified 31 interacting human proteins representing distinct biological processes that are closely related to the major clinical diagnostic feature of dengue infection: haemostatic imbalance. In addition, we found dengue-binding human proteins involved with additional key aspects, previously described as fundamental for virus entry into cells and the innate immune response to infection. Construction of a DENV2-human global protein interaction network revealed interesting biological properties suggested by simple network topology analysis. Our experimental strategy revealed that dengue structural proteins interact with human protein targets involved in the maintenance of blood coagulation and innate anti-viral response processes, and predicts that the interaction of dengue proteins with a proposed human protein interaction network produces a modified biological outcome that may be behind the hallmark pathologies of dengue infection.

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

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

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system

DOE PAGES

AlQuraishi, Mohammed; Tang, Shengdong; Xia, Xide

2015-11-19

Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions. We have developed an integrated affinity-structure database inmore » which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Lastly, this database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.« less

Real-Time Analysis of Specific Protein-DNA Interactions with Surface Plasmon Resonance

PubMed Central

Ritzefeld, Markus; Sewald, Norbert

2012-01-01

Several proteins, like transcription factors, bind to certain DNA sequences, thereby regulating biochemical pathways that determine the fate of the corresponding cell. Due to these key positions, it is indispensable to analyze protein-DNA interactions and to identify their mode of action. Surface plasmon resonance is a label-free method that facilitates the elucidation of real-time kinetics of biomolecular interactions. In this article, we focus on this biosensor-based method and provide a detailed guide how SPR can be utilized to study binding of proteins to oligonucleotides. After a description of the physical phenomenon and the instrumental realization including fiber-optic-based SPR and SPR imaging, we will continue with a survey of immobilization methods. Subsequently, we will focus on the optimization of the experiment, expose pitfalls, and introduce how data should be analyzed and published. Finally, we summarize several interesting publications of the last decades dealing with protein-DNA and RNA interaction analysis by SPR. PMID:22500214

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 antibody-antigen complexes, the sign is somewhat ambiguous. From the evolutionary perspective, while protease-inhibitors and sig-naling proteins have optimized their interfaces to suit their biological functions, antibody-antigen interactions are the happenstance, implying that antibody-antigen complexes do not show distinctive interaction types. Persistent interaction types such as pi...pi, amide-carbonyl, and hydroxyl-carbonyl interaction, are also investigated. Analyzing the structural orientations of the pi...pi stacking interactions, we find that herringbone shape is a major configuration in transient protein-protein interfaces. This result is different from that of protein core, where parallel-displaced configurations are the major configuration. We also analyze overall trend of amide-carbonyl and hydroxyl-carbonyl interactions. It is noticeable that nearly 82% of the interfaces have at least one hydroxyl-carbonyl interactions. (c) 2006 Wiley-Liss, Inc.

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.

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

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

Extreme disorder in an ultrahigh-affinity protein complex

NASA Astrophysics Data System (ADS)

Borgia, Alessandro; Borgia, Madeleine B.; Bugge, Katrine; Kissling, Vera M.; Heidarsson, Pétur O.; Fernandes, Catarina B.; Sottini, Andrea; Soranno, Andrea; Buholzer, Karin J.; Nettels, Daniel; Kragelund, Birthe B.; Best, Robert B.; Schuler, Benjamin

2018-03-01

Molecular communication in biology is mediated by protein interactions. According to the current paradigm, the specificity and affinity required for these interactions are encoded in the precise complementarity of binding interfaces. Even proteins that are disordered under physiological conditions or that contain large unstructured regions commonly interact with well-structured binding sites on other biomolecules. Here we demonstrate the existence of an unexpected interaction mechanism: the two intrinsically disordered human proteins histone H1 and its nuclear chaperone prothymosin-α associate in a complex with picomolar affinity, but fully retain their structural disorder, long-range flexibility and highly dynamic character. On the basis of closely integrated experiments and molecular simulations, we show that the interaction can be explained by the large opposite net charge of the two proteins, without requiring defined binding sites or interactions between specific individual residues. Proteome-wide sequence analysis suggests that this interaction mechanism may be abundant in eukaryotes.

A comparative analysis of human plasma and serum proteins by combining native PAGE, whole-gel slicing and quantitative LC-MS/MS: Utilizing native MS-electropherograms in proteomic analysis for discovering structure and interaction-correlated differences.

PubMed

Wen, Meiling; Jin, Ya; Manabe, Takashi; Chen, Shumin; Tan, Wen

2017-12-01

MS identification has long been used for PAGE-separated protein bands, but global and systematic quantitation utilizing MS after PAGE has remained rare and not been reported for native PAGE. Here we reported on a new method combining native PAGE, whole-gel slicing and quantitative LC-MS/MS, aiming at comparative analysis on not only abundance, but also structures and interactions of proteins. A pair of human plasma and serum samples were used as test samples and separated on a native PAGE gel. Six lanes of each sample were cut, each lane was further sliced into thirty-five 1.1 mm × 1.1 mm squares and all the squares were subjected to standardized procedures of in-gel digestion and quantitative LC-MS/MS. The results comprised 958 data rows that each contained abundance values of a protein detected in one square in eleven gel lanes (one plasma lane excluded). The data were evaluated to have satisfactory reproducibility of assignment and quantitation. Totally 315 proteins were assigned, with each protein assigned in 1-28 squares. The abundance distributions in the plasma and serum gel lanes were reconstructed for each protein, named as "native MS-electropherograms". Comparison of the electropherograms revealed significant plasma-versus-serum differences on 33 proteins in 87 squares (fold difference > 2 or < 0.5, p < 0.05). Many of the differences matched with accumulated knowledge on protein interactions and proteolysis involved in blood coagulation, complement and wound healing processes. We expect this method would be useful to provide more comprehensive information in comparative proteomic analysis, on both quantities and structures/interactions. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

PubMed Central

Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

2015-01-01

It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

PubMed

Ding, Dewu; Sun, Xiao

2018-01-16

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c -type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

Proteomic analysis and cross species comparison of casein fractions from the milk of dairy animals

PubMed Central

Wang, Xiaxia; Zhao, Xiaowei; Huang, Dongwei; Pan, Xiaocheng; Qi, Yunxia; Yang, Yongxin; Zhao, Huiling; Cheng, Guanglong

2017-01-01

Casein micelles contribute to the physicochemical properties of milk and may also influence its functionality. At present, however, there is an incomplete understanding of the casein micelle associated proteins and its diversity among the milk obtained from different species. Therefore, milk samples were collected from seven dairy animals groups, casein fractions were prepared by ultracentrifugation and their constituent proteins were identified by liquid chromatography tandem mass spectrometry. A total of 193 distinct proteins were identified among all the casein micelle preparations. Protein interaction analysis indicated that caseins could interact with major whey proteins, including β-lactoglobulin, α-lactalbumin, lactoferrin, and serum albumin, and then whey proteins interacted with other proteins. Pathway analysis found that the peroxisome proliferator-activated receptor signaling pathway is shared among the studied animals. Additionally, galactose metabolism pathway is also found to be commonly involved for proteins derived from camel and horse milk. According to the similarity of casein micelle proteomes, two major sample clusters were classified into ruminant animals (Holstein and Jersey cows, buffaloes, yaks, and goats) and non-ruminants (camels and horses). Our results provide new insights into the protein profile associated with casein micelles and the functionality of the casein micelle from the studied animals. PMID:28240229

Purification and Crystallization Reveal Two Types of Interactions of the Fusion Protein Homotrimer of Semliki Forest Virus

PubMed Central

Gibbons, Don L.; Reilly, Brigid; Ahn, Anna; Vaney, Marie-Christine; Vigouroux, Armelle; Rey, Felix A.; Kielian, Margaret

2004-01-01

The fusion proteins of the alphaviruses and flaviviruses have a similar native structure and convert to a highly stable homotrimer conformation during the fusion of the viral and target membranes. The properties of the alpha- and flavivirus fusion proteins distinguish them from the class I viral fusion proteins, such as influenza virus hemagglutinin, and establish them as the first members of the class II fusion proteins. Understanding how this new class carries out membrane fusion will require analysis of the structural basis for both the interaction of the protein subunits within the homotrimer and their interaction with the viral and target membranes. To this end we report a purification method for the E1 ectodomain homotrimer from the alphavirus Semliki Forest virus. The purified protein is trimeric, detergent soluble, retains the characteristic stability of the starting homotrimer, and is free of lipid and other contaminants. In contrast to the postfusion structures that have been determined for the class I proteins, the E1 homotrimer contains the fusion peptide region responsible for interaction with target membranes. This E1 trimer preparation is an excellent candidate for structural studies of the class II viral fusion proteins, and we report conditions that generate three-dimensional crystals suitable for analysis by X-ray diffraction. Determination of the structure will provide our first high-resolution views of both the low-pH-induced trimeric conformation and the target membrane-interacting region of the alphavirus fusion protein. PMID:15016874

Neurofibromin interacts with CRMP-2 and CRMP-4 in rat brain

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

Lin, Y.-L.; Hsueh, Y.-P., E-mail: yph@gate.sinica.edu.tw

Neurofibromin, encoded by the neurofibromatosis type 1 (NF1) gene, regulates the Ras and cAMP pathways and plays a role in proliferation and neuronal morphogenesis. The details of the molecular mechanism of neurofibromin action in these processes are still unclear. In this study, immunoprecipitation and proteomics were used to identify novel proteins from rat brain that interact with neurofibromin. Mass spectrometry analysis showed that two proteins, the collapsin response mediator protein-2 (CRMP-2) and propionyl-CoA carboxylase alpha chain (PCCA), associated with neurofibromin. Immunoprecipitation-immunoblotting analysis confirmed the interactions between neurofibromin and CRMP-2 and CRMP-4, but not CRMP-1, in rat brain. CDK5, a kinasemore » that regulates CRMP-2 in axonal outgrowth, was required for the interaction between neurofibromin and CRMP-2. Since both neurofibromin and CRMP proteins are involved in proliferation and axonal morphogenesis, these results suggest that the interaction with CRMPs contributes to the function of neurofibromin in tumorigenesis and neuronal morphogenesis.« less

Protein interactions during flour mixing using wheat flour with altered starch.

PubMed

Wang, Xiaolong; Appels, Rudi; Zhang, Xiaoke; Diepeveen, Dean; Torok, Kitti; Tomoskozi, Sandor; Bekes, Ferenc; Ma, Wujun; Sharp, Peter; Islam, Shahidul

2017-09-15

Wheat grain proteins responses to mixing and thermal treatment were investigated using Mixolab-dough analysis systems with flour from two cultivars, Ventura-26 (normal amylose content) and Ventura-19 (reduced amylose content). Size exclusion high performance liquid chromatography (SE-HPLC) and two-dimensional gel electrophoresis (2-DGE) analysis revealed that, stress associated and metabolic proteins largely interacted with dough matrix of Ventura-26 after 26min (56°C); gliadins, avenin-like b proteins, LMW-GSs, and partial globulins showed stronger interactions within the dough matrix of Ventura-26 at 32min/C3 (80°C), thereafter, however, stronger protein interactions were observed within the dough matrix of Ventura-19 at 38min/C4 (85°C) and 43min (80°C). Thirty-seven proteins associated with changes in dough matrix due to reduced amylose content were identified by mass spectrometry and mainly annotated to the chromosome group 1, 4, and 6. The findings provide new entry points for modifying final product attributes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Proteomic Analysis of the Mediator Complex Interactome in Saccharomyces cerevisiae

PubMed Central

Uthe, Henriette; Vanselow, Jens T.; Schlosser, Andreas

2017-01-01

Here we present the most comprehensive analysis of the yeast Mediator complex interactome to date. Particularly gentle cell lysis and co-immunopurification conditions allowed us to preserve even transient protein-protein interactions and to comprehensively probe the molecular environment of the Mediator complex in the cell. Metabolic 15N-labeling thereby enabled stringent discrimination between bona fide interaction partners and nonspecifically captured proteins. Our data indicates a functional role for Mediator beyond transcription initiation. We identified a large number of Mediator-interacting proteins and protein complexes, such as RNA polymerase II, general transcription factors, a large number of transcriptional activators, the SAGA complex, chromatin remodeling complexes, histone chaperones, highly acetylated histones, as well as proteins playing a role in co-transcriptional processes, such as splicing, mRNA decapping and mRNA decay. Moreover, our data provides clear evidence, that the Mediator complex interacts not only with RNA polymerase II, but also with RNA polymerases I and III, and indicates a functional role of the Mediator complex in rRNA processing and ribosome biogenesis. PMID:28240253

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.

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 (https://bioconda.github.io).

Bound Water at Protein-Protein Interfaces: Partners, Roles and Hydrophobic Bubbles as a Conserved Motif

PubMed Central

Ahmed, Mostafa H.; Spyrakis, Francesca; Cozzini, Pietro; Tripathi, Parijat K.; Mozzarelli, Andrea; Scarsdale, J. Neel; Safo, Martin A.; Kellogg, Glen E.

2011-01-01

Background There is a great interest in understanding and exploiting protein-protein associations as new routes for treating human disease. However, these associations are difficult to structurally characterize or model although the number of X-ray structures for protein-protein complexes is expanding. One feature of these complexes that has received little attention is the role of water molecules in the interfacial region. Methodology A data set of 4741 water molecules abstracted from 179 high-resolution (≤ 2.30 Å) X-ray crystal structures of protein-protein complexes was analyzed with a suite of modeling tools based on the HINT forcefield and hydrogen-bonding geometry. A metric termed Relevance was used to classify the general roles of the water molecules. Results The water molecules were found to be involved in: a) (bridging) interactions with both proteins (21%), b) favorable interactions with only one protein (53%), and c) no interactions with either protein (26%). This trend is shown to be independent of the crystallographic resolution. Interactions with residue backbones are consistent for all classes and account for 21.5% of all interactions. Interactions with polar residues are significantly more common for the first group and interactions with non-polar residues dominate the last group. Waters interacting with both proteins stabilize on average the proteins' interaction (−0.46 kcal mol−1), but the overall average contribution of a single water to the protein-protein interaction energy is unfavorable (+0.03 kcal mol−1). Analysis of the waters without favorable interactions with either protein suggests that this is a conserved phenomenon: 42% of these waters have SASA ≤ 10 Å2 and are thus largely buried, and 69% of these are within predominantly hydrophobic environments or “hydrophobic bubbles”. Such water molecules may have an important biological purpose in mediating protein-protein interactions. PMID:21961043

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

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.

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.

Great interactions: How binding incorrect partners can teach us about protein recognition and function

PubMed Central

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra

2016-01-01

ABSTRACT Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:27287388

Inferring protein domains associated with drug side effects based on drug-target interaction network.

PubMed

Iwata, Hiroaki; Mizutani, Sayaka; Tabei, Yasuo; Kotera, Masaaki; Goto, Susumu; Yamanishi, Yoshihiro

2013-01-01

Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.

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.

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.

Partial molar volume of proteins studied by the three-dimensional reference interaction site model theory.

PubMed

Imai, Takashi; Kovalenko, Andriy; Hirata, Fumio

2005-04-14

The three-dimensional reference interaction site model (3D-RISM) theory is applied to the analysis of hydration effects on the partial molar volume of proteins. For the native structure of some proteins, the partial molar volume is decomposed into geometric and hydration contributions using the 3D-RISM theory combined with the geometric volume calculation. The hydration contributions are correlated with the surface properties of the protein. The thermal volume, which is the volume of voids around the protein induced by the thermal fluctuation of water molecules, is directly proportional to the accessible surface area of the protein. The interaction volume, which is the contribution of electrostatic interactions between the protein and water molecules, is apparently governed by the charged atomic groups on the protein surface. The polar atomic groups do not make any contribution to the interaction volume. The volume differences between low- and high-pressure structures of lysozyme are also analyzed by the present method.

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.

Proteomic analysis of the herpes simplex virus 1 virion protein 16 transactivator protein in infected cells.

PubMed

Suk, Hyung; Knipe, David M

2015-06-01

The herpes simplex virus 1 virion protein 16 (VP16) tegument protein forms a transactivation complex with the cellular proteins host cell factor 1 (HCF-1) and octamer-binding transcription factor 1 (Oct-1) upon entry into the host cell. VP16 has also been shown to interact with a number of virion tegument proteins and viral glycoprotein H to promote viral assembly, but no comprehensive study of the VP16 proteome has been performed at early times postinfection. We therefore performed a proteomic analysis of VP16-interacting proteins at 3 h postinfection. We confirmed the interaction of VP16 with HCF-1 and a large number of cellular Mediator complex proteins, but most surprisingly, we found that the major viral protein associating with VP16 is the infected cell protein 4 (ICP4) immediate-early (IE) transactivator protein. These results raise the potential for a new function for VP16 in associating with the IE ICP4 and playing a role in transactivation of early and late gene expression, in addition to its well-documented function in transactivation of IE gene expression. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of protein interactions within the cytokinin-signaling pathway of Arabidopsis thaliana.

PubMed

Dortay, Hakan; Mehnert, Nijuscha; Bürkle, Lukas; Schmülling, Thomas; Heyl, Alexander

2006-10-01

The signal of the plant hormone cytokinin is perceived by membrane-located sensor histidine kinases and transduced by other members of the plant two-component system. In Arabidopsis thaliana, 28 two-component system proteins (phosphotransmitters and response regulators) act downstream of three receptors, transmitting the signal from the membrane to the nucleus and modulating the cellular response. Although the principal signaling mechanism has been elucidated, redundancy in the system has made it difficult to understand which of the many components interact to control the downstream biological processes. Here, we present a large-scale interaction study comprising most members of the Arabidopsis cytokinin signaling pathway. Using the yeast two-hybrid system, we detected 42 new interactions, of which more than 90% were confirmed by in vitro coaffinity purification. There are distinct patterns of interaction between protein families, but only a few interactions between proteins of the same family. An interaction map of this signaling pathway shows the Arabidopsis histidine phosphotransfer proteins as hubs, which interact with members from all other protein families, mostly in a redundant fashion. Domain-mapping experiments revealed the interaction domains of the proteins of this pathway. Analyses of Arabidopsis histidine phosphotransfer protein 5 mutant proteins showed that the presence of the canonical phospho-accepting histidine residue is not required for the interactions. Interaction of A-type response regulators with Arabidopsis histidine phosphotransfer proteins but not with B-type response regulators suggests that their known activity in feedback regulation may be realized by interfering at the level of Arabidopsis histidine phosphotransfer protein-mediated signaling. This study contributes to our understanding of the protein interactions of the cytokinin-signaling system and provides a framework for further functional studies in planta.

New methods for the analysis of the protein-solvent interface

NASA Astrophysics Data System (ADS)

Goodfellow, Julia M.; Pitt, William R.; Smart, Oliver S.; Williams, Mark A.

1995-09-01

The protein-solvent interface is complex and may include solvent channels and cavities as well as the normal surface water molecules. We describe several algorithms for investigating the intra- and inter-molecular interactions of proteins in general but with the aim of developing methods to accurately and definitively characterise the interactions of water and other small ligands with proteins. Specifically, we present the methods which underlie three programs (AQUARIUS2, HOLE and PRO_ACT) which can be used to to look at different aspects of these interactions.

Multi-Harmony: detecting functional specificity from sequence alignment

PubMed Central

Brandt, Bernd W.; Feenstra, K. Anton; Heringa, Jaap

2010-01-01

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein–protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww. PMID:20525785

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

Highly Dynamic Anion-Quadrupole Networks in Proteins.

PubMed

Kapoor, Karan; Duff, Michael R; Upadhyay, Amit; Bucci, Joel C; Saxton, Arnold M; Hinde, Robert J; Howell, Elizabeth E; Baudry, Jerome

2016-11-01

The dynamics of anion-quadrupole (or anion-π) interactions formed between negatively charged (Asp/Glu) and aromatic (Phe) side chains are for the first time computationally characterized in RmlC (Protein Data Bank entry 1EP0 ), a homodimeric epimerase. Empirical force field-based molecular dynamics simulations predict anion-quadrupole pairs and triplets (anion-anion-π and anion-π-π) are formed by the protein during the simulated trajectory, which suggests that the anion-quadrupole interactions may provide a significant contribution to the overall stability of the protein, with an average of -1.6 kcal/mol per pair. Some anion-π interactions are predicted to form during the trajectory, extending the number of anion-quadrupole interactions beyond those predicted from crystal structure analysis. At the same time, some anion-π pairs observed in the crystal structure exhibit marginal stability. Overall, most anion-π interactions alternate between an "on" state, with significantly stabilizing energies, and an "off" state, with marginal or null stabilizing energies. The way proteins possibly compensate for transient loss of anion-quadrupole interactions is characterized in the RmlC aspartate 84-phenylalanine 112 anion-quadrupole pair observed in the crystal structure. A double-mutant cycle analysis of the thermal stability suggests a possible loss of anion-π interactions compensated by variations of hydration of the residues and formation of compensating electrostatic interactions. These results suggest that near-planar anion-quadrupole pairs can exist, sometimes transiently, which may play a role in maintaining the structural stability and function of the protein, in an otherwise very dynamic interplay of a nonbonded interaction network as well as solvent effects.

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.

RNA-binding Protein Immunoprecipitation (RIP) to Examine AUF1 Binding to Senescence-Associated Secretory Phenotype (SASP) Factor mRNA

PubMed Central

Alspach, Elise; Stewart, Sheila A.

2016-01-01

Immunoprecipitation and subsequent isolation of nucleic acids allows for the investigation of protein:nucleic acid interactions. RNA-binding protein immunoprecipitation (RIP) is used for the analysis of protein interactions with mRNA. Combining RIP with quantitative real-time PCR (qRT-PCR) further enhances the RIP technique by allowing for the quantitative assessment of RNA-binding protein interactions with their target mRNAs, and how these interactions change in different cellular settings. Here, we describe the immunoprecipitation of the RNA-binding protein AUF1 with several different factors associated with the senescence-associated secretory phenotype (SASP) (Alspach and Stewart, 2013), specifically IL6 and IL8. This protocol was originally published in Alspach et al. (2014). PMID:27453911

Differential proteome profiling in the hippocampus of amnesic mice.

PubMed

Baghel, Meghraj Singh; Thakur, Mahendra Kumar

2017-08-01

Amnesia or memory loss is associated with brain aging and several neurodegenerative pathologies including Alzheimer's disease (AD). This can be induced by a cholinergic antagonist scopolamine but the underlying molecular mechanism is poorly understood. This study of proteome profiling in the hippocampus could provide conceptual insights into the molecular mechanisms involved in amnesia. To reveal this, mice were administered scopolamine to induce amnesia and memory impairment was validated by novel object recognition test. Using two-dimensional gel electrophoresis coupled with MALDI-MS/MS, we have analyzed the hippocampal proteome and identified 18 proteins which were differentially expressed. Out of these proteins, 11 were downregulated and 7 were upregulated in scopolamine-treated mice as compared to control. In silico analysis showed that the majority of identified proteins are involved in metabolism, catalytic activity, and cytoskeleton architectural functions. STRING interaction network analysis revealed that majority of identified proteins exhibit common association with Actg1 cytoskeleton and Vdac1 energy transporter protein. Furthermore, interaction map analysis showed that Fascin1 and Coronin 1b individually interact with Actg1 and regulate the actin filament dynamics. Vdac1 was significantly downregulated in amnesic mice and showed interaction with other proteins in interaction network. Therefore, we silenced Vdac1 in the hippocampus of normal young mice and found similar impairment in recognition memory of Vdac1 silenced and scopolamine-treated mice. Thus, these findings suggest that Vdac1-mediated disruption of energy metabolism and cytoskeleton architecture might be involved in scopolamine-induced amnesia. © 2017 Wiley Periodicals, Inc.

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

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.

SPR and electrochemical analyses of interactions between CYP3A4 or 3A5 and cytochrome b5

NASA Astrophysics Data System (ADS)

Gnedenko, O. V.; Yablokov, E. O.; Usanov, S. A.; Mukha, D. V.; Sergeev, G. V.; Bulko, T. V.; Kuzikov, A. V.; Moskaleva, N. E.; Shumyantseva, V. V.; Ivanov, A. S.; Archakov, A. I.

2014-02-01

The combination of SPR biosensor with electrochemical analysis was used for the study of protein-protein interaction between cytochromes CYP3A4 or 3А5 and cytochromes b5: the microsomal, mitochondrial forms of this protein, and 2 ≪chimeric≫ proteins. Kinetic constants of CYP3A4 and CYP3А5 complex formation with cytochromes b5 were determined by the SPR biosensor. Essential distinction between CYP3A4 and CYP3A5 was observed upon their interactions with mitochondrial cytochrome b5. The electrochemical analysis of CYP3A4, CYP3A5, and cytochromes b5 immobilized on screen printed graphite electrodes modified with membranous matrix revealed that these proteins have very close reduction potentials -0.435 to -0.350 V (vs. Ag/AgCl).

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.

Complementary analysis of the hard and soft protein corona: sample preparation critically effects corona composition.

PubMed

Winzen, S; Schoettler, S; Baier, G; Rosenauer, C; Mailaender, V; Landfester, K; Mohr, K

2015-02-21

Here we demonstrate how a complementary analysis of nanocapsule-protein interactions with and without application media allows gaining insights into the so called hard and soft protein corona. We have investigated how both human plasma and individual proteins (human serum albumin (HSA), apolipoprotein A-I (ApoA-I)) adsorb and interact with hydroxyethyl starch (HES) nanocapsules possessing different functionalities. To analyse the hard protein corona we used sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) and a protein quantitation assay. No significant differences were observed with regards to the hard protein corona. For analysis of the soft protein corona we characterized the nanocapsule-protein interaction with isothermal titration calorimetry (ITC) and dynamic light scattering (DLS). DLS and ITC measurements revealed that a high amount of plasma proteins were adsorbed onto the capsules' surface. Although HSA was not detected in the hard protein corona, ITC measurements indicated the adsorption of an HSA amount similar to plasma with a low binding affinity and reaction heat. In contrast, only small amounts of ApoA-I protein adsorb to the capsules with high binding affinities. Through a comparison of these methods we have identified ApoA-I to be a component of the hard protein corona and HSA as a component of the soft corona. We demonstrate a pronounced difference in the protein corona observed depending on the type of characterization technique applied. As the biological identity of a particle is given by the protein corona it is crucial to use complementary characterization techniques to analyse different aspects of the protein corona.

SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis.

PubMed

Tsagrasoulis, Dimosthenis; Danos, Vasilis; Kissa, Maria; Trimpalis, Philip; Koumandou, V Lila; Karagouni, Amalia D; Tsakalidis, Athanasios; Kossida, Sophia

2012-01-01

Domain Fusion Analysis takes advantage of the fact that certain proteins in a given proteome A, are found to have statistically significant similarity with two separate proteins in another proteome B. In other words, the result of a fusion event between two separate proteins in proteome B is a specific full-length protein in proteome A. In such a case, it can be safely concluded that the protein pair has a common biological function or even interacts physically. In this paper, we present the Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and the Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events (both available at: http://www.bioacademy.gr/bioinformatics/projects/ProteinFusion/index.htm). Finally, we analyze the proteomes of three microorganisms using these tools in order to demonstrate their functionality.

SAFE Software and FED Database to Uncover Protein-Protein Interactions using Gene Fusion Analysis

PubMed Central

Tsagrasoulis, Dimosthenis; Danos, Vasilis; Kissa, Maria; Trimpalis, Philip; Koumandou, V. Lila; Karagouni, Amalia D.; Tsakalidis, Athanasios; Kossida, Sophia

2012-01-01

Domain Fusion Analysis takes advantage of the fact that certain proteins in a given proteome A, are found to have statistically significant similarity with two separate proteins in another proteome B. In other words, the result of a fusion event between two separate proteins in proteome B is a specific full-length protein in proteome A. In such a case, it can be safely concluded that the protein pair has a common biological function or even interacts physically. In this paper, we present the Fusion Events Database (FED), a database for the maintenance and retrieval of fusion data both in prokaryotic and eukaryotic organisms and the Software for the Analysis of Fusion Events (SAFE), a computational platform implemented for the automated detection, filtering and visualization of fusion events (both available at: http://www.bioacademy.gr/bioinformatics/projects/ProteinFusion/index.htm). Finally, we analyze the proteomes of three microorganisms using these tools in order to demonstrate their functionality. PMID:22267904

Hybrid In Silico/In Vitro Approaches for the Identification of Functional Cholesterol-Binding Domains in Membrane Proteins.

PubMed

Di Scala, Coralie; Fantini, Jacques

2017-01-01

In eukaryotic cells, cholesterol is an important regulator of a broad range of membrane proteins, including receptors, transporters, and ion channels. Understanding how cholesterol interacts with membrane proteins is a difficult task because structural data of these proteins complexed with cholesterol are scarce. Here, we describe a dual approach based on in silico studies of protein-cholesterol interactions, combined with physico-chemical measurements of protein insertion into cholesterol-containing monolayers. Our algorithm is validated through careful analysis of the effect of key mutations within and outside the predicted cholesterol-binding site. Our method is illustrated by a complete analysis of cholesterol-binding to Alzheimer's β-amyloid peptide, a protein that penetrates the plasma membrane of brain cells through a cholesterol-dependent process.

DOMMINO 2.0: integrating structurally resolved protein-, RNA-, and DNA-mediated macromolecular interactions

PubMed Central

Kuang, Xingyan; Dhroso, Andi; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2016-01-01

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction’s mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein–protein interactions or protein–DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org PMID:26827237

Towards accurate modeling of noncovalent interactions for protein rigidity analysis.

PubMed

Fox, Naomi; Streinu, Ileana

2013-01-01

Protein rigidity analysis is an efficient computational method for extracting flexibility information from static, X-ray crystallography protein data. Atoms and bonds are modeled as a mechanical structure and analyzed with a fast graph-based algorithm, producing a decomposition of the flexible molecule into interconnected rigid clusters. The result depends critically on noncovalent atomic interactions, primarily on how hydrogen bonds and hydrophobic interactions are computed and modeled. Ongoing research points to the stringent need for benchmarking rigidity analysis software systems, towards the goal of increasing their accuracy and validating their results, either against each other and against biologically relevant (functional) parameters. We propose two new methods for modeling hydrogen bonds and hydrophobic interactions that more accurately reflect a mechanical model, without being computationally more intensive. We evaluate them using a novel scoring method, based on the B-cubed score from the information retrieval literature, which measures how well two cluster decompositions match. To evaluate the modeling accuracy of KINARI, our pebble-game rigidity analysis system, we use a benchmark data set of 20 proteins, each with multiple distinct conformations deposited in the Protein Data Bank. Cluster decompositions for them were previously determined with the RigidFinder method from Gerstein's lab and validated against experimental data. When KINARI's default tuning parameters are used, an improvement of the B-cubed score over a crude baseline is observed in 30% of this data. With our new modeling options, improvements were observed in over 70% of the proteins in this data set. We investigate the sensitivity of the cluster decomposition score with case studies on pyruvate phosphate dikinase and calmodulin. To substantially improve the accuracy of protein rigidity analysis systems, thorough benchmarking must be performed on all current systems and future extensions. We have measured the gain in performance by comparing different modeling methods for noncovalent interactions. We showed that new criteria for modeling hydrogen bonds and hydrophobic interactions can significantly improve the results. The two new methods proposed here have been implemented and made publicly available in the current version of KINARI (v1.3), together with the benchmarking tools, which can be downloaded from our software's website, http://kinari.cs.umass.edu.

Towards accurate modeling of noncovalent interactions for protein rigidity analysis

PubMed Central

2013-01-01

Background Protein rigidity analysis is an efficient computational method for extracting flexibility information from static, X-ray crystallography protein data. Atoms and bonds are modeled as a mechanical structure and analyzed with a fast graph-based algorithm, producing a decomposition of the flexible molecule into interconnected rigid clusters. The result depends critically on noncovalent atomic interactions, primarily on how hydrogen bonds and hydrophobic interactions are computed and modeled. Ongoing research points to the stringent need for benchmarking rigidity analysis software systems, towards the goal of increasing their accuracy and validating their results, either against each other and against biologically relevant (functional) parameters. We propose two new methods for modeling hydrogen bonds and hydrophobic interactions that more accurately reflect a mechanical model, without being computationally more intensive. We evaluate them using a novel scoring method, based on the B-cubed score from the information retrieval literature, which measures how well two cluster decompositions match. Results To evaluate the modeling accuracy of KINARI, our pebble-game rigidity analysis system, we use a benchmark data set of 20 proteins, each with multiple distinct conformations deposited in the Protein Data Bank. Cluster decompositions for them were previously determined with the RigidFinder method from Gerstein's lab and validated against experimental data. When KINARI's default tuning parameters are used, an improvement of the B-cubed score over a crude baseline is observed in 30% of this data. With our new modeling options, improvements were observed in over 70% of the proteins in this data set. We investigate the sensitivity of the cluster decomposition score with case studies on pyruvate phosphate dikinase and calmodulin. Conclusion To substantially improve the accuracy of protein rigidity analysis systems, thorough benchmarking must be performed on all current systems and future extensions. We have measured the gain in performance by comparing different modeling methods for noncovalent interactions. We showed that new criteria for modeling hydrogen bonds and hydrophobic interactions can significantly improve the results. The two new methods proposed here have been implemented and made publicly available in the current version of KINARI (v1.3), together with the benchmarking tools, which can be downloaded from our software's website, http://kinari.cs.umass.edu. PMID:24564209

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.

Insights into positive and negative requirements for protein-protein interactions by crystallographic analysis of the beta-lactamase inhibitory proteins BLIP, BLIP-I, and BLP.

PubMed

Gretes, Michael; Lim, Daniel C; de Castro, Liza; Jensen, Susan E; Kang, Sung Gyun; Lee, Kye Joon; Strynadka, Natalie C J

2009-06-05

Beta-lactamase inhibitory protein (BLIP) binds a variety of beta-lactamase enzymes with wide-ranging specificity. Its binding mechanism and interface interactions are a well-established model system for the characterization of protein-protein interactions. Published studies have examined the binding of BLIP to diverse target beta-lactamases (e.g., TEM-1, SME-1, and SHV-1). However, apart from point mutations of amino acid residues, variability on the inhibitor side of this enzyme-inhibitor interface has remained unexplored. Thus, we present crystal structures of two likely BLIP relatives: (1) BLIP-I (solved alone and in complex with TEM-1), which has beta-lactamase inhibitory activity very similar to that of BLIP; and (2) beta-lactamase-inhibitory-protein-like protein (BLP) (in two apo forms, including an ultra-high-resolution structure), which is unable to inhibit any tested beta-lactamase. Despite categorical differences in species of origin and function, BLIP-I and BLP share nearly identical backbone conformations, even at loop regions differing in BLIP. We describe interacting residues and provide a comparative structural analysis of the interactions formed at the interface of BLIP-I.TEM-1 versus those formed at the interface of BLIP.TEM-1. Along with initial attempts to functionally characterize BLP, we examine its amino acid residues that structurally correspond to BLIP/BLIP-I binding hotspots to explain its inability to bind and inhibit TEM-1. We conclude that the BLIP family fold is a robust and flexible scaffold that permits the formation of high-affinity protein-protein interactions while remaining highly selective. Comparison of the two naturally occurring, distinct binding interfaces built upon this scaffold (BLIP and BLIP-I) shows that there is substantial variation possible in the subnanomolar binding interaction with TEM-1. The corresponding (non-TEM-1-binding) BLP surface shows that numerous favorable backbone-backbone/backbone-side-chain interactions with a protein partner can be negated by the presence of a few, strongly unfavorable interactions, especially electrostatic repulsions.

FEZ2 has acquired additional protein interaction partners relative to FEZ1: functional and evolutionary implications.

PubMed

Alborghetti, Marcos R; Furlan, Ariane S; Kobarg, Jörg

2011-03-08

The FEZ (fasciculation and elongation protein zeta) family designation was purposed by Bloom and Horvitz by genetic analysis of C. elegans unc-76. Similar human sequences were identified in the expressed sequence tag database as FEZ1 and FEZ2. The unc-76 function is necessary for normal axon fasciculation and is required for axon-axon interactions. Indeed, the loss of UNC-76 function results in defects in axonal transport. The human FEZ1 protein has been shown to rescue defects caused by unc-76 mutations in nematodes, indicating that both UNC-76 and FEZ1 are evolutionarily conserved in their function. Until today, little is known about FEZ2 protein function. Using the yeast two-hybrid system we demonstrate here conserved evolutionary features among orthologs and non-conserved features between paralogs of the FEZ family of proteins, by comparing the interactome profiles of the C-terminals of human FEZ1, FEZ2 and UNC-76 from C. elegans. Furthermore, we correlate our data with an analysis of the molecular evolution of the FEZ protein family in the animal kingdom. We found that FEZ2 interacted with 59 proteins and that of these only 40 interacted with FEZ1. Of the 40 FEZ1 interacting proteins, 36 (90%), also interacted with UNC-76 and none of the 19 FEZ2 specific proteins interacted with FEZ1 or UNC-76. This together with the duplication of unc-76 gene in the ancestral line of chordates suggests that FEZ2 is in the process of acquiring new additional functions. The results provide also an explanation for the dramatic difference between C. elegans and D. melanogaster unc-76 mutants on one hand, which cause serious defects in the nervous system, and the mouse FEZ1 -/- knockout mice on the other, which show no morphological and no strong behavioural phenotype. Likely, the ubiquitously expressed FEZ2 can completely compensate the lack of neuronal FEZ1, since it can interact with all FEZ1 interacting proteins and additional 19 proteins.

FEZ2 Has Acquired Additional Protein Interaction Partners Relative to FEZ1: Functional and Evolutionary Implications

PubMed Central

Alborghetti, Marcos R.; Furlan, Ariane S.; Kobarg, Jörg

2011-01-01

Background The FEZ (fasciculation and elongation protein zeta) family designation was purposed by Bloom and Horvitz by genetic analysis of C. elegans unc-76. Similar human sequences were identified in the expressed sequence tag database as FEZ1 and FEZ2. The unc-76 function is necessary for normal axon fasciculation and is required for axon-axon interactions. Indeed, the loss of UNC-76 function results in defects in axonal transport. The human FEZ1 protein has been shown to rescue defects caused by unc-76 mutations in nematodes, indicating that both UNC-76 and FEZ1 are evolutionarily conserved in their function. Until today, little is known about FEZ2 protein function. Methodology/Principal Findings Using the yeast two-hybrid system we demonstrate here conserved evolutionary features among orthologs and non-conserved features between paralogs of the FEZ family of proteins, by comparing the interactome profiles of the C-terminals of human FEZ1, FEZ2 and UNC-76 from C. elegans. Furthermore, we correlate our data with an analysis of the molecular evolution of the FEZ protein family in the animal kingdom. Conclusions/Significance We found that FEZ2 interacted with 59 proteins and that of these only 40 interacted with FEZ1. Of the 40 FEZ1 interacting proteins, 36 (90%), also interacted with UNC-76 and none of the 19 FEZ2 specific proteins interacted with FEZ1 or UNC-76. This together with the duplication of unc-76 gene in the ancestral line of chordates suggests that FEZ2 is in the process of acquiring new additional functions. The results provide also an explanation for the dramatic difference between C. elegans and D. melanogaster unc-76 mutants on one hand, which cause serious defects in the nervous system, and the mouse FEZ1 -/- knockout mice on the other, which show no morphological and no strong behavioural phenotype. Likely, the ubiquitously expressed FEZ2 can completely compensate the lack of neuronal FEZ1, since it can interact with all FEZ1 interacting proteins and additional 19 proteins. PMID:21408165

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.

Observation of CH⋅⋅⋅π Interactions between Methyl and Carbonyl Groups in Proteins.

PubMed

Perras, Frédéric A; Marion, Dominique; Boisbouvier, Jérôme; Bryce, David L; Plevin, Michael J

2017-06-19

Protein structure and function is dependent on myriad noncovalent interactions. Direct detection and characterization of these weak interactions in large biomolecules, such as proteins, is experimentally challenging. Herein, we report the first observation and measurement of long-range "through-space" scalar couplings between methyl and backbone carbonyl groups in proteins. These J couplings are indicative of the presence of noncovalent C-H⋅⋅⋅π hydrogen-bond-like interactions involving the amide π network. Experimentally detected scalar couplings were corroborated by a natural bond orbital analysis, which revealed the orbital nature of the interaction and the origins of the through-space J couplings. The experimental observation of this type of CH⋅⋅⋅π interaction adds a new dimension to the study of protein structure, function, and dynamics by NMR spectroscopy. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

PubMed

Stacey, R Greg; Skinnider, Michael A; Scott, Nichollas E; Foster, Leonard J

2017-10-23

An organism's protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.

Protein source and choice of anticoagulant decisively affect nanoparticle protein corona and cellular uptake

NASA Astrophysics Data System (ADS)

Schöttler, S.; Klein, Katja; Landfester, K.; Mailänder, V.

2016-03-01

Protein adsorption on nanoparticles has been a focus of the field of nanocarrier research in the past few years and more and more papers are dealing with increasingly detailed lists of proteins adsorbed to a plethora of nanocarriers. While there is an urgent need to understand the influence of this protein corona on nanocarriers' interactions with cells the strong impact of the protein source on corona formation and the consequence for interaction with different cell types are factors that are regularly neglected, but should be taken into account for a meaningful analysis. In this study, the importance of the choice of protein source used for in vitro protein corona analysis is concisely investigated. Major and decisive differences in cellular uptake of a polystyrene nanoparticle incubated in fetal bovine serum, human serum, human citrate and heparin plasma are reported. Furthermore, the protein compositions are determined for coronas formed in the respective incubation media. A strong influence of heparin, which is used as an anticoagulant for plasma generation, on cell interaction is demonstrated. While heparin enhances the uptake into macrophages, it prevents internalization into HeLa cells. Taken together we can give the recommendation that human plasma anticoagulated with citrate seems to give the most relevant results for in vitro studies of nanoparticle uptake.Protein adsorption on nanoparticles has been a focus of the field of nanocarrier research in the past few years and more and more papers are dealing with increasingly detailed lists of proteins adsorbed to a plethora of nanocarriers. While there is an urgent need to understand the influence of this protein corona on nanocarriers' interactions with cells the strong impact of the protein source on corona formation and the consequence for interaction with different cell types are factors that are regularly neglected, but should be taken into account for a meaningful analysis. In this study, the importance of the choice of protein source used for in vitro protein corona analysis is concisely investigated. Major and decisive differences in cellular uptake of a polystyrene nanoparticle incubated in fetal bovine serum, human serum, human citrate and heparin plasma are reported. Furthermore, the protein compositions are determined for coronas formed in the respective incubation media. A strong influence of heparin, which is used as an anticoagulant for plasma generation, on cell interaction is demonstrated. While heparin enhances the uptake into macrophages, it prevents internalization into HeLa cells. Taken together we can give the recommendation that human plasma anticoagulated with citrate seems to give the most relevant results for in vitro studies of nanoparticle uptake. Electronic supplementary information (ESI) available: Complete list of proteins identified by LC-MS. See DOI: 10.1039/c5nr08196c

Preparation of Gap Junctions in Membrane Microdomains for Immunoprecipitation and Mass Spectrometry Interactome Analysis.

PubMed

Fowler, Stephanie; Akins, Mark; Bennett, Steffany A L

2016-01-01

Protein interaction networks at gap junction plaques are increasingly implicated in a variety of intracellular signaling cascades. Identifying protein interactions of integral membrane proteins is a valuable tool for determining channel function. However, several technical challenges exist. Subcellular fractionation of the bait protein matrix is usually required to identify less abundant proteins in complex homogenates. Sufficient solvation of the lipid environment without perturbation of the protein interactome must also be achieved. The present chapter describes the flotation of light and heavy liver tissue membrane microdomains to facilitate the identification and analysis of endogenous gap junction proteins and includes technical notes for translation to other integral membrane proteins, tissues, or cell culture models. These procedures are valuable tools for the enrichment of gap junction membrane compartments and for the identification of gap junction signaling interactomes.

InterProSurf: a web server for predicting interacting sites on protein surfaces

PubMed Central

Negi, Surendra S.; Schein, Catherine H.; Oezguen, Numan; Power, Trevor D.; Braun, Werner

2009-01-01

Summary A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. PMID:17933856

Translating Current Bioanalytical Techniques for Studying Corona Activity.

PubMed

Wang, Chunming; Wang, Zhenzhen; Dong, Lei

2018-07-01

The recent discovery of the biological corona is revolutionising our understanding of the in vivo behaviour of nanomaterials. Accurate analysis of corona bioactivity is essential for predicting the fate of nanomaterials and thereby improving nanomedicine design. Nevertheless, current biotechniques for protein analysis are not readily adaptable for analysing corona proteins, given that their conformation, activity, and interaction may largely differ from those of the native proteins. Here, we introduce and propose tailor-made modifications to five types of mainstream bioanalytical methodologies. We specifically illustrate how these modifications can translate existing techniques for protein analysis into competent tools for dissecting the composition, bioactivity, and interaction (with both nanomaterials and the tissue) of corona formed on specific nanomaterial surfaces. Copyright © 2018 Elsevier Ltd. All rights reserved.

Building toy models of proteins using coevolutionary information

NASA Astrophysics Data System (ADS)

Cheng, Ryan; Raghunathan, Mohit; Onuchic, Jose

2015-03-01

Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid positions within the multiple sequence alignment of a protein family. Here, we use Direct Coupling Analysis (DCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family to obtain the sequence-dependent interaction energies of a toy protein model. We demonstrate that this methodology predicts residue-residue interaction energies that are consistent with experimental mutational changes in protein stabilities as well as other computational methodologies. Furthermore, we demonstrate with several examples that DCA could be used to construct a structure-based model that quantitatively agrees with experimental data on folding mechanisms. This work serves as a potential framework for generating models of proteins that are enriched by evolutionary data that can potentially be used to engineer key functional motions and interactions in protein systems. This research has been supported by the NSF INSPIRE award MCB-1241332 and by the CTBP sponsored by the NSF (Grant PHY-1427654).

Human HOX Proteins Use Diverse and Context-Dependent Motifs to Interact with TALE Class Cofactors.

PubMed

Dard, Amélie; Reboulet, Jonathan; Jia, Yunlong; Bleicher, Françoise; Duffraisse, Marilyne; Vanaker, Jean-Marc; Forcet, Christelle; Merabet, Samir

2018-03-13

HOX proteins achieve numerous functions by interacting with the TALE class PBX and MEIS cofactors. In contrast to this established partnership in development and disease, how HOX proteins could interact with PBX and MEIS remains unclear. Here, we present a systematic analysis of HOX/PBX/MEIS interaction properties, scanning all paralog groups with human and mouse HOX proteins in vitro and in live cells. We demonstrate that a previously characterized HOX protein motif known to be critical for HOX-PBX interactions becomes dispensable in the presence of MEIS in all except the two most anterior paralog groups. We further identify paralog-specific TALE-binding sites that are used in a highly context-dependent manner. One of these binding sites is involved in the proliferative activity of HOXA7 in breast cancer cells. Together these findings reveal an extraordinary level of interaction flexibility between HOX proteins and their major class of developmental cofactors. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Integration of cell-free protein coexpression with an enzyme-linked immunosorbent assay enables rapid analysis of protein–protein interactions directly from DNA

PubMed Central

Layton, Curtis J; Hellinga, Homme W

2011-01-01

Assays that integrate detection of binding with cell-free protein expression directly from DNA can dramatically increase the pace at which protein–protein interactions (PPIs) can be analyzed by mutagenesis. In this study, we present a method that combines in vitro protein production with an enzyme-linked immunosorbent assay (ELISA) to measure PPIs. This method uses readily available commodity instrumentation and generic antibody–affinity tag interactions. It is straightforward and rapid to execute, enabling many interactions to be assessed in parallel. In traditional ELISAs, reporter complexes are assembled stepwise with one layer at a time. In the method presented here, all the members of the reporter complex are present and assembled together. The signal strength is dependent on all the intercomponent interaction affinities and concentrations. Although this assay is straightforward to execute, establishing proper conditions and analysis of the results require a thorough understanding of the processes that determine the signal strength. The formation of the fully assembled reporter sandwich can be modeled as a competition between Langmuir adsorption isotherms for the immobilized components and binding equilibria of the solution components. We have shown that modeling this process provides semiquantitative understanding of the effects of affinity and concentration and can guide strategies for the development of experimental protocols. We tested the method experimentally using the interaction between a synthetic ankyrin repeat protein (Off7) and maltose-binding protein. Measurements obtained for a collection of alanine mutations in the interface between these two proteins demonstrate that a range of affinities can be analyzed. PMID:21674663

Stacking and T-shape competition in aromatic-aromatic amino acid interactions.

PubMed

Chelli, Riccardo; Gervasio, Francesco Luigi; Procacci, Piero; Schettino, Vincenzo

2002-05-29

The potential of mean force of interacting aromatic amino acids is calculated using molecular dynamics simulations. The free energy surface is determined in order to study stacking and T-shape competition for phenylalanine-phenylalanine (Phe-Phe), phenylalanine-tyrosine (Phe-Tyr), and tyrosine-tyrosine (Tyr-Tyr) complexes in vacuo, water, carbon tetrachloride, and methanol. Stacked structures are favored in all solvents with the exception of the Tyr-Tyr complex in carbon tetrachloride, where T-shaped structures are also important. The effect of anchoring the two alpha-carbons (C(alpha)) at selected distances is investigated. We find that short and large C(alpha)-C(alpha) distances favor stacked and T-shaped structures, respectively. We analyze a set of 2396 protein structures resolved experimentally. Comparison of theoretical free energies for the complexes to the experimental analogue shows that Tyr-Tyr interaction occurs mainly at the protein surface, while Phe-Tyr and Phe-Phe interactions are more frequent in the hydrophobic protein core. This is confirmed by the Voronoi polyhedron analysis on the database protein structures. As found from the free energy calculation, analysis of the protein database has shown that proximal and distal interacting aromatic residues are predominantly stacked and T-shaped, respectively.

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

Multiple-Localization and Hub Proteins

PubMed Central

Ota, Motonori; Gonja, Hideki; Koike, Ryotaro; Fukuchi, Satoshi

2016-01-01

Protein-protein interactions are fundamental for all biological phenomena, and protein-protein interaction networks provide a global view of the interactions. The hub proteins, with many interaction partners, play vital roles in the networks. We investigated the subcellular localizations of proteins in the human network, and found that the ones localized in multiple subcellular compartments, especially the nucleus/cytoplasm proteins (NCP), the cytoplasm/cell membrane proteins (CMP), and the nucleus/cytoplasm/cell membrane proteins (NCMP), tend to be hubs. Examinations of keywords suggested that among NCP, those related to post-translational modifications and transcription functions are the major contributors to the large number of interactions. These types of proteins are characterized by a multi-domain architecture and intrinsic disorder. A survey of the typical hub proteins with prominent numbers of interaction partners in the type revealed that most are either transcription factors or co-regulators involved in signaling pathways. They translocate from the cytoplasm to the nucleus, triggered by the phosphorylation and/or ubiquitination of intrinsically disordered regions. Among CMP and NCMP, the contributors to the numerous interactions are related to either kinase or ubiquitin ligase activity. Many of them reside on the cytoplasmic side of the cell membrane, and act as the upstream regulators of signaling pathways. Overall, these hub proteins function to transfer external signals to the nucleus, through the cell membrane and the cytoplasm. Our analysis suggests that multiple-localization is a crucial concept to characterize groups of hub proteins and their biological functions in cellular information processing. PMID:27285823

Exploiting the Proteome to Improve the Genome-Wide Genetic Analysis of Epistasis in Common Human Diseases

PubMed Central

Pattin, Kristine A.; Moore, Jason H.

2009-01-01

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene-gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available. PMID:18551320

HyCCAPP as a tool to characterize promoter DNA-protein interactions in Saccharomyces cerevisiae.

PubMed

Guillen-Ahlers, Hector; Rao, Prahlad K; Levenstein, Mark E; Kennedy-Darling, Julia; Perumalla, Danu S; Jadhav, Avinash Y L; Glenn, Jeremy P; Ludwig-Kubinski, Amy; Drigalenko, Eugene; Montoya, Maria J; Göring, Harald H; Anderson, Corianna D; Scalf, Mark; Gildersleeve, Heidi I S; Cole, Regina; Greene, Alexandra M; Oduro, Akua K; Lazarova, Katarina; Cesnik, Anthony J; Barfknecht, Jared; Cirillo, Lisa A; Gasch, Audrey P; Shortreed, Michael R; Smith, Lloyd M; Olivier, Michael

2016-06-01

Currently available methods for interrogating DNA-protein interactions at individual genomic loci have significant limitations, and make it difficult to work with unmodified cells or examine single-copy regions without specific antibodies. In this study, we describe a physiological application of the Hybridization Capture of Chromatin-Associated Proteins for Proteomics (HyCCAPP) methodology we have developed. Both novel and known locus-specific DNA-protein interactions were identified at the ENO2 and GAL1 promoter regions of Saccharomyces cerevisiae, and revealed subgroups of proteins present in significantly different levels at the loci in cells grown on glucose versus galactose as the carbon source. Results were validated using chromatin immunoprecipitation. Overall, our analysis demonstrates that HyCCAPP is an effective and flexible technology that does not require specific antibodies nor prior knowledge of locally occurring DNA-protein interactions and can now be used to identify changes in protein interactions at target regions in the genome in response to physiological challenges. Copyright © 2016 Elsevier Inc. All rights reserved.

An Introductory Classroom Exercise on Protein Molecular Model Visualization and Detailed Analysis of Protein-Ligand Binding

ERIC Educational Resources Information Center

Poeylaut-Palena, Andres, A.; de los Angeles Laborde, Maria

2013-01-01

A learning module for molecular level analysis of protein structure and ligand/drug interaction through the visualization of X-ray diffraction is presented. Using DeepView as molecular model visualization software, students learn about the general concepts of protein structure. This Biochemistry classroom exercise is designed to be carried out by…

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.

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-encoded proteins are attached to the core at more peripheral positions of the networks.

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

PubMed Central

Rodriguez-Rivas, Juan; Marsili, Simone; Juan, David; Valencia, Alfonso

2016-01-01

Protein–protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein–protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein–protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein–protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach. PMID:27965389

Analysis of sDMA modifications of PIWI proteins

PubMed Central

Honda, Shozo; Kirino, Yoriko; Kirino, Yohei

2015-01-01

Summary Arginine methylation is an important post-translational protein modification that modulates protein function for a wide range of biological processes. PIWI proteins, a subclade of the Argonaute family proteins, contain evolutionarily conserved symmetrical dimethylarginines (sDMAs). It has become increasingly apparent that the sDMAs of PIWI proteins serve as binding elements for TUDOR-domain containing proteins and that sDMA-dependent protein interactions play crucial roles in the biogenesis and function of PIWI-interacting RNAs (piRNAs). We describe a method for detecting PIWI sDMAs and purifying PIWI/piRNA complexes using anti-sDMA antibodies. PMID:24178562

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.

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.

Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen

PubMed Central

2011-01-01

Background The genus Flavivirus encompasses more than 50 distinct species of arthropod-borne viruses, including several major human pathogens, such as West Nile virus, yellow fever virus, Japanese encephalitis virus and the four serotypes of dengue viruses (DENV type 1-4). Each year, flaviviruses cause more than 100 million infections worldwide, some of which lead to life-threatening conditions such as encephalitis or haemorrhagic fever. Among the viral proteins, NS3 and NS5 proteins constitute the major enzymatic components of the viral replication complex and are essential to the flavivirus life cycle. Results We report here the results of a high-throughput yeast two-hybrid screen to identify the interactions between human host proteins and the flavivirus NS3 and NS5 proteins. Using our screen results and literature curation, we performed a global analysis of the NS3 and NS5 cellular targets based on functional annotation with the Gene Ontology features. We finally created the first flavivirus NS3 and NS5 proteins interaction network and analysed the topological features of this network. Our proteome mapping screen identified 108 human proteins interacting with NS3 or NS5 proteins or both. The global analysis of the cellular targets revealed the enrichment of host proteins involved in RNA binding, transcription regulation, vesicular transport or innate immune response regulation. Conclusions We proposed that the selective disruption of these newly identified host/virus interactions could represent a novel and attractive therapeutic strategy in treating flavivirus infections. Our virus-host interaction map provides a basis to unravel fundamental processes about flavivirus subversion of the host replication machinery and/or immune defence strategy. PMID:22014111

Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen.

PubMed

Le Breton, Marc; Meyniel-Schicklin, Laurène; Deloire, Alexandre; Coutard, Bruno; Canard, Bruno; de Lamballerie, Xavier; Andre, Patrice; Rabourdin-Combe, Chantal; Lotteau, Vincent; Davoust, Nathalie

2011-10-20

The genus Flavivirus encompasses more than 50 distinct species of arthropod-borne viruses, including several major human pathogens, such as West Nile virus, yellow fever virus, Japanese encephalitis virus and the four serotypes of dengue viruses (DENV type 1-4). Each year, flaviviruses cause more than 100 million infections worldwide, some of which lead to life-threatening conditions such as encephalitis or haemorrhagic fever. Among the viral proteins, NS3 and NS5 proteins constitute the major enzymatic components of the viral replication complex and are essential to the flavivirus life cycle. We report here the results of a high-throughput yeast two-hybrid screen to identify the interactions between human host proteins and the flavivirus NS3 and NS5 proteins. Using our screen results and literature curation, we performed a global analysis of the NS3 and NS5 cellular targets based on functional annotation with the Gene Ontology features. We finally created the first flavivirus NS3 and NS5 proteins interaction network and analysed the topological features of this network. Our proteome mapping screen identified 108 human proteins interacting with NS3 or NS5 proteins or both. The global analysis of the cellular targets revealed the enrichment of host proteins involved in RNA binding, transcription regulation, vesicular transport or innate immune response regulation. We proposed that the selective disruption of these newly identified host/virus interactions could represent a novel and attractive therapeutic strategy in treating flavivirus infections. Our virus-host interaction map provides a basis to unravel fundamental processes about flavivirus subversion of the host replication machinery and/or immune defence strategy.

Polycomb purification by in vivo biotinylation tagging reveals cohesin and Trithorax group proteins as interaction partners

PubMed Central

Strübbe, Gero; Popp, Christian; Schmidt, Alexander; Pauli, Andrea; Ringrose, Leonie; Beisel, Christian; Paro, Renato

2011-01-01

The maintenance of specific gene expression patterns during cellular proliferation is crucial for the identity of every cell type and the development of tissues in multicellular organisms. Such a cellular memory function is conveyed by the complex interplay of the Polycomb and Trithorax groups of proteins (PcG/TrxG). These proteins exert their function at the level of chromatin by establishing and maintaining repressed (PcG) and active (TrxG) chromatin domains. Past studies indicated that a core PcG protein complex is potentially associated with cell type or even cell stage-specific sets of accessory proteins. In order to better understand the dynamic aspects underlying PcG composition and function we have established an inducible version of the biotinylation tagging approach to purify Polycomb and associated factors from Drosophila embryos. This system enabled fast and efficient isolation of Polycomb containing complexes under near physiological conditions, thereby preserving substoichiometric interactions. Novel interacting proteins were identified by highly sensitive mass spectrometric analysis. We found many TrxG related proteins, suggesting a previously unrecognized extent of molecular interaction of the two counteracting chromatin regulatory protein groups. Furthermore, our analysis revealed an association of PcG protein complexes with the cohesin complex and showed that Polycomb-dependent silencing of a transgenic reporter depends on cohesin function. PMID:21415365

Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

PubMed

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

2012-05-01

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.

Interaction between Na-K-ATPase and Bcl-2 proteins BclXL and Bak.

PubMed

Lauf, Peter K; Alqahtani, Tariq; Flues, Karin; Meller, Jaroslaw; Adragna, Norma C

2015-01-01

In silico analysis predicts interaction between Na-K-ATPase (NKA) and Bcl-2 protein canonical BH3- and BH1-like motifs, consistent with NKA inhibition by the benzo-phenanthridine alkaloid chelerythrine, a BH3 mimetic, in fetal human lens epithelial cells (FHLCs) (Lauf PK, Heiny J, Meller J, Lepera MA, Koikov L, Alter GM, Brown TL, Adragna NC. Cell Physiol Biochem 31: 257-276, 2013). This report establishes proof of concept: coimmunoprecipitation and immunocolocalization showed unequivocal and direct physical interaction between NKA and Bcl-2 proteins. Specifically, NKA antibodies (ABs) coimmunoprecipitated BclXL (B-cell lymphoma extra large) and BAK (Bcl-2 antagonist killer) proteins in FHLCs and A549 lung cancer cells. In contrast, both anti-Bcl-2 ABs failed to pull down NKA. Notably, the molecular mass of BAK1 proteins pulled down by NKA and BclXL ABs appeared to be some 4-kDa larger than found in input monomers. In silico analysis predicts these higher molecular mass BAK1 proteins as alternative splicing variants, encoding 42 amino acid (aa) larger proteins than the known 211-aa long canonical BAK1 protein. These BAK1 variants may constitute a pool separate from that forming mitochondrial pores by specifically interacting with NKA and BclXL proteins. We propose a NKA-Bcl-2 protein ternary complex supporting our hypothesis for a special sensor role of NKA in Bcl-2 protein control of cell survival and apoptosis. Copyright © 2015 the American Physiological Society.

Dimerization between aequorea fluorescent proteins does not affect interaction between tagged estrogen receptors in living cells

PubMed Central

Kofoed, Eric M.; Guerbadot, Martin; Schaufele, Fred

2008-01-01

Förster resonance energy transfer (FRET) detection of protein interaction in living cells is commonly measured following the expression of interacting proteins genetically fused to the cyan (CFP) and yellow (YFP) derivatives of the Aequorea victoria fluorescent protein (FP). These FPs can dimerize at mM concentrations, which may introduce artifacts into the measurement of interaction between proteins that are fused with the FPs. Here, FRET analysis of the interaction between estrogen receptors (alpha isoform, ERα) labeled with “wild-type” CFP and YFP is compared with that of ERα labeled with “monomeric” A206K mutants of CFP and YFP. The intracellular equilibrium dissociation constant for the hormone-induced ERα-ERα interaction is similar for ERα labeled with wild-type or monomeric FPs. However, the measurement of energy transfer measured for ERα-ERα interaction in each cell is less consistent with the monomeric FPs. Thus, dimerization of the FPs does not affect the kinetics of ERα-ERα interaction but, when brought close together via ERα-ERα interaction, FP dimerization modestly improves FRET measurement. PMID:18601531

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

PubMed Central

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

2016-01-01

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

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.

Systematic analyses of the ultraviolet radiation resistance-associated gene product (UVRAG) protein interactome by tandem affinity purification.

PubMed

Son, Ji-Hye; Hwang, Eurim C; Kim, Joungmok

2016-03-01

Ultraviolet radiation resistance-associated gene product (UVRAG) was originally identified as a protein involved in cellular responses to UV irradiation. Subsequent studies have demonstrated that UVRAG plays as an important role in autophagy, a lysosome-dependent catabolic program, as a part of a pro-autophagy PIK3C3/VPS34 lipid kinase complex. Several recent studies have shown that UVRAG is also involved in autophagy-independent cellular functions, such as DNA repair/stability and vesicular trafficking/fusion. Here, we examined the UVRAG protein interactome to obtain information about its functional network. To this end, we screened UVRAG-interacting proteins using a tandem affinity purification method coupled with MALDI-TOF/MS analysis. Our results demonstrate that UVRAG interacts with various proteins involved in a wide spectrum of cellular functions, including genome stability, protein translational elongation, protein localization (trafficking), vacuole organization, transmembrane transport as well as autophagy. Notably, the interactome list of high-confidence UVRAG-interacting proteins is enriched for proteins involved in the regulation of genome stability. Our systematic UVRAG interactome analysis should provide important clues for understanding a variety of UVRAG functions.

Pathway redundancy and protein essentiality revealed in the Saccharomyces cerevisiae interaction networks

PubMed Central

Ulitsky, Igor; Shamir, Ron

2007-01-01

The biological interpretation of genetic interactions is a major challenge. Recently, Kelley and Ideker proposed a method to analyze together genetic and physical networks, which explains many of the known genetic interactions as linking different pathways in the physical network. Here, we extend this method and devise novel analytic tools for interpreting genetic interactions in a physical context. Applying these tools on a large-scale Saccharomyces cerevisiae data set, our analysis reveals 140 between-pathway models that explain 3765 genetic interactions, roughly doubling those that were previously explained. Model genes tend to have short mRNA half-lives and many phosphorylation sites, suggesting that their stringent regulation is linked to pathway redundancy. We also identify ‘pivot' proteins that have many physical interactions with both pathways in our models, and show that pivots tend to be essential and highly conserved. Our analysis of models and pivots sheds light on the organization of the cellular machinery as well as on the roles of individual proteins. PMID:17437029

Accumulation of transcription factors and cell signaling-related proteins in the nucleus during citrus-Xanthomonas interaction.

PubMed

Rani, T Swaroopa; Durgeshwar, P; Podile, Appa Rao

2015-07-20

The nucleus is the maestro of the cell and is involved in the modulation of cell signaling during stress. We performed a comprehensive nuclear proteome analysis of Citrus sinensis during interaction with host (Xanthomonas citri pv. citri-Xcc) and non-host (Xanthomonas oryzae pv. oryzae-Xoo) pathogens. The nuclear proteome was obtained using a sequential method of organelle enrichment and determined by nano-LC-MS/MS analysis. A total of 243 proteins accumulated differentially during citrus-Xanthomonas interaction, belonging to 11 functional groups, with signaling and transcription-related proteins dominating. MADS-box transcription factors, DEAD-box RNA helicase and leucine aminopeptidase, mainly involved in jasmonic acid (JA) responses, were in high abundance during non-host interaction (Xoo). Signaling-related proteins like serine/threonine kinase, histones (H3.2, H2A), phosphoglycerate kinase, dynamin, actin and aldolase showed increased accumulation early during Xoo interaction. Our results suggest that there is a possible involvement of JA-triggered defense responses during non-host resistance, with early recognition of the non-host pathogen. Copyright © 2015. Published by Elsevier GmbH.

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

PubMed Central

Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

2015-01-01

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

PARPs database: A LIMS systems for protein-protein interaction data mining or laboratory information management system

PubMed Central

Droit, Arnaud; Hunter, Joanna M; Rouleau, Michèle; Ethier, Chantal; Picard-Cloutier, Aude; Bourgais, David; Poirier, Guy G

2007-01-01

Background In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools. Description We herein describe "PARPs database," a data analysis and management pipeline for liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics. PARPs database is a web-based tool whose features include experiment annotation, protein database searching, protein sequence management, as well as data-mining of the peptides and proteins identified. Conclusion Using this pipeline, we have successfully identified several interactions of biological significance between PARP-1 and other proteins, namely RFC-1, 2, 3, 4 and 5. PMID:18093328

Cloning and characterization of carboxyl terminus of heat shock cognate 70-interacting protein gene from the silkworm, Bombyx mori.

PubMed

Ohsawa, Takeshi; Fujimoto, Shota; Tsunakawa, Akane; Shibano, Yuka; Kawasaki, Hideki; Iwanaga, Masashi

2016-11-01

Carboxyl terminus of heat shock cognate 70-interacting protein (CHIP) is an evolutionarily conserved E3 ubiquitin ligase across different eukaryotic species and is known to play a key role in protein quality control. CHIP has two distinct functional domains, an N-terminal tetratricopeptide repeat (TPR) and a C-terminal U-box domain, which are required for the ubiquitination of numerous labile client proteins that are chaperoned by heat shock proteins (HSPs) and heat shock cognate proteins (HSCs). During our screen for CHIP-like proteins in the Bombyx mori databases, we found a novel silkworm gene, Bombyx mori CHIP. Phylogenetic analysis showed that BmCHIP belongs to Lepidopteran lineages. Quantitative reverse transcription-PCR analysis indicated that BmCHIP was relatively highly expressed in the gonad and fat body. A pull-down experiment and auto-ubiquitination assay showed that BmCHIP interacted with BmHSC70 and had E3 ligase activity. Additionally, immunohistochemical analysis revealed that BmCHIP was partially co-localized with ubiquitin in BmN4 cells. These data support that BmCHIP plays an important role in the ubiquitin proteasome system as an E3 ubiquitin ligase in B. mori. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of Protein-DNA Interaction by Chromatin Immunoprecipitation and DNA Tiling Microarray (ChIP-on-chip).

PubMed

Gao, Hui; Zhao, Chunyan

2018-01-01

Chromatin immunoprecipitation (ChIP) has become the most effective and widely used tool to study the interactions between specific proteins or modified forms of proteins and a genomic DNA region. Combined with genome-wide profiling technologies, such as microarray hybridization (ChIP-on-chip) or massively parallel sequencing (ChIP-seq), ChIP could provide a genome-wide mapping of in vivo protein-DNA interactions in various organisms. Here, we describe a protocol of ChIP-on-chip that uses tiling microarray to obtain a genome-wide profiling of ChIPed DNA.

Inferring protein domains associated with drug side effects based on drug-target interaction network

PubMed Central

2013-01-01

Background Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. Results In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. Conclusion The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains. PMID:24565527

The alpha-fetoprotein (AFP) third domain: a search for AFP interaction sites of cell cycle proteins.

PubMed

Mizejewski, G J

2016-09-01

The carboxy-terminal third domain of alpha-fetoprotein (AFP-3D) is known to harbor binding and/or interaction sites for hydrophobic ligands, receptors, and binding proteins. Such reports have established that AFP-3D consists of amino acid (AA) sequence stretches on the AFP polypeptide that engages in protein-to-protein interactions with various ligands and receptors. Using a computer software program specifically designed for such interactions, the present report identified AA sequence fragments on AFP-3D that could potentially interact with a variety of cell cycle proteins. The cell cycle proteins identified were (1) cyclins, (2) cyclin-dependent kinases, (3) cell cycle-associated proteins (inhibitors, checkpoints, initiators), and (4) ubiquitin ligases. Following detection of the AFP-3D to cell cycle protein interaction sites, the computer-derived AFP localization AA sequences were compared and aligned with previously reported hydrophobic ligand and receptor interaction sites on AFP-3D. A literature survey of the association of cell cycle proteins with AFP showed both positive relationships and correlations. Previous reports of experimental AFP-derived peptides effects on various cell cycle proteins served to confirm and verify the present computer cell cycle protein identifications. Cell cycle protein interactions with AFP-CD peptides have been reported in cultured MCF-7 breast cancer cells subjected to mRNA microarray analysis. After 7 days in culture with MCF-7 cells, the AFP-derived peptides were shown to downregulate cyclin E, SKP2, checkpoint suppressors, cyclin-dependent kinases, and ubiquitin ligases that modulate cyclin E/CdK2 transition from the G1 to the S-phase of the cell cycle. Thus, the experimental data on AFP-CD interaction with cell cycle proteins were consistent with the "in silico" findings.

Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity Mechanisms

DTIC Science & Technology

2015-03-04

were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we...performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can...virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their

Comprehensive identification of proteins binding to RNA G-quadruplex motifs in the 5' UTR of tumor-associated mRNAs.

PubMed

Serikawa, Tatsuo; Spanos, Christos; von Hacht, Annekathrin; Budisa, Nediljko; Rappsilber, Juri; Kurreck, Jens

2018-01-01

G-quadruplex structures in the 5' UTR of mRNAs are widely considered to suppress translation without affecting transcription. The current study describes the comprehensive analysis of proteins binding to four different G-quadruplex motifs located in mRNAs of the cancer-related genes Bcl-2, NRAS, MMP16, and ARPC2. Following metabolic labeling (Stable Isotope Labeling with Amino acids in Cell culture, SILAC) of proteins in the human cell line HEK293, G-quadruplex binding proteins were enriched by pull-down assays and identified by LC-orbitrap mass spectrometry. We found different patterns of interactions for the G-quadruplex motifs under investigation. While the G-quadruplexes in the mRNAs of NRAS and MMP16 specifically interacted with a small number of proteins, the Bcl-2 and ARPC2 G-quadruplexes exhibited a broad range of proteinaceous interaction partners with 99 and 82 candidate proteins identified in at least two replicates, respectively. The use of a control composed of samples from all G-quadruplex-forming sequences and their mutated controls ensured that the identified proteins are specific for RNA G-quadruplex structures and are not general RNA-binding proteins. Independent validation experiments based on pull-down assays and Western blotting confirmed the MS data. Among the interaction partners were many proteins known to bind to RNA, including multiple heterogenous nuclear ribonucleoproteins (hnRNPs). Several of the candidate proteins are likely to reflect stalling of the ribosome by RNA G-quadruplex structures. Interestingly, additional proteins were identified that have not previously been described to interact with RNA. Gene ontology analysis of the candidate proteins revealed that many interaction partners are known to be tumor related. The majority of the identified RNA G-quadruplex interacting proteins are thought to be involved in post-transcriptional processes, particularly in splicing. These findings indicate that protein-G-quadruplex interactions are not only important for the fine-tuning of translation but are also relevant to the regulation of mRNA maturation and may play an important role in tumor biology. Proteomic data are available via ProteomeXchange with identifier PXD005761. Copyright © 2017 Elsevier B.V. and Société Française de Biochimie et Biologie Moléculaire (SFBBM). All rights reserved.

Analysis of DNA interactions using single-molecule force spectroscopy.

PubMed

Ritzefeld, Markus; Walhorn, Volker; Anselmetti, Dario; Sewald, Norbert

2013-06-01

Protein-DNA interactions are involved in many biochemical pathways and determine the fate of the corresponding cell. Qualitative and quantitative investigations on these recognition and binding processes are of key importance for an improved understanding of biochemical processes and also for systems biology. This review article focusses on atomic force microscopy (AFM)-based single-molecule force spectroscopy and its application to the quantification of forces and binding mechanisms that lead to the formation of protein-DNA complexes. AFM and dynamic force spectroscopy are exciting tools that allow for quantitative analysis of biomolecular interactions. Besides an overview on the method and the most important immobilization approaches, the physical basics of the data evaluation is described. Recent applications of AFM-based force spectroscopy to investigate DNA intercalation, complexes involving DNA aptamers and peptide- and protein-DNA interactions are given.

MD simulations of papillomavirus DNA-E2 protein complexes hints at a protein structural code for DNA deformation.

PubMed

Falconi, M; Oteri, F; Eliseo, T; Cicero, D O; Desideri, A

2008-08-01

The structural dynamics of the DNA binding domains of the human papillomavirus strain 16 and the bovine papillomavirus strain 1, complexed with their DNA targets, has been investigated by modeling, molecular dynamics simulations, and nuclear magnetic resonance analysis. The simulations underline different dynamical features of the protein scaffolds and a different mechanical interaction of the two proteins with DNA. The two protein structures, although very similar, show differences in the relative mobility of secondary structure elements. Protein structural analyses, principal component analysis, and geometrical and energetic DNA analyses indicate that the two transcription factors utilize a different strategy in DNA recognition and deformation. Results show that the protein indirect DNA readout is not only addressable to the DNA molecule flexibility but it is finely tuned by the mechanical and dynamical properties of the protein scaffold involved in the interaction.

Molecular characterization and intermolecular interaction of coat protein of Prunus necrotic ringspot virus: implications for virus assembly.

PubMed

Kulshrestha, Saurabh; Hallan, Vipin; Sharma, Anshul; Seth, Chandrika Attri; Chauhan, Anjali; Zaidi, Aijaz Asghar

2013-09-01

Coat protein (CP) and RNA3 from Prunus necrotic ringspot virus (PNRSV-rose), the most prevalent virus infecting rose in India, were characterized and regions in the coat protein important for self-interaction, during dimer formation were identified. The sequence analysis of CP and partial RNA 3 revealed that the rose isolate of PNRSV in India belongs to PV-32 group of PNRSV isolates. Apart from the already established group specific features of PV-32 group member's additional group-specific and host specific features were also identified. Presence of methionine at position 90 in the amino acid sequence alignment of PNRSV CP gene (belonging to PV-32 group) was identified as the specific conserved feature for the rose isolates of PNRSV. As protein-protein interaction plays a vital role in the infection process, an attempt was made to identify the portions of PNRSV CP responsible for self-interaction using yeast two-hybrid system. It was found (after analysis of the deletion clones) that the C-terminal region of PNRSV CP (amino acids 153-226) plays a vital role in this interaction during dimer formation. N-terminal of PNRSV CP is previously known to be involved in CP-RNA interactions, but our results also suggested that N-terminal of PNRSV CP represented by amino acids 1-77 also interacts with C-terminal (amino acids 153-226) in yeast two-hybrid system, suggesting its probable involvement in the CP-CP interaction.

Native Hydrophobic Binding Interactions at the Transition State for Association between the TAZ1 Domain of CBP and the Disordered TAD-STAT2 Are Not a Requirement.

PubMed

Lindström, Ida; Dogan, Jakob

2017-08-15

A significant fraction of the eukaryotic proteome consists of proteins that are either partially or completely disordered under native-like conditions. Intrinsically disordered proteins (IDPs) are common in protein-protein interactions and are involved in numerous cellular processes. Although many proteins have been identified as disordered, much less is known about the binding mechanisms of the coupled binding and folding reactions involving IDPs. Here we have analyzed the rate-limiting transition state for binding between the TAZ1 domain of CREB binding protein and the intrinsically disordered transactivation domain of STAT2 (TAD-STAT2) by site-directed mutagenesis and kinetic experiments (Φ-value analysis) and found that the native protein-protein binding interface is not formed at the transition state for binding. Instead, native hydrophobic binding interactions form late, after the rate-limiting barrier has been crossed. The association rate constant in the absence of electrostatic enhancement was determined to be rather high. This is consistent with the Φ-value analysis, which showed that there are few or no obligatory native contacts. Also, linear free energy relationships clearly demonstrate that native interactions are cooperatively formed, a scenario that has usually been observed for proteins that fold according to the so-called nucleation-condensation mechanism. Thus, native hydrophobic binding interactions at the rate-limiting transition state for association between TAD-STAT2 and TAZ1 are not a requirement, which is generally in agreement with previous findings on other IDP systems and might be a common mechanism for IDPs.

Generation of wavy structure on lipid membrane by peripheral proteins: a linear elastic analysis.

PubMed

Mahata, Paritosh; Das, Sovan Lal

2017-05-01

We carry out a linear elastic analysis to study wavy structure generation on lipid membrane by peripheral membrane proteins. We model the lipid membrane as linearly elastic and anisotropic material. The hydrophobic insertion by proteins into the lipid membrane has been idealized as penetration of rigid rod-like inclusions into the membrane and the electrostatic interaction between protein and membrane has been modeled by a distributed surface traction acting on the membrane surface. With the proposed model we study curvature generation by several binding domains of peripheral membrane proteins containing BAR domains and amphipathic alpha-helices. It is observed that electrostatic interaction is essential for curvature generation by the BAR domains. © 2017 Federation of European Biochemical Societies.

Energetics and Dynamics Across the Bcl-2-Regulated Apoptotic Pathway Reveal Distinct Evolutionary Determinants of Specificity and Affinity.

PubMed

Ivanov, Stefan M; Huber, Roland G; Warwicker, Jim; Bond, Peter J

2016-11-01

Critical regulatory pathways are replete with instances of intra- and interfamily protein-protein interactions due to the pervasiveness of gene duplication throughout evolution. Discerning the specificity determinants within these systems has proven a challenging task. Here, we present an energetic analysis of the specificity determinants within the Bcl-2 family of proteins (key regulators of the intrinsic apoptotic pathway) via a total of ∼20 μs of simulation of 60 distinct protein-protein complexes. We demonstrate where affinity and specificity of protein-protein interactions arise across the family, and corroborate our conclusions with extensive experimental evidence. We identify energy and specificity hotspots that may offer valuable guidance in the design of targeted therapeutics for manipulating the protein-protein interactions within the apoptosis-regulating pathway. Moreover, we propose a conceptual framework that allows us to quantify the relationship between sequence, structure, and binding energetics. This approach may represent a general methodology for investigating other paralogous protein-protein interaction sites. Copyright © 2016 Elsevier Ltd. All rights reserved.

Defining the human deubiquitinating enzyme interaction landscape.

PubMed

Sowa, Mathew E; Bennett, Eric J; Gygi, Steven P; Harper, J Wade

2009-07-23

Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel nonreciprocal proteomic data sets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, subcellular localization, and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway.

Defining the Human Deubiquitinating Enzyme Interaction Landscape

PubMed Central

Sowa, Mathew E.; Bennett, Eric J.; Gygi, Steven P.; Harper, J. Wade

2009-01-01

Summary Deubiquitinating enzymes (Dubs) function to remove covalently attached ubiquitin from proteins, thereby controlling substrate activity and/or abundance. For most Dubs, their functions, targets, and regulation are poorly understood. To systematically investigate Dub function, we initiated a global proteomic analysis of Dubs and their associated protein complexes. This was accomplished through the development of a software platform, called CompPASS, which uses unbiased metrics to assign confidence measurements to interactions from parallel non-reciprocal proteomic datasets. We identified 774 candidate interacting proteins associated with 75 Dubs. Using Gene Ontology, interactome topology classification, sub-cellular localization and functional studies, we link Dubs to diverse processes, including protein turnover, transcription, RNA processing, DNA damage, and endoplasmic reticulum-associated degradation. This work provides the first glimpse into the Dub interaction landscape, places previously unstudied Dubs within putative biological pathways, and identifies previously unknown interactions and protein complexes involved in this increasingly important arm of the ubiquitin-proteasome pathway. PMID:19615732

Studies on interaction of insect repellent compounds with odorant binding receptor proteins by in silico molecular docking approach.

PubMed

Gopal, J Vinay; Kannabiran, K

2013-12-01

The aim of the study was to identify the interactions between insect repellent compounds and target olfactory proteins. Four compounds, camphor (C10H16O), carvacrol (C10H14O), oleic acid (C18H34O2) and firmotox (C22H28O5) were chosen as ligands. Seven olfactory proteins of insects with PDB IDs: 3K1E, 1QWV, 1TUJ, 1OOF, 2ERB, 3R1O and OBP1 were chosen for docking analysis. Patch dock was used and pymol for visualizing the structures. The interactions of these ligands with few odorant binding proteins showed binding energies. The ligand camphor had showed a binding energy of -136 kcal/mol with OBP1 protein. The ligand carvacrol interacted with 1QWV and 1TUJ proteins with a least binding energy of -117.45 kcal/mol and -21.78 kcal/mol respectively. The ligand oleic acid interacted with 1OOF, 2ERB, 3R1O and OBP1 with least binding energies. Ligand firmotox interacted with OBP1 and showed least binding energies. Three ligands (camphor, oleic acid and firmotox) had one, two, three interactions with a single protein OBP1 of Nilaparvatha lugens (Rice pest). From this in silico study we identified the interaction patterns for insect repellent compounds with the target insect odarant proteins. The results of our study revealed that the chosen ligands showed hydrogen bond interactions with the target olfactory receptor proteins.

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.

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 posttranslational modification to LD cluster induction. PMID:27956707

Genistein suppresses adhesion-induced protein tyrosine phosphorylation and invasion of B16-BL6 melanoma cells.

PubMed

Yan, C; Han, R

1998-07-03

Protein tyrosine phosphorylation occurs as one of the earlier events in cancer cell-extracellular matrix (ECM) interaction. With immunoblot analysis and immunofluorescence microscopy, genistein was found to suppress the tyrosine phosphorylation of proteins located at the cell periphery, including a 125 kDa protein, when B16-BL6 melanoma cells attached to and interacted with ECM. When accompanied by the suppression of adhesion-induced protein tyrosine phosphorylation, the invasive potential of B16-BL6 cells through reconstituted basement membrane was decreased significantly. However, neither adhesive capability nor cell growth was significantly affected by genistein. Therefore, the interruption of cancer cell-ECM interaction by suppression of protein tyrosine phosphorylation may contribute to invasion prevention of genistein.

Visualization of protein interaction networks: problems and solutions

PubMed Central

2013-01-01

Background Visualization concerns the representation of data visually and is an important task in scientific research. Protein-protein interactions (PPI) are discovered using either wet lab techniques, such mass spectrometry, or in silico predictions tools, resulting in large collections of interactions stored in specialized databases. The set of all interactions of an organism forms a protein-protein interaction network (PIN) and is an important tool for studying the behaviour of the cell machinery. Since graphic representation of PINs may highlight important substructures, e.g. protein complexes, visualization is more and more used to study the underlying graph structure of PINs. Although graphs are well known data structures, there are different open problems regarding PINs visualization: the high number of nodes and connections, the heterogeneity of nodes (proteins) and edges (interactions), the possibility to annotate proteins and interactions with biological information extracted by ontologies (e.g. Gene Ontology) that enriches the PINs with semantic information, but complicates their visualization. Methods In these last years many software tools for the visualization of PINs have been developed. Initially thought for visualization only, some of them have been successively enriched with new functions for PPI data management and PIN analysis. The paper analyzes the main software tools for PINs visualization considering four main criteria: (i) technology, i.e. availability/license of the software and supported OS (Operating System) platforms; (ii) interoperability, i.e. ability to import/export networks in various formats, ability to export data in a graphic format, extensibility of the system, e.g. through plug-ins; (iii) visualization, i.e. supported layout and rendering algorithms and availability of parallel implementation; (iv) analysis, i.e. availability of network analysis functions, such as clustering or mining of the graph, and the possibility to interact with external databases. Results Currently, many tools are available and it is not easy for the users choosing one of them. Some tools offer sophisticated 2D and 3D network visualization making available many layout algorithms, others tools are more data-oriented and support integration of interaction data coming from different sources and data annotation. Finally, some specialistic tools are dedicated to the analysis of pathways and cellular processes and are oriented toward systems biology studies, where the dynamic aspects of the processes being studied are central. Conclusion A current trend is the deployment of open, extensible visualization tools (e.g. Cytoscape), that may be incrementally enriched by the interactomics community with novel and more powerful functions for PIN analysis, through the development of plug-ins. On the other hand, another emerging trend regards the efficient and parallel implementation of the visualization engine that may provide high interactivity and near real-time response time, as in NAViGaTOR. From a technological point of view, open-source, free and extensible tools, like Cytoscape, guarantee a long term sustainability due to the largeness of the developers and users communities, and provide a great flexibility since new functions are continuously added by the developer community through new plug-ins, but the emerging parallel, often closed-source tools like NAViGaTOR, can offer near real-time response time also in the analysis of very huge PINs. PMID:23368786

Quantifying Additive Interactions of the Osmolyte Proline with Individual Functional Groups of Proteins: Comparisons with Urea and Glycine Betaine, Interpretation of m-Values

PubMed Central

Diehl, Roger C.; Guinn, Emily J.; Capp, Michael W.; Tsodikov, Oleg V.; Record, M. Thomas

2013-01-01

To quantify interactions of the osmolyte L-proline with protein functional groups and predict its effects on protein processes, we use vapor pressure osmometry to determine chemical potential derivatives dµ2/dm3 = µ23 quantifying preferential interactions of proline (component 3) with 21 solutes (component 2) selected to display different combinations of aliphatic or aromatic C, amide, carboxylate, phosphate or hydroxyl O, and/or amide or cationic N surface. Solubility data yield µ23 values for 4 less-soluble solutes. Values of µ23 are dissected using an ASA-based analysis to test the hypothesis of additivity and obtain α-values (proline interaction potentials) for these eight surface types and three inorganic ions. Values of µ23 predicted from these α-values agree with experiment, demonstrating additivity. Molecular interpretation of α-values using the solute partitioning model yields partition coefficients (Kp) quantifying the local accumulation or exclusion of proline in the hydration water of each functional group. Interactions of proline with native protein surface and effects of proline on protein unfolding are predicted from α-values and ASA information and compared with experimental data, with results for glycine betaine and urea, and with predictions from transfer free energy analysis. We conclude that proline stabilizes proteins because of its unfavorable interactions with (exclusion from) amide oxygens and aliphatic hydrocarbon surface exposed in unfolding, and that proline is an effective in vivo osmolyte because of the osmolality increase resulting from its unfavorable interactions with anionic (carboxylate and phosphate) and amide oxygens and aliphatic hydrocarbon groups on the surface of cytoplasmic proteins and nucleic acids. PMID:23909383

Structural and functional analyses of genes encoding VQ proteins in apple.

PubMed

Dong, Qinglong; Zhao, Shuang; Duan, Dingyue; Tian, Yi; Wang, Yanpeng; Mao, Ke; Zhou, Zongshan; Ma, Fengwang

2018-07-01

Recent studies with Arabidopsis and soybean have shown that a class of valine-glutamine (VQ) motif-containing proteins interacts with some WRKY transcription factors. However, little is known about the evolution, structures, and functions of those proteins in apple. Here, we examined their features and identified 49 apple VQ genes. Our evolutional analysis revealed that the proteins could be clustered into nine groups together with their homologues in 33 species. Historically, the main characteristics of proteins in Groups I, V, VI, VII, IX, and X were thought to have been generated before the monocot-dicot split, whereas those in Groups II, III + IV, and VIII were generated after that split. In the structural analysis, apple MdVQ proteins appeared to bind only with Group I and IIc MdWRKY proteins. Meanwhile, MdVQ1, MdVQ10, MdVQ15, and MdVQ36 interacted with multiple MdVQ proteins to form heterodimers but MdVQ15 formed a homodimer. The functional analysis indicated that overexpression of some apple MdVQs in Arabidopsis and tobacco plants effected their vegetative and reproductive growth. These results provide important information about the characteristics of apple MdVQ genes and can serve as a solid foundation for further studies about the role of WRKY-VQ interactions in regulating apple developmental and defense mechanisms. Copyright © 2018 Elsevier B.V. All rights reserved.

Unraveling novel broad-spectrum antibacterial targets in food and waterborne pathogens using comparative genomics and protein interaction network analysis.

PubMed

Jadhav, Ankush; Shanmugham, Buvaneswari; Rajendiran, Anjana; Pan, Archana

2014-10-01

Food and waterborne diseases are a growing concern in terms of human morbidity and mortality worldwide, even in the 21st century, emphasizing the need for new therapeutic interventions for these diseases. The current study aims at prioritizing broad-spectrum antibacterial targets, present in multiple food and waterborne bacterial pathogens, through a comparative genomics strategy coupled with a protein interaction network analysis. The pathways unique and common to all the pathogens under study (viz., methane metabolism, d-alanine metabolism, peptidoglycan biosynthesis, bacterial secretion system, two-component system, C5-branched dibasic acid metabolism), identified by comparative metabolic pathway analysis, were considered for the analysis. The proteins/enzymes involved in these pathways were prioritized following host non-homology analysis, essentiality analysis, gut flora non-homology analysis and protein interaction network analysis. The analyses revealed a set of promising broad-spectrum antibacterial targets, present in multiple food and waterborne pathogens, which are essential for bacterial survival, non-homologous to host and gut flora, and functionally important in the metabolic network. The identified broad-spectrum candidates, namely, integral membrane protein/virulence factor (MviN), preprotein translocase subunits SecB and SecG, carbon storage regulator (CsrA), and nitrogen regulatory protein P-II 1 (GlnB), contributed by the peptidoglycan pathway, bacterial secretion systems and two-component systems, were also found to be present in a wide range of other disease-causing bacteria. Cytoplasmic proteins SecG, CsrA and GlnB were considered as drug targets, while membrane proteins MviN and SecB were classified as vaccine targets. The identified broad-spectrum targets can aid in the design and development of antibacterial agents not only against food and waterborne pathogens but also against other pathogens. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis of the interactome of the Ser/Thr Protein Phosphatase type 1 in Plasmodium falciparum.

PubMed

Hollin, Thomas; De Witte, Caroline; Lenne, Astrid; Pierrot, Christine; Khalife, Jamal

2016-03-17

Protein Phosphatase 1 (PP1) is an enzyme essential to cell viability in the malaria parasite Plasmodium falciparum (Pf). The activity of PP1 is regulated by the binding of regulatory subunits, of which there are up to 200 in humans, but only 3 have been so far reported for the parasite. To better understand the P. falciparum PP1 (PfPP1) regulatory network, we here report the use of three strategies to characterize the PfPP1 interactome: co-affinity purified proteins identified by mass spectrometry, yeast two-hybrid (Y2H) screening and in silico analysis of the P. falciparum predicted proteome. Co-affinity purification followed by MS analysis identified 6 PfPP1 interacting proteins (Pips) of which 3 contained the RVxF consensus binding, 2 with a Fxx[RK]x[RK] motif, also shown to be a PP1 binding motif and one with both binding motifs. The Y2H screens identified 134 proteins of which 30 present the RVxF binding motif and 20 have the Fxx[RK]x[RK] binding motif. The in silico screen of the Pf predicted proteome using a consensus RVxF motif as template revealed the presence of 55 potential Pips. As further demonstration, 35 candidate proteins were validated as PfPP1 interacting proteins in an ELISA-based assay. To the best of our knowledge, this is the first study on PfPP1 interactome. The data reports several conserved PP1 interacting proteins as well as a high number of specific interactors to PfPP1. Their analysis indicates a high diversity of biological functions for PP1 in Plasmodium. Based on the present data and on an earlier study of the Pf interactome, a potential implication of Pips in protein folding/proteolysis, transcription and pathogenicity networks is proposed. The present work provides a starting point for further studies on the structural basis of these interactions and their functions in P. falciparum.

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions.

PubMed

Baltoumas, Fotis A; Theodoropoulou, Margarita C; Hamodrakas, Stavros J

2016-06-01

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions

NASA Astrophysics Data System (ADS)

Baltoumas, Fotis A.; Theodoropoulou, Margarita C.; Hamodrakas, Stavros J.

2016-06-01

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

A Survey of Aspartate Phenylalanine and Glutamate Phenylalanine Interactions in the Protein Data Bank: Searching for Anion Pairs

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

Philip, Vivek M; Harris, Jason B; Adams, Rachel M

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242 8249]. To study the role of anion interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused onmore » identification of Phe Asp or Glu pairs separated by less than 7 in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura Morokuma energy calculations were performed on roughly 19000 benzene formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (2 to 7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion pairs are found throughout protein structures, in helices as well as strands. Numerous pairs also had nearby cation interactions as well as potential stacking. While more than 1000 structures did not contain an anion pair, the 3134 remaining structures contained approximately 2.6 anion pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.« less

A Survey of Aspartate-Phenylalanine and Glutamate-Phenylalanine Interactions in the Protein Data Bank: Searching for Anion-pi Pairs

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

Philip, Vivek M; Harris, Jason B; Adams, Rachel M

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion-{pi} pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion-quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242-8249]. To study the role of anion-{pi} interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification ofmore » Phe-Asp or -Glu pairs separated by less than 7 {angstrom} in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura-Morokuma energy calculations were performed on roughly 19000 benzene-formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (-2 to -7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion-{pi} pairs are found throughout protein structures, in helices as well as {beta} strands. Numerous pairs also had nearby cation-{pi} interactions as well as potential {pi}-{pi} stacking. While more than 1000 structures did not contain an anion-{pi} pair, the 3134 remaining structures contained approximately 2.6 anion-{pi} pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.« less

A survey of aspartate-phenylalanine and glutamate-phenylalanine interactions in the protein data bank: searching for anion-π pairs.

PubMed

Philip, Vivek; Harris, Jason; Adams, Rachel; Nguyen, Don; Spiers, Jeremy; Baudry, Jerome; Howell, Elizabeth E; Hinde, Robert J

2011-04-12

Protein structures are stabilized using noncovalent interactions. In addition to the traditional noncovalent interactions, newer types of interactions are thought to be present in proteins. One such interaction, an anion-π pair, in which the positively charged edge of an aromatic ring interacts with an anion, forming a favorable anion-quadrupole interaction, has been previously proposed [Jackson, M. R., et al. (2007) J. Phys. Chem. B111, 8242-8249]. To study the role of anion-π interactions in stabilizing protein structure, we analyzed pairwise interactions between phenylalanine (Phe) and the anionic amino acids, aspartate (Asp) and glutamate (Glu). Particular emphasis was focused on identification of Phe-Asp or -Glu pairs separated by less than 7 Å in the high-resolution, nonredundant Protein Data Bank. Simplifying Phe to benzene and Asp or Glu to formate molecules facilitated in silico analysis of the pairs. Kitaura-Morokuma energy calculations were performed on roughly 19000 benzene-formate pairs and the resulting energies analyzed as a function of distance and angle. Edgewise interactions typically produced strongly stabilizing interaction energies (-2 to -7.3 kcal/mol), while interactions involving the ring face resulted in weakly stabilizing to repulsive interaction energies. The strongest, most stabilizing interactions were identified as preferentially occurring in buried residues. Anion-π pairs are found throughout protein structures, in helices as well as β strands. Numerous pairs also had nearby cation-π interactions as well as potential π-π stacking. While more than 1000 structures did not contain an anion-π pair, the 3134 remaining structures contained approximately 2.6 anion-π pairs per protein, suggesting it is a reasonably common motif that could contribute to the overall structural stability of a protein.

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.

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

PubMed Central

Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

2015-01-01

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

PubMed

Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

2013-01-01

H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

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.

Mutational analysis of STE5 in the yeast Saccharomyces cerevisiae: Application of a differential interaction trap assay for examining protein-protein interactions

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

Inouye, C.; Dhillon, N.; Durfee, T.

1997-10-01

Ste5 is essential for the yeast mating pheromone response pathway and is thought to function as a scaffold that organized the components of the mitogen-activated protein kinase (MAKP) cascade. A new method was developed to isolate missense mutations in Ste5 that differentially affect the ability of Ste5 to interact with either of two MAPK cascade constituents, the MEKK (Ste11) and the MEK (Ste7). Mutations that affect association with Ste7 or with Ste11 delineate discrete regions of Ste5 that are critical for each interaction. Co-immunoprecipitation analysis, examining the binding in vitro of Ste5 to Ste11, Ste7, Ste4 (G protein {beta} subunit),more » and Fus3 (MAPK), confirmed that each mutation specifically affects the interaction of Ste5 with only one protein. When expressed in a ste5{delta} cell, mutant Ste5 proteins that are defective in their ability to interact with either Ste11 or Ste7 result in a markedly reduced mating proficiency. One mutation that clearly weakened (but did not eliminate) interaction of Ste5 with Ste7 permitted mating at wild-type efficiency, indicating that an efficacious signal is generated even when Ste5 associates with only a small fraction of (or only transiently with) Ste7. Ste5 mutants defective in association with Ste11 or Ste7 showed strong interallelic complementation when co-expressed, suggesting that the functional form of Ste5 in vivo is an oligomer. 69 refs., 6 figs., 3 tabs.« less

Combinatorial multispectral, thermodynamics, docking and site-directed mutagenesis reveal the cognitive characteristics of honey bee chemosensory protein to plant semiochemical.

PubMed

Tan, Jing; Song, Xinmi; Fu, Xiaobin; Wu, Fan; Hu, Fuliang; Li, Hongliang

2018-08-05

In the chemoreceptive system of insects, there are always some soluble binding proteins, such as some antennal-specific chemosensory proteins (CSPs), which are abundantly distributed in the chemosensory sensillar lymph. The antennal-specific CSPs usually have strong capability to bind diverse semiochemicals, while the detailed interaction between CSPs and the semiochemicals remain unclear. Here, by means of the combinatorial multispectral, thermodynamics, docking and site-directed mutagenesis, we detailedly interpreted a binding interaction between a plant semiochemical β-ionone and antennal-specific CSP1 from the worker honey bee. Thermodynamic parameters (ΔH < 0, ΔS > 0) indicate that the interaction is mainly driven by hydrophobic forces and electrostatic interactions. Docking prediction results showed that there are two key amino acids, Phe44 and Gln63, may be involved in the interacting process of CSP1 to β-ionone. In order to confirm the two key amino acids, site-directed mutagenesis were performed and the binding constant (K A ) for two CSP1 mutant proteins was reduced by 60.82% and 46.80% compared to wild-type CSP1. The thermodynamic analysis of mutant proteins furtherly verified that Phe44 maintained an electrostatic interaction and Gln63 contributes hydrophobic and electrostatic forces. Our investigation initially elucidates the physicochemical mechanism of the interaction between antennal-special CSPs in insects including bees to plant semiochemicals, as well as the development of twice thermodynamic analysis (wild type and mutant proteins) combined with multispectral and site-directed mutagenesis methods. Copyright © 2018 Elsevier B.V. All rights reserved.

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

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.

Differential signaling through p190 and p210 BCR-ABL fusion proteins revealed by interactome and phosphoproteome analysis.

PubMed

Cutler, J A; Tahir, R; Sreenivasamurthy, S K; Mitchell, C; Renuse, S; Nirujogi, R S; Patil, A H; Heydarian, M; Wong, X; Wu, X; Huang, T-C; Kim, M-S; Reddy, K L; Pandey, A

2017-07-01

Two major types of leukemogenic BCR-ABL fusion proteins are p190 BCR-ABL and p210 BCR-ABL . Although the two fusion proteins are closely related, they can lead to different clinical outcomes. A thorough understanding of the signaling programs employed by these two fusion proteins is necessary to explain these clinical differences. We took an integrated approach by coupling protein-protein interaction analysis using biotinylation identification with global phosphorylation analysis to investigate the differences in signaling between these two fusion proteins. Our findings suggest that p190 BCR-ABL and p210 BCR-ABL differentially activate important signaling pathways, such as JAK-STAT, and engage with molecules that indicate interaction with different subcellular compartments. In the case of p210 BCR-ABL , we observed an increased engagement of molecules active proximal to the membrane and in the case of p190 BCR-ABL , an engagement of molecules of the cytoskeleton. These differences in signaling could underlie the distinct leukemogenic process induced by these two protein variants.

Bimolecular fluorescence complementation: visualization of molecular interactions in living cells.

PubMed

Kerppola, Tom K

2008-01-01

A variety of experimental methods have been developed for the analysis of protein interactions. The majority of these methods either require disruption of the cells to detect molecular interactions or rely on indirect detection of the protein interaction. The bimolecular fluorescence complementation (BiFC) assay provides a direct approach for the visualization of molecular interactions in living cells and organisms. The BiFC approach is based on the facilitated association between two fragments of a fluorescent protein when the fragments are brought together by an interaction between proteins fused to the fragments. The BiFC approach has been used for visualization of interactions among a variety of structurally diverse interaction partners in many different cell types. It enables detection of transient complexes as well as complexes formed by a subpopulation of the interaction partners. It is essential to include negative controls in each experiment in which the interface between the interaction partners has been mutated or deleted. The BiFC assay has been adapted for simultaneous visualization of multiple protein complexes in the same cell and the competition for shared interaction partners. A ubiquitin-mediated fluorescence complementation assay has also been developed for visualization of the covalent modification of proteins by ubiquitin family peptides. These fluorescence complementation assays have a great potential to illuminate a variety of biological interactions in the future.

Interactions of the α-subunits of heterotrimeric G-proteins with GPCRs, effectors and RGS proteins: a critical review and analysis of interacting surfaces, conformational shifts, structural diversity and electrostatic potentials.

PubMed

Baltoumas, Fotis A; Theodoropoulou, Margarita C; Hamodrakas, Stavros J

2013-06-01

G-protein coupled receptors (GPCRs) are one of the largest families of membrane receptors in eukaryotes. Heterotrimeric G-proteins, composed of α, β and γ subunits, are important molecular switches in the mediation of GPCR signaling. Receptor stimulation after the binding of a suitable ligand leads to G-protein heterotrimer activation and dissociation into the Gα subunit and Gβγ heterodimer. These subunits then interact with a large number of effectors, leading to several cell responses. We studied the interactions between Gα subunits and their binding partners, using information from structural, mutagenesis and Bioinformatics studies, and conducted a series of comparisons of sequence, structure, electrostatic properties and intermolecular energies among different Gα families and subfamilies. We identified a number of Gα surfaces that may, in several occasions, participate in interactions with receptors as well as effectors. The study of Gα interacting surfaces in terms of sequence, structure and electrostatic potential reveals features that may account for the Gα subunit's behavior towards its interacting partners. The electrostatic properties of the Gα subunits, which in some cases differ greatly not only between families but also between subfamilies, as well as the G-protein interacting surfaces of effectors and regulators of G-protein signaling (RGS) suggest that electrostatic complementarity may be an important factor in G-protein interactions. Energy calculations also support this notion. This information may be useful in future studies of G-protein interactions with GPCRs and effectors. Copyright © 2013 Elsevier Inc. All rights reserved.

Network topological analysis reveals the functional cohesiveness for the newly discovered links by Yeast 2 Hybrid approach

NASA Astrophysics Data System (ADS)

Ghiassian, Susan; Pevzner, Sam; Rolland, Thomas; Tassan, Murat; Barabasi, Albert Laszlo; Vidal, Mark; CCNR, Northeastern University Collaboration; Dana Farber Cancer Institute Collaboration

2014-03-01

Protein-protein interaction maps and interactomes are the blueprint of Network Medicine and systems biology and are being experimentally studied by different groups. Despite the wide usage of Literature Curated Interactome (LCI), these sources are biased towards different parameters such as highly studied proteins. Yeast two hybrid method is a high throughput experimental setup which screens proteins in an unbiased fashion. Current knowledge of protein interactions is far from complete. In fact the previous offered data from Y2H method (2005), is estimated to offer only 5% of all potential protein interactions. Currently this coverage has increased to 20% of what is known as reference HI In this work we study the topological properties of Y2H protein-protein interactions network with LCI and show although they both agree on some properties, LCI shows a clear unbiased nature of interaction selections. Most importantly, we assess the properties of PPI as it evolves with increasing the coverage. We show that, the newly discovered interactions tend to connect proteins that have been closer than average in the previous PPI release. reinforcing the modular structure of PPI. Furthermore, we show, some unseen effects on PPI (as opposed to LCI) can be explained by its incompleteness.

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.

Visualisation and graph-theoretic analysis of a large-scale protein structural interactome

PubMed Central

Bolser, Dan; Dafas, Panos; Harrington, Richard; Park, Jong; Schroeder, Michael

2003-01-01

Background Large-scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in the PDB. PSIMAP incorporates both functional and evolutionary information into a single network. Results We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Conclusions Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level. PMID:14531933

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

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

PubMed

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

2008-10-01

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

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.

Determination of cell metabolite VEGF₁₆₅ and dynamic analysis of protein-DNA interactions by combination of microfluidic technique and luminescent switch-on probe.

PubMed

Lin, Xuexia; Leung, Ka-Ho; Lin, Ling; Lin, Luyao; Lin, Sheng; Leung, Chung-Hang; Ma, Dik-Lung; Lin, Jin-Ming

2016-05-15

In this paper, we rationally design a novel G-quadruplex-selective luminescent iridium (III) complex for rapid detection of oligonucleotide and VEGF165 in microfluidics. This new probe is applied as a convenient biosensor for label-free quantitative analysis of VEGF165 protein from cell metabolism, as well as for studying the kinetics of the aptamer-protein interaction combination with a microfluidic platform. As a result, we have successfully established a quantitative analysis of VEGF165 from cell metabolism. Furthermore, based on the principles of hydrodynamic focusing and diffusive mixing, different transient states during kinetics process were monitored and recorded. Thus, the combination of microfluidic technique and G-quadruplex luminescent probe will be potentially applied in the studies of intramolecular interactions and molecule recognition in the future. Copyright © 2015 Elsevier B.V. All rights reserved.

P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

PubMed

Webb-Robertson, Bobbie-Jo M; Bramer, Lisa M; Jensen, Jeffrey L; Kobold, Markus A; Stratton, Kelly G; White, Amanda M; Rodland, Karin D

2017-11-01

P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR . ©2017 American Association for Cancer Research.

KFC Server: interactive forecasting of protein interaction hot spots

PubMed Central

Darnell, Steven J.; LeGault, Laura; Mitchell, Julie C.

2008-01-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model—a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein–protein or protein–DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org. PMID:18539611

Structure-related statistical singularities along protein sequences: a correlation study.

PubMed

Colafranceschi, Mauro; Colosimo, Alfredo; Zbilut, Joseph P; Uversky, Vladimir N; Giuliani, Alessandro

2005-01-01

A data set composed of 1141 proteins representative of all eukaryotic protein sequences in the Swiss-Prot Protein Knowledge base was coded by seven physicochemical properties of amino acid residues. The resulting numerical profiles were submitted to correlation analysis after the application of a linear (simple mean) and a nonlinear (Recurrence Quantification Analysis, RQA) filter. The main RQA variables, Recurrence and Determinism, were subsequently analyzed by Principal Component Analysis. The RQA descriptors showed that (i) within protein sequences is embedded specific information neither present in the codes nor in the amino acid composition and (ii) the most sensitive code for detecting ordered recurrent (deterministic) patterns of residues in protein sequences is the Miyazawa-Jernigan hydrophobicity scale. The most deterministic proteins in terms of autocorrelation properties of primary structures were found (i) to be involved in protein-protein and protein-DNA interactions and (ii) to display a significantly higher proportion of structural disorder with respect to the average data set. A study of the scaling behavior of the average determinism with the setting parameters of RQA (embedding dimension and radius) allows for the identification of patterns of minimal length (six residues) as possible markers of zones specifically prone to inter- and intramolecular interactions.

Label-free quantitative secretome analysis of Xanthomonas oryzae pv. oryzae highlights the involvement of a novel cysteine protease in its pathogenicity.

PubMed

Wang, Yiming; Gupta, Ravi; Song, Wei; Huh, Hyun-Hye; Lee, So Eui; Wu, Jingni; Agrawal, Ganesh Kumar; Rakwal, Randeep; Kang, Kyu Young; Park, Sang-Ryeol; Kim, Sun Tae

2017-10-03

Bacterial blight, caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the most devastating diseases resulting in a huge loss of the total rice productivity. The initial interaction between rice and Xoo takes place in the host apoplast and is mediated primarily by secretion of various proteins from both partners. Yet, such secretory proteins remain to be largely identified and characterized. This study employed a label-free quantitative proteomics approach and identified 404 and 323 Xoo-secreted proteins from in vitro suspension-cultured cells and in planta systems, respectively. Gene Ontology analysis showed their involvement primarily in catalytic, transporter, and ATPase activities. Of a particular interest was a Xoo cysteine protease (XoCP), which showed dramatic increase in its protein abundance in planta upon Xoo interaction with a susceptible rice cultivar. Knock-out mutants of XoCP showed reduced pathogenicity on rice, highlighting its potential involvement in Xoo virulence. Besides, a parallel analysis of in planta rice-secreted proteins resulted in identification of 186 secretory proteins mainly associated with the catalytic, antioxidant, and electron carrier activities. Identified secretory proteins were exploited to shed light on their possible role in the rice-Xoo interaction, and that further deepen our understanding of such interaction. Xanthomonas oryzae pv. oryzae (Xoo), causative agent of bacterial blight disease, results in a huge loss of the total rice productivity. Using a label-free quantitative proteomics approach, we identified 727 Xoo- and 186 rice-secreted proteins. Functional annotation showed Xoo secreted proteins were mainly associated with the catalytic, transporter, and ATPase activities while the rice secreted proteins were mainly associated with the catalytic, antioxidant, and electron carrier activities. A novel Xoo cysteine protease (XoCP) was identified, showing dramatic increase in its protein abundance in planta upon Xoo interaction with a susceptible rice cultivar. Knock-out mutants of XoCP showed reduced pathogenicity on rice, highlighting its potential involvement in Xoo virulence. Copyright © 2017 Elsevier B.V. All rights reserved.

Analyzing Protein Clusters on the Plasma Membrane: Application of Spatial Statistical Analysis Methods on Super-Resolution Microscopy Images.

PubMed

Paparelli, Laura; Corthout, Nikky; Pavie, Benjamin; Annaert, Wim; Munck, Sebastian

2016-01-01

The spatial distribution of proteins within the cell affects their capability to interact with other molecules and directly influences cellular processes and signaling. At the plasma membrane, multiple factors drive protein compartmentalization into specialized functional domains, leading to the formation of clusters in which intermolecule interactions are facilitated. Therefore, quantifying protein distributions is a necessity for understanding their regulation and function. The recent advent of super-resolution microscopy has opened up the possibility of imaging protein distributions at the nanometer scale. In parallel, new spatial analysis methods have been developed to quantify distribution patterns in super-resolution images. In this chapter, we provide an overview of super-resolution microscopy and summarize the factors influencing protein arrangements on the plasma membrane. Finally, we highlight methods for analyzing clusterization of plasma membrane proteins, including examples of their applications.

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.

Molecular analysis of Hsp70 mechanisms in plants and their function in response to stress.

PubMed

Usman, Magaji G; Rafii, Mohd Y; Martini, Mohammad Y; Yusuff, Oladosu A; Ismail, Mohd R; Miah, Gous

2017-04-01

Studying the strategies of improving abiotic stress tolerance is quite imperative and research under this field will increase our understanding of response mechanisms to abiotic stress such as heat. The Hsp70 is an essential regulator of protein having the tendency to maintain internal cell stability like proper folding protein and breakdown of unfolded proteins. Hsp70 holds together protein substrates to help in movement, regulation, and prevent aggregation under physical and or chemical pressure. However, this review reports the molecular mechanism of heat shock protein 70 kDa (Hsp70) action and its structural and functional analysis, research progress on the interaction of Hsp70 with other proteins and their interaction mechanisms as well as the involvement of Hsp70 in abiotic stress responses as an adaptive defense mechanism.

Entire-Dataset Analysis of NMR Fast-Exchange Titration Spectra: A Mg2+ Titration Analysis for HIV-1 Ribonuclease H Domain.

PubMed

Karki, Ichhuk; Christen, Martin T; Spiriti, Justin; Slack, Ryan L; Oda, Masayuki; Kanaori, Kenji; Zuckerman, Daniel M; Ishima, Rieko

2016-12-15

This article communicates our study to elucidate the molecular determinants of weak Mg 2+ interaction with the ribonuclease H (RNH) domain of HIV-1 reverse transcriptase in solution. As the interaction is weak (a ligand-dissociation constant >1 mM), nonspecific Mg 2+ interaction with the protein or interaction of the protein with other solutes that are present in the buffer solution can confound the observed Mg 2+ -titration data. To investigate these indirect effects, we monitored changes in the chemical shifts of backbone amides of RNH by recording NMR 1 H- 15 N heteronuclear single-quantum coherence spectra upon titration of Mg 2+ into an RNH solution. We performed the titration under three different conditions: (1) in the absence of NaCl, (2) in the presence of 50 mM NaCl, and (3) at a constant 160 mM Cl - concentration. Careful analysis of these three sets of titration data, along with molecular dynamics simulation data of RNH with Na + and Cl - ions, demonstrates two characteristic phenomena distinct from the specific Mg 2+ interaction with the active site: (1) weak interaction of Mg 2+ , as a salt, with the substrate-handle region of the protein and (2) overall apparent lower Mg 2+ affinity in the absence of NaCl compared to that in the presence of 50 mM NaCl. A possible explanation may be that the titrated MgCl 2 is consumed as a salt and interacts with RNH in the absence of NaCl. In addition, our data suggest that Na + increases the kinetic rate of the specific Mg 2+ interaction at the active site of RNH. Taken together, our study provides biophysical insight into the mechanism of weak metal interaction on a protein.

Molecular simulations of multimodal ligand-protein binding: elucidation of binding sites and correlation with experiments.

PubMed

Freed, Alexander S; Garde, Shekhar; Cramer, Steven M

2011-11-17

Multimodal chromatography, which employs more than one mode of interaction between ligands and proteins, has been shown to have unique selectivity and high efficacy for protein purification. To test the ability of free solution molecular dynamics (MD) simulations in explicit water to identify binding regions on the protein surface and to shed light on the "pseudo affinity" nature of multimodal interactions, we performed MD simulations of a model protein ubiquitin in aqueous solution of free ligands. Comparisons of MD with NMR spectroscopy of ubiquitin mutants in solutions of free ligands show a good agreement between the two with regard to the preferred binding region on the surface of the protein and several binding sites. MD simulations also identify additional binding sites that were not observed in the NMR experiments. "Bound" ligands were found to be sufficiently flexible and to access a number of favorable conformations, suggesting only a moderate loss of ligand entropy in the "pseudo affinity" binding of these multimodal ligands. Analysis of locations of chemical subunits of the ligand on the protein surface indicated that electrostatic interaction units were located on the periphery of the preferred binding region on the protein. The analysis of the electrostatic potential, the hydrophobicity maps, and the binding of both acetate and benzene probes were used to further study the localization of individual ligand moieties. These results suggest that water-mediated electrostatic interactions help the localization and orientation of the MM ligand to the binding region with additional stability provided by nonspecific hydrophobic interactions.

Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies

PubMed Central

Bueno, Anibal; Morilla, Ian; Diez, Diego; Moya-Garcia, Aurelio A.; Lozano, José; Ranea, Juan A.G.

2016-01-01

RAS proteins are the founding members of the RAS superfamily of GTPases. They are involved in key signaling pathways regulating essential cellular functions such as cell growth and differentiation. As a result, their deregulation by inactivating mutations often results in aberrant cell proliferation and cancer. With the exception of the relatively well-known KRAS, HRAS and NRAS proteins, little is known about how the interactions of the other RAS human paralogs affect cancer evolution and response to treatment. In this study we performed a comprehensive analysis of the relationship between the phylogeny of RAS proteins and their location in the protein interaction network. This analysis was integrated with the structural analysis of conserved positions in available 3D structures of RAS complexes. Our results show that many RAS proteins with divergent sequences are found close together in the human interactome. We found specific conserved amino acid positions in this group that map to the binding sites of RAS with many of their signaling effectors, suggesting that these pairs could share interacting partners. These results underscore the potential relevance of cross-talking in the RAS signaling network, which should be taken into account when considering the inhibitory activity of drugs targeting specific RAS oncoproteins. This study broadens our understanding of the human RAS signaling network and stresses the importance of considering its potential cross-talk in future therapies. PMID:27713118

Systematic analysis of the lysine acetylome in Vibrio parahemolyticus.

PubMed

Pan, Jianyi; Ye, Zhicang; Cheng, Zhongyi; Peng, Xiaojun; Wen, Liangyou; Zhao, Fukun

2014-07-03

Lysine acetylation of proteins is a major post-translational modification that plays an important regulatory role in almost every aspect of cells, both eukaryotes and prokaryotes. Vibrio parahemolyticus, a model marine bacterium, is a worldwide cause of bacterial seafood-borne illness. Here, we conducted the first lysine acetylome in this bacterium through a combination of highly sensitive immune-affinity purification and high-resolution LC-MS/MS. Overall, we identified 1413 lysine acetylation sites in 656 proteins, which account for 13.6% of the total proteins in the cells; this is the highest ratio of acetyl proteins that has so far been identified in bacteria. The bioinformatics analysis of the acetylome showed that the acetylated proteins are involved in a wide range of cellular functions and exhibit diverse subcellular localizations. More specifically, proteins related to protein biosynthesis and carbon metabolism are the preferential targets of lysine acetylation. Moreover, two types of acetylation motifs, a lysine or arginine at the +4/+5 positions and a tyrosine, histidine, or phenylalanine at the +1/+2 positions, were revealed from the analysis of the acetylome. Additionally, protein interaction network analysis demonstrates that a wide range of interactions are modulated by protein acetylation. This study provides a significant beginning for the in-depth exploration of the physiological role of lysine acetylation in V. parahemolyticus.

Predictive and comparative analysis of Ebolavirus proteins

PubMed Central

Cong, Qian; Pei, Jimin; Grishin, Nick V

2015-01-01

Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus. PMID:26158395

Predictive and comparative analysis of Ebolavirus proteins.

PubMed

Cong, Qian; Pei, Jimin; Grishin, Nick V

2015-01-01

Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus.

Analysis of initial changes in the proteins of soybean root tip under flooding stress using gel-free and gel-based proteomic techniques.

PubMed

Yin, Xiaojian; Sakata, Katsumi; Nanjo, Yohei; Komatsu, Setsuko

2014-06-25

Flooding has a severe negative effect on soybean cultivation in the early stages of growth. To obtain a better understanding of the response mechanisms of soybean to flooding stress, initial changes in root tip proteins under flooding were analyzed using two proteomic techniques. Two-day-old soybeans were treated with flooding for 3, 6, 12, and 24h. The weight of soybeans increased during the first 3h of flooding, but root elongation was not observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed by a hierarchical clustering method based on induction levels during the flooding, and the proteins were divided into 5 clusters. Additional interaction analysis of the proteins revealed that ten proteins belonging to cluster I formed the center of a protein interaction network. mRNA expression analysis of these ten proteins showed that citrate lyase and heat shock protein 70 were down-regulated, whereas calreticulin was up-regulated in initial phase of flooding. These results suggest that flooding stress to soybean induces calcium-related signal transduction, which might play important roles in the early responses to flooding. Flooding has a severe negative effect on soybean cultivation, particularly in the early stages of growth. To better understand the response mechanisms of soybean to the early stages of flooding stress, two proteomic techniques were used. Two-day-old soybeans were treated without or with flooding for 3, 6, 12, and 24h. The fresh weight of soybeans increased during the first 3h of flooding stress, but the growth then slowed and no root elongation was observed. Using gel-based and gel-free proteomic techniques, 115 proteins were identified in root tips, of which 9 proteins were commonly detected by both methods. The 71 proteins identified by the gel-free proteomics were analyzed by a hierarchical clustering method based on induction levels during the flooding stress, and 5 protein clusters were recognized. Protein interaction analysis revealed that ten proteins belonging to cluster I formed the center of a protein interaction network. mRNA expression analysis of these ten proteins showed that citrate lyase and heat shock protein 70 were down-regulated in response to flooding stress, whereas calreticulin was up-regulated. These results suggest that flooding stress to soybean induces calcium-related signal transduction, which might play important roles in the early responses to flooding. Copyright © 2014 Elsevier B.V. All rights reserved.

Docking analysis of verteporfin with YAP WW domain.

PubMed

Kandoussi, Ilham; Lakhlili, Wiame; Taoufik, Jamal; Ibrahimi, Azeddine

2017-01-01

The YAP oncogene is a known cancer target. Therefore, it is of interest to understand the molecular docking interaction of verteporfin (a derivative of benzo-porphyrin) with the WW domain of YAP (clinically used for photo-dynamic therapy in macular degeneration) as a potential WW domain-ligand modulator by inhibition. A homology protein SWISS MODEL of the human YAP protein was constructed to dock (using AutoDock vina) with the PubChem verteporfin structure for interaction analysis. The docking result shows the possibilities of verteporfin interaction with the oncogenic transcription cofactor YAP having WW1 and WW2 domains. Thus, the ability of verteporfin to bind with the YAP WW domain having modulator activity is implied in this analysis.

Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites.

PubMed

Marsh, Lorraine

2015-01-01

Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where "nonspecific" interactions contribute to biological function.

The modular architecture of protein-protein binding interfaces.

PubMed

Reichmann, D; Rahat, O; Albeck, S; Meged, R; Dym, O; Schreiber, G

2005-01-04

Protein-protein interactions are essential for life. Yet, our understanding of the general principles governing binding is not complete. In the present study, we show that the interface between proteins is built in a modular fashion; each module is comprised of a number of closely interacting residues, with few interactions between the modules. The boundaries between modules are defined by clustering the contact map of the interface. We show that mutations in one module do not affect residues located in a neighboring module. As a result, the structural and energetic consequences of the deletion of entire modules are surprisingly small. To the contrary, within their module, mutations cause complex energetic and structural consequences. Experimentally, this phenomenon is shown on the interaction between TEM1-beta-lactamase and beta-lactamase inhibitor protein (BLIP) by using multiple-mutant analysis and x-ray crystallography. Replacing an entire module of five interface residues with Ala created a large cavity in the interface, with no effect on the detailed structure of the remaining interface. The modular architecture of binding sites, which resembles human engineering design, greatly simplifies the design of new protein interactions and provides a feasible view of how these interactions evolved.

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

PubMed

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

2015-01-01

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

Low-temperature protein dynamics: a simulation analysis of interprotein vibrations and the boson peak at 150 k.

PubMed

Kurkal-Siebert, Vandana; Smith, Jeremy C

2006-02-22

An understanding of low-frequency, collective protein dynamics at low temperatures can furnish valuable information on functional protein energy landscapes, on the origins of the protein glass transition and on protein-protein interactions. Here, molecular dynamics (MD) simulations and normal-mode analyses are performed on various models of crystalline myoglobin in order to characterize intra- and interprotein vibrations at 150 K. Principal component analysis of the MD trajectories indicates that the Boson peak, a broad peak in the dynamic structure factor centered at about approximately 2-2.5 meV, originates from approximately 10(2) collective, harmonic vibrations. An accurate description of the environment is found to be essential in reproducing the experimental Boson peak form and position. At lower energies other strong peaks are found in the calculated dynamic structure factor. Characterization of these peaks shows that they arise from harmonic vibrations of proteins relative to each other. These vibrations are likely to furnish valuable information on the physical nature of protein-protein interactions.

Comparative salivary proteomics analysis of children with and without dental caries using the iTRAQ/MRM approach.

PubMed

Wang, Kun; Wang, Yufei; Wang, Xiuqing; Ren, Qian; Han, Sili; Ding, Longjiang; Li, Zhongcheng; Zhou, Xuedong; Li, Wei; Zhang, Linglin

2018-01-19

Dental caries is a major worldwide oral disease afflicting a large proportion of children. As an important host factor of caries susceptibility, saliva plays a significant role in the occurrence and development of caries. The aim of the present study was to characterize the healthy and cariogenic salivary proteome and determine the changes in salivary protein expression of children with varying degrees of active caries, also to establish salivary proteome profiles with a potential therapeutic use against dental caries. In this study, unstimulated saliva samples were collected from 30 children (age 10-12 years) with no dental caries (NDC, n = 10), low dental caries (LDC, n = 10), and high dental caries (HDC, n = 10). Salivary proteins were extracted, reduced, alkylated, trypsin digested and labeled with isobaric tags for relative and absolute quantitation, and then they were analyzed with GO annotation, biological pathway analysis, hierarchical clustering analysis, and protein-protein interaction analysis. Targeted verifications were then performed using multiple reaction monitoring mass spectrometry. A total of 244 differentially expressed proteins annotated with GO annotation in biological processes, cellular component and molecular function were identified in comparisons among children with varying degrees of active caries. A number of caries-related proteins as well as pathways were identified in this study. As compared with caries-free children, the most significantly enriched pathways involved by the up-regulated proteins in LDC and HDC were the ubiquitin mediated proteolysis pathway and African trypanosomiasis pathway, respectively. Subsequently, we selected 53 target proteins with differential expression in different comparisons, including mucin 7, mucin 5B, histatin 1, cystatin S and cystatin SN, basic salivary proline rich protein 2, for further verification using MRM assays. Protein-protein interaction analysis of these proteins revealed complex protein interaction networks, indicating synergistic action of salivary proteins in caries resistance or cariogenicity. Overall, our results afford new insight into the salivary proteome of children with dental caries. These findings might have bright prospect in future in developing novel biomimetic peptides with preventive and therapeutic benefits for childhood caries.

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

Amyloidogenic Regions and Interaction Surfaces Overlap in Globular Proteins Related to Conformational Diseases

PubMed Central

Castillo, Virginia; Ventura, Salvador

2009-01-01

Protein aggregation underlies a wide range of human disorders. The polypeptides involved in these pathologies might be intrinsically unstructured or display a defined 3D-structure. Little is known about how globular proteins aggregate into toxic assemblies under physiological conditions, where they display an initially folded conformation. Protein aggregation is, however, always initiated by the establishment of anomalous protein-protein interactions. Therefore, in the present work, we have explored the extent to which protein interaction surfaces and aggregation-prone regions overlap in globular proteins associated with conformational diseases. Computational analysis of the native complexes formed by these proteins shows that aggregation-prone regions do frequently overlap with protein interfaces. The spatial coincidence of interaction sites and aggregating regions suggests that the formation of functional complexes and the aggregation of their individual subunits might compete in the cell. Accordingly, single mutations affecting complex interface or stability usually result in the formation of toxic aggregates. It is suggested that the stabilization of existing interfaces in multimeric proteins or the formation of new complexes in monomeric polypeptides might become effective strategies to prevent disease-linked aggregation of globular proteins. PMID:19696882

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

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

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

Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data

PubMed Central

Fujimori, Shigeo; Hirai, Naoya; Ohashi, Hiroyuki; Masuoka, Kazuyo; Nishikimi, Akihiko; Fukui, Yoshinori; Washio, Takanori; Oshikubo, Tomohiro; Yamashita, Tatsuhiro; Miyamoto-Sato, Etsuko

2012-01-01

Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. The completely cell-free method gives a high-throughput and a large detection space, testing the interactions without using clones. The quantitative information provided by NGS reduces the number of false positives. The method is suitable for the in vitro detection of proteins that interact not only with the bait protein, but also with DNA, RNA and chemical compounds. Thus, it could become a universal approach for exploring the large space of protein sequences and interactome networks. PMID:23056904

Cross-species Virus-host Protein-Protein Interactions Inhibiting Innate Immunity

DTIC Science & Technology

2016-07-01

Distribution A: Approved for public release; distribution is unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The single- stranded negative sense RNA...focused upon members of three negative-sense single- stranded RNA (ssRNA(-)) virus families with know or suspected histories of changes in host-species...however, the N and C-termini are disordered extended strands . In contrast, our covariance analysis mapped hotspots for protein interaction to the

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.

Peptide Array X-Linking (PAX): A New Peptide-Protein Identification Approach

PubMed Central

Okada, Hirokazu; Uezu, Akiyoshi; Soderblom, Erik J.; Moseley, M. Arthur; Gertler, Frank B.; Soderling, Scott H.

2012-01-01

Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery. PMID:22606326

Proteomics of xenografted human breast cancer indicates novel targets related to tamoxifen resistance.

PubMed

Besada, Vladimir; Diaz, Maylin; Becker, Michael; Ramos, Yassel; Castellanos-Serra, Lila; Fichtner, Iduna

2006-02-01

Tamoxifen is the most frequently used drug for hormone therapy of breast cancer patients, even though a high percentage of women are (or become) refractory to this treatment. The proteins involved in tamoxifen resistance of breast tumor cells as well as the mechanisms by which they interact, are still unknown. Some years ago, we established the xenograft breast tumor 3366, sensitive to tamoxifen and the 3366/TAM, resistant to tamoxifen, derived after two years of in vivo passages of the parental 3366 under tamoxifen treatment. Here, we compare the protein expression levels of both xenografts. 2-DE of proteins from total cell extracts showed very high reproducibility among tumors from each group (tamoxifen sensitive and tamoxifen resistant). The heuristic clustering analysis of these gels pooled them correctly in both groups. Twelve proteins were found up-regulated in the tamoxifen-resistant line, while nine were down-regulated. The proteins differentially expressed were identified by MS and sequence database analysis. Biological functions of these proteins are related to cell-cell adhesion and interaction, signal transduction, DNA and protein synthesis machinery, mitochondrial respiratory chain, oxidative stress processes and apoptosis. Three of the identified proteins (ALG-2 interacting protein and two GDP-dissociation inhibitors) could be directly involved in the resistance phenomenon.

Mass Spectrometry Based Proteomic Analysis of Salivary Glands of Urban Malaria Vector Anopheles stephensi

PubMed Central

Vijay, Sonam

2014-01-01

Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE), ion trap liquid chromatography mass spectrometry (LC/MS/MS), and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes. PMID:25126571

Mass spectrometry based proteomic analysis of salivary glands of urban malaria vector Anopheles stephensi.

PubMed

Vijay, Sonam; Rawat, Manmeet; Sharma, Arun

2014-01-01

Salivary gland proteins of Anopheles mosquitoes offer attractive targets to understand interactions with sporozoites, blood feeding behavior, homeostasis, and immunological evaluation of malaria vectors and parasite interactions. To date limited studies have been carried out to elucidate salivary proteins of An. stephensi salivary glands. The aim of the present study was to provide detailed analytical attributives of functional salivary gland proteins of urban malaria vector An. stephensi. A proteomic approach combining one-dimensional electrophoresis (1DE), ion trap liquid chromatography mass spectrometry (LC/MS/MS), and computational bioinformatic analysis was adopted to provide the first direct insight into identification and functional characterization of known salivary proteins and novel salivary proteins of An. stephensi. Computational studies by online servers, namely, MASCOT and OMSSA algorithms, identified a total of 36 known salivary proteins and 123 novel proteins analysed by LC/MS/MS. This first report describes a baseline proteomic catalogue of 159 salivary proteins belonging to various categories of signal transduction, regulation of blood coagulation cascade, and various immune and energy pathways of An. stephensi sialotranscriptome by mass spectrometry. Our results may serve as basis to provide a putative functional role of proteins in concept of blood feeding, biting behavior, and other aspects of vector-parasite host interactions for parasite development in anopheline mosquitoes.

How much do we know about the coupling of G-proteins to serotonin receptors?

PubMed Central

2014-01-01

Serotonin receptors are G-protein-coupled receptors (GPCRs) involved in a variety of psychiatric disorders. G-proteins, heterotrimeric complexes that couple to multiple receptors, are activated when their receptor is bound by the appropriate ligand. Activation triggers a cascade of further signalling events that ultimately result in cell function changes. Each of the several known G-protein types can activate multiple pathways. Interestingly, since several G-proteins can couple to the same serotonin receptor type, receptor activation can result in induction of different pathways. To reach a better understanding of the role, interactions and expression of G-proteins a literature search was performed in order to list all the known heterotrimeric combinations and serotonin receptor complexes. Public databases were analysed to collect transcript and protein expression data relating to G-proteins in neural tissues. Only a very small number of heterotrimeric combinations and G-protein-receptor complexes out of the possible thousands suggested by expression data analysis have been examined experimentally. In addition this has mostly been obtained using insect, hamster, rat and, to a lesser extent, human cell lines. Besides highlighting which interactions have not been explored, our findings suggest additional possible interactions that should be examined based on our expression data analysis. PMID:25011628

How much do we know about the coupling of G-proteins to serotonin receptors?

PubMed

Giulietti, Matteo; Vivenzio, Viviana; Piva, Francesco; Principato, Giovanni; Bellantuono, Cesario; Nardi, Bernardo

2014-07-10

Serotonin receptors are G-protein-coupled receptors (GPCRs) involved in a variety of psychiatric disorders. G-proteins, heterotrimeric complexes that couple to multiple receptors, are activated when their receptor is bound by the appropriate ligand. Activation triggers a cascade of further signalling events that ultimately result in cell function changes. Each of the several known G-protein types can activate multiple pathways. Interestingly, since several G-proteins can couple to the same serotonin receptor type, receptor activation can result in induction of different pathways. To reach a better understanding of the role, interactions and expression of G-proteins a literature search was performed in order to list all the known heterotrimeric combinations and serotonin receptor complexes. Public databases were analysed to collect transcript and protein expression data relating to G-proteins in neural tissues. Only a very small number of heterotrimeric combinations and G-protein-receptor complexes out of the possible thousands suggested by expression data analysis have been examined experimentally. In addition this has mostly been obtained using insect, hamster, rat and, to a lesser extent, human cell lines. Besides highlighting which interactions have not been explored, our findings suggest additional possible interactions that should be examined based on our expression data analysis.

Identification of new protein interactions between dengue fever virus and its hosts, human and mosquito.

PubMed

Mairiang, Dumrong; Zhang, Huamei; Sodja, Ann; Murali, Thilakam; Suriyaphol, Prapat; Malasit, Prida; Limjindaporn, Thawornchai; Finley, Russell L

2013-01-01

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host - dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.

Identification of New Protein Interactions between Dengue Fever Virus and Its Hosts, Human and Mosquito

PubMed Central

Mairiang, Dumrong; Zhang, Huamei; Sodja, Ann; Murali, Thilakam; Suriyaphol, Prapat; Malasit, Prida; Limjindaporn, Thawornchai; Finley, Russell L.

2013-01-01

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host – dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies. PMID:23326450

[POSSIBILITIES OF APPLICATION OF MALDI-TOF MASS-SPECTROMETRY FOR STUDY OF CARBOHYDRATE-SPECIFIC RECEPTORS FOR DIAGNOSTIC BACTERIOPHAGE EL TOR].

PubMed

Telesmanich, N R; Goncharenko, E V; Chaika, S O; Chaika, I A; Telicheva, V O

2016-01-01

Study mechanisms of interaction of diagnostic bacteriophage El Tor with sensitive strain Vibrio cholerae El Tor 18507 using direct protein profiling, identification of constant and variable proteins, taking part in interaction of the phage and cell, as well as carbohydrate-specific phage receptors. . A commercial preparation of cholera diagnostic bacteriophage El Tor, strain V. cholerae El Tor 18507 were used. Effect of carbohydrates on bacteriophage activity was determined in experiments with phage by a classic and modified by us method. Protein profiles of the studied objects were studied using MSP-analysis method. Sucrose was shown to inhibit lytic activity of bacteriophage. Proteome profiles of El Tor bacteriophage and sensitive indicator strains were studied, identification of constant and variable proteins of the studied objects by MSP Peak-list program was carried out. Analysis of changes of profiles of phage and microbial cell during interaction with sucrose gave a basis for assuming, that sucrose in the mixture of culture-phage enters interaction namely with phage protein receptors, blocking receptors specific for cholera vibrio, that subsequently manifests in a sharp decrease of phage activity against the sensitive strain.

Cooperative interactions of LPPR family members in membrane localization and alteration of cellular morphology

PubMed Central

Yu, Panpan; Agbaegbu, Chinyere; Malide, Daniela A.; Wu, Xufeng; Katagiri, Yasuhiro; Hammer, John A.; Geller, Herbert M.

2015-01-01

ABSTRACT The lipid phosphate phosphatase-related proteins (LPPRs), also known as plasticity-related genes (PRGs), are classified as a new brain-enriched subclass of the lipid phosphate phosphatase (LPP) superfamily. They induce membrane protrusions, neurite outgrowth or dendritic spine formation in cell lines and primary neurons. However, the exact roles of LPPRs and the mechanisms underlying their effects are not certain. Here, we present the results of a large-scale proteome analysis to determine LPPR1-interacting proteins using co-immunoprecipitation coupled to mass spectrometry. We identified putative LPPR1-binding proteins involved in various biological processes. Most interestingly, we identified the interaction of LPPR1 with its family member LPPR3, LPPR4 and LPPR5. Their interactions were characterized by co-immunoprecipitation and colocalization analysis using confocal and super-resolution microscopy. Moreover, co-expressing two LPPR members mutually elevated their protein levels, facilitated their plasma membrane localization and resulted in an increased induction of membrane protrusions as well as the phosphorylation of S6 ribosomal protein. Taken together, we revealed a new functional cooperation between LPPR family members and discovered for the first time that LPPRs likely exert their function through forming complex with its family members. PMID:26183180

Proteomic profiling of epileptogenesis in a rat model: Focus on cell stress, extracellular matrix and angiogenesis.

PubMed

Keck, Michael; van Dijk, Roelof Maarten; Deeg, Cornelia A; Kistler, Katharina; Walker, Andreas; von Rüden, Eva-Lotta; Russmann, Vera; Hauck, Stefanie M; Potschka, Heidrun

2018-04-01

Information about epileptogenesis-associated changes in protein expression patterns is of particular interest for future selection of target and biomarker candidates. Bioinformatic analysis of proteomic data sets can increase our knowledge about molecular alterations characterizing the different phases of epilepsy development following an initial epileptogenic insult. Here, we report findings from a focused analysis of proteomic data obtained for the hippocampus and parahippocampal cortex samples collected during the early post-insult phase, latency phase, and chronic phase of a rat model of epileptogenesis. The study focused on proteins functionally associated with cell stress, cell death, extracellular matrix (ECM) remodeling, cell-ECM interaction, cell-cell interaction, angiogenesis, and blood-brain barrier function. The analysis revealed prominent pathway enrichment providing information about the complex expression alterations of the respective protein groups. In the hippocampus, the number of differentially expressed proteins declined over time during the course of epileptogenesis. In contrast, a peak in the regulation of proteins linked with cell stress and death as well as ECM and cell-cell interaction became evident at later phases during epileptogenesis in the parahippocampal cortex. The data sets provide valuable information about the time course of protein expression patterns during epileptogenesis for a series of proteins. Moreover, the findings provide comprehensive novel information about expression alterations of proteins that have not been discussed yet in the context of epileptogenesis. These for instance include different members of the lamin protein family as well as the fermitin family member 2 (FERMT2). Induction of FERMT2 and other selected proteins, CD18 (ITGB2), CD44 and Nucleolin were confirmed by immunohistochemistry. Taken together, focused bioinformatic analysis of the proteomic data sets completes our knowledge about molecular alterations linked with cell death and cellular plasticity during epileptogenesis. The analysis provided can guide future selection of target and biomarker candidates. Copyright © 2018 Elsevier Inc. All rights reserved.

Interactions of glycine betaine with proteins: insights from volume and compressibility measurements.

PubMed

Shek, Yuen Lai; Chalikian, Tigran V

2013-01-29

We report the first application of volume and compressibility measurements to characterization of interactions between cosolvents (osmolytes) and globular proteins. Specifically, we measure the partial molar volumes and adiabatic compressibilities of cytochrome c, ribonuclease A, lysozyme, and ovalbumin in aqueous solutions of the stabilizing osmolyte glycine betaine (GB) at concentrations between 0 and 4 M. The fact that globular proteins do not undergo any conformational transitions in the presence of GB provides an opportunity to study the interactions of GB with proteins in their native states within the entire range of experimentally accessible GB concentrations. We analyze our resulting volumetric data within the framework of a statistical thermodynamic model in which each instance of GB interaction with a protein is viewed as a binding reaction that is accompanied by release of four water molecules. From this analysis, we calculate the association constants, k, as well as changes in volume, ΔV(0), and adiabatic compressibility, ΔK(S0), accompanying each GB-protein association event in an ideal solution. By comparing these parameters with similar characteristics determined for low-molecular weight analogues of proteins, we conclude that there are no significant cooperative effects involved in interactions of GB with any of the proteins studied in this work. We also evaluate the free energies of direct GB-protein interactions. The energetic properties of GB-protein association appear to scale with the size of the protein. For all proteins, the highly favorable change in free energy associated with direct protein-cosolvent interactions is nearly compensated by an unfavorable free energy of cavity formation (excluded volume effect), yielding a modestly unfavorable free energy for the transfer of a protein from water to a GB/water mixture.

Rapid and sensitive MRM-based mass spectrometry approach for systematically exploring ganglioside-protein interactions.

PubMed

Tian, Ruijun; Jin, Jing; Taylor, Lorne; Larsen, Brett; Quaggin, Susan E; Pawson, Tony

2013-04-01

Gangliosides are ubiquitous components of cell membranes. Their interactions with bacterial toxins and membrane-associated proteins (e.g. receptor tyrosine kinases) have important roles in the regulation of multiple cellular functions. Currently, an effective approach for measuring ganglioside-protein interactions especially in a large-scale fashion is largely missing. To this end, we report a facile MS-based approach to explore gangliosides extracted from cells and measure their interactions with protein of interest globally. We optimized a two-step protocol for extracting total gangliosides from cells within 2 h. Easy-to-use magnetic beads conjugated with a protein of interest were used to capture interacting gangliosides. To measure ganglioside-protein interaction on a global scale, we applied a high-sensitive LC-MS system, containing hydrophilic interaction LC separation and multiple reaction monitoring-based MS for ganglioside detection. Sensitivity for ganglioside GM1 is below 100 pg, and the whole analysis can be done in 20 min with isocratic elution. To measure ganglioside interactions with soluble vascular endothelial growth factor receptor 1 (sFlt1), we extracted and readily detected 36 species of gangliosides from perivascular retinal pigment epithelium cells across eight different classes. Twenty-three ganglioside species have significant interactions with sFlt1 as compared with IgG control based on p value cutoff <0.05. These results show that the described method provides a rapid and high-sensitive approach for systematically measuring ganglioside-protein interactions. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A proteomic analysis reveals the interaction of GluK1 ionotropic kainate receptor subunits with Go proteins.

PubMed

Rutkowska-Wlodarczyk, Izabela; Aller, M Isabel; Valbuena, Sergio; Bologna, Jean-Charles; Prézeau, Laurent; Lerma, Juan

2015-04-01

Kainate receptors (KARs) are found ubiquitously in the CNS and are present presynaptically and postsynaptically regulating synaptic transmission and excitability. Functional studies have proven that KARs act as ion channels as well as potentially activating G-proteins, thus indicating the existance of a dual signaling system for KARs. Nevertheless, it is not clear how these ion channels activate G-proteins and which of the KAR subunits is involved. Here we performed a proteomic analysis to define proteins that interact with the C-terminal domain of GluK1 and we identified a variety of proteins with many different functions, including a Go α subunit. These interactions were verified through distinct in vitro and in vivo assays, and the activation of the Go protein by GluK1 was validated in bioluminescence resonance energy transfer experiments, while the specificity of this association was confirmed in GluK1-deficient mice. These data reveal components of the KAR interactome, and they show that GluK1 and Go proteins are natural partners, accounting for the metabotropic effects of KARs. Copyright © 2015 the authors 0270-6474/15/355171-09$15.00/0.

Structure and orientation of interfacial proteins determined by sum frequency generation vibrational spectroscopy: method and application.

PubMed

Ye, Shuji; Wei, Feng; Li, Hongchun; Tian, Kangzhen; Luo, Yi

2013-01-01

In situ and real-time characterization of molecular structures and orientation of proteins at interfaces is essential to understand the nature of interfacial protein interaction. Such work will undoubtedly provide important clues to control biointerface in a desired manner. Sum frequency generation vibrational spectroscopy (SFG-VS) has been demonstrated to be a powerful technique to study the interfacial structures and interactions at the molecular level. This paper first systematically introduced the methods for the calculation of the Raman polarizability tensor, infrared transition dipole moment, and SFG molecular hyperpolarizability tensor elements of proteins/peptides with the secondary structures of α-helix, 310-helix, antiparallel β-sheet, and parallel β-sheet, as well as the methodology to determine the orientation of interfacial protein secondary structures using SFG amide I spectra. After that, recent progresses on the determination of protein structure and orientation at different interfaces by SFG-VS were then reviewed, which provides a molecular-level understanding of the structures and interactions of interfacial proteins, specially understanding the nature of driving force behind such interactions. Although this review has focused on analysis of amide I spectra, it will be expected to offer a basic idea for the spectral analysis of amide III SFG signals and other complicated molecular systems such as RNA and DNA. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Detailed analysis of RNA-protein interactions within the bacterial ribosomal protein L5/5S rRNA complex.

PubMed

Perederina, Anna; Nevskaya, Natalia; Nikonov, Oleg; Nikulin, Alexei; Dumas, Philippe; Yao, Min; Tanaka, Isao; Garber, Maria; Gongadze, George; Nikonov, Stanislav

2002-12-01

The crystal structure of ribosomal protein L5 from Thermus thermophilus complexed with a 34-nt fragment comprising helix III and loop C of Escherichia coli 5S rRNA has been determined at 2.5 A resolution. The protein specifically interacts with the bulged nucleotides at the top of loop C of 5S rRNA. The rRNA and protein contact surfaces are strongly stabilized by intramolecular interactions. Charged and polar atoms forming the network of conserved intermolecular hydrogen bonds are located in two narrow planar parallel layers belonging to the protein and rRNA, respectively. The regions, including these atoms conserved in Bacteria and Archaea, can be considered an RNA-protein recognition module. Comparison of the T. thermophilus L5 structure in the RNA-bound form with the isolated Bacillus stearothermophilus L5 structure shows that the RNA-recognition module on the protein surface does not undergo significant changes upon RNA binding. In the crystal of the complex, the protein interacts with another RNA molecule in the asymmetric unit through the beta-sheet concave surface. This protein/RNA interface simulates the interaction of L5 with 23S rRNA observed in the Haloarcula marismortui 50S ribosomal subunit.

Detailed analysis of RNA-protein interactions within the bacterial ribosomal protein L5/5S rRNA complex.

PubMed Central

Perederina, Anna; Nevskaya, Natalia; Nikonov, Oleg; Nikulin, Alexei; Dumas, Philippe; Yao, Min; Tanaka, Isao; Garber, Maria; Gongadze, George; Nikonov, Stanislav

2002-01-01

The crystal structure of ribosomal protein L5 from Thermus thermophilus complexed with a 34-nt fragment comprising helix III and loop C of Escherichia coli 5S rRNA has been determined at 2.5 A resolution. The protein specifically interacts with the bulged nucleotides at the top of loop C of 5S rRNA. The rRNA and protein contact surfaces are strongly stabilized by intramolecular interactions. Charged and polar atoms forming the network of conserved intermolecular hydrogen bonds are located in two narrow planar parallel layers belonging to the protein and rRNA, respectively. The regions, including these atoms conserved in Bacteria and Archaea, can be considered an RNA-protein recognition module. Comparison of the T. thermophilus L5 structure in the RNA-bound form with the isolated Bacillus stearothermophilus L5 structure shows that the RNA-recognition module on the protein surface does not undergo significant changes upon RNA binding. In the crystal of the complex, the protein interacts with another RNA molecule in the asymmetric unit through the beta-sheet concave surface. This protein/RNA interface simulates the interaction of L5 with 23S rRNA observed in the Haloarcula marismortui 50S ribosomal subunit. PMID:12515387

Common structural features of cholesterol binding sites in crystallized soluble proteins

PubMed Central

Bukiya, Anna N.; Dopico, Alejandro M.

2017-01-01

Cholesterol-protein interactions are essential for the architectural organization of cell membranes and for lipid metabolism. While cholesterol-sensing motifs in transmembrane proteins have been identified, little is known about cholesterol recognition by soluble proteins. We reviewed the structural characteristics of binding sites for cholesterol and cholesterol sulfate from crystallographic structures available in the Protein Data Bank. This analysis unveiled key features of cholesterol-binding sites that are present in either all or the majority of sites: i) the cholesterol molecule is generally positioned between protein domains that have an organized secondary structure; ii) the cholesterol hydroxyl/sulfo group is often partnered by Asn, Gln, and/or Tyr, while the hydrophobic part of cholesterol interacts with Leu, Ile, Val, and/or Phe; iii) cholesterol hydrogen-bonding partners are often found on α-helices, while amino acids that interact with cholesterol’s hydrophobic core have a slight preference for β-strands and secondary structure-lacking protein areas; iv) the steroid’s C21 and C26 constitute the “hot spots” most often seen for steroid-protein hydrophobic interactions; v) common “cold spots” are C8–C10, C13, and C17, at which contacts with the proteins were not detected. Several common features we identified for soluble protein-steroid interaction appear evolutionarily conserved. PMID:28420706

DNAproDB: an interactive tool for structural analysis of DNA–protein complexes

PubMed Central

Sagendorf, Jared M.

2017-01-01

Abstract Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu. PMID:28431131

Plasma membrane lipid–protein interactions affect signaling processes in sterol-biosynthesis mutants in Arabidopsis thaliana

PubMed Central

Zauber, Henrik; Burgos, Asdrubal; Garapati, Prashanth; Schulze, Waltraud X.

2014-01-01

The plasma membrane is an important organelle providing structure, signaling and transport as major biological functions. Being composed of lipids and proteins with different physicochemical properties, the biological functions of membranes depend on specific protein–protein and protein–lipid interactions. Interactions of proteins with their specific sterol and lipid environment were shown to be important factors for protein recruitment into sub-compartmental structures of the plasma membrane. System-wide implications of altered endogenous sterol levels for membrane functions in living cells were not studied in higher plant cells. In particular, little is known how alterations in membrane sterol composition affect protein and lipid organization and interaction within membranes. Here, we conducted a comparative analysis of the plasma membrane protein and lipid composition in Arabidopsis sterol-biosynthesis mutants smt1 and ugt80A2;B1. smt1 shows general alterations in sterol composition while ugt80A2;B1 is significantly impaired in sterol glycosylation. By systematically analyzing different cellular fractions and combining proteomic with lipidomic data we were able to reveal contrasting alterations in lipid–protein interactions in both mutants, with resulting differential changes in plasma membrane signaling status. PMID:24672530

A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain.

PubMed

Goulding, Scott P; Szumlinski, Karen K; Contet, Candice; MacCoss, Michael J; Wu, Christine C

2017-08-23

In the brain, the Homer protein family modulates excitatory signal transduction and receptor plasticity through interactions with other proteins in dendritic spines. Homer proteins are implicated in a variety of psychiatric disorders such as schizophrenia and addiction. Since long Homers serve as scaffolding proteins, identifying their interacting partners is an important first step in understanding their biological function and could help to guide the design of new therapeutic strategies. The present study set out to document Homer2-interacting proteins in the mouse brain using a co-immunoprecipitation-based mass spectrometry approach where Homer2 knockout samples were used to filter out non-specific interactors. We found that in the mouse brain, Homer2 interacts with a limited subset of its previously reported interacting partners (3 out of 31). Importantly, we detected an additional 15 novel Homer2-interacting proteins, most of which are part of the N-methyl-D-aspartate receptor signaling pathway. These results corroborate the central role Homer2 plays in glutamatergic transmission and expand the network of proteins potentially contributing to the behavioral abnormalities associated with altered Homer2 expression. Long Homer proteins are scaffolding proteins that regulate signal transduction in neurons. Identifying their interacting partners is key to understanding their function. We used co-immunoprecipitation in combination with mass spectrometry to establish the first comprehensive list of Homer2-interacting partners in the mouse brain. The specificity of interactions was evaluated using Homer2 knockout brain tissue as a negative control. The set of proteins that we identified minimally overlaps with previously reported interacting partners of Homer2; however, we identified novel interactors that are part of a signaling cascade activated by glutamatergic transmission, which improves our mechanistic understanding of the role of Homer2 in behavior. Copyright © 2017 Elsevier B.V. All rights reserved.

Using a Specific RNA-Protein Interaction To Quench the Fluorescent RNA Spinach.

PubMed

Roszyk, Laura; Kollenda, Sebastian; Hennig, Sven

2017-12-15

RNAs are involved in interaction networks with other biomolecules and are crucial for proper cell function. Yet their biochemical analysis remains challenging. For Förster Resonance Energy Transfer (FRET), a common tool to study such interaction networks, two interacting molecules have to be fluorescently labeled. "Spinach" is a genetically encodable RNA aptamer that starts to fluoresce upon binding of an organic molecule. Therefore, it is a biological fluorophore tag for RNAs. However, spinach has never been used in a FRET assembly before. Here, we describe how spinach is quenched when close to acceptors. We used RNA-DNA hybridization to bring quenchers or red organic dyes in close proximity to spinach. Furthermore, we investigate RNA-protein interactions quantitatively on the example of Pseudomonas aeruginosa phage coat protein 7 (PP7) and its interacting pp7-RNA. We utilize spinach quenching as a fully genetically encodable system even under lysate conditions. Therefore, this work represents a direct method to analyze RNA-protein interactions by quenching the spinach aptamer.

On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins

PubMed Central

Wong, Eric T. C.; Na, Dokyun; Gsponer, Jörg

2013-01-01

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions. PMID:23990768

SysBioCube: A Data Warehouse and Integrative Data Analysis Platform Facilitating Systems Biology Studies of Disorders of Military Relevance

DTIC Science & Technology

2013-12-18

include interactive gene and methylation profiles, interactive heatmaps, cytoscape network views, integrative genomics viewer ( IGV ), and protein-protein...single chart. The website also provides an option to include multiple genes. Integrative Genomics Viewer ( IGV )1, is a high-performance desktop tool for

Design, synthesis and characterization of peptidomimetic conjugate of BODIPY targeting HER2 protein extracellular domain

PubMed Central

Banappagari, Sashikanth; McCall, Alecia; Fontenot, Krystal; Vicente, M. Graca H.; Gujar, Amit; Satyanarayanajois, Seetharama

2013-01-01

Among the EGFRs, HER2 is a major heterodimer partner and also has important implications in the formation of particular tumors. Interaction of HER2 protein with other EGFR proteins can be modulated by small molecule ligands and, hence, these protein-protein interactions play a key role in biochemical reactions related to control of cell growth. A peptidomimetic (compound 5-1) that binds to HER2 protein extracellular domain and inhibits protein-protein interactions of EGFRs was conjugated with BODIPY (4,4-difluoro-5,7-dimethyl-4-bora-3a,4a-diaza-s-indacene). Conjugation of BODIPY to the peptidomimetic was investigated by different approaches. The conjugate was characterized for its ability to bind to HER2 overexpressing SKBR-3 and BT-474 cells. Furthermore, cellular uptake of conjugate of BODIPY was studied in the presence of membrane tracker and Lyso tracker using confocal microscopy. Our results suggested that fluorescently labeled compound 5-7 binds to the extracellular domain and stays in the membrane for nearly 24 h. After 24 h there is an indication of internalization of the conjugate. Inhibition of protein-protein interaction and downstream signaling effect of compound 5-1 was also studied by proximity ligation assay and western blot analysis. Results suggested that compound 5-1 inhibits protein-protein interactions of HER2-HER3 and phosphorylation of HER2 in a time-dependent manner. PMID:23688700

Comprehensive analysis of RNA-protein interactions by high-throughput sequencing-RNA affinity profiling.

PubMed

Tome, Jacob M; Ozer, Abdullah; Pagano, John M; Gheba, Dan; Schroth, Gary P; Lis, John T

2014-06-01

RNA-protein interactions play critical roles in gene regulation, but methods to quantitatively analyze these interactions at a large scale are lacking. We have developed a high-throughput sequencing-RNA affinity profiling (HiTS-RAP) assay by adapting a high-throughput DNA sequencer to quantify the binding of fluorescently labeled protein to millions of RNAs anchored to sequenced cDNA templates. Using HiTS-RAP, we measured the affinity of mutagenized libraries of GFP-binding and NELF-E-binding aptamers to their respective targets and identified critical regions of interaction. Mutations additively affected the affinity of the NELF-E-binding aptamer, whose interaction depended mainly on a single-stranded RNA motif, but not that of the GFP aptamer, whose interaction depended primarily on secondary structure.

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

PubMed Central

Ramadan, Emad; Ward, Michael; Guo, Xin; Durkin, Sarah S; Sawyer, Adam; Vilela, Marcelo; Osgood, Christopher; Pothen, Alex; Semmes, Oliver J

2008-01-01

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome. PMID:18922151

REEPs Are Membrane Shaping Adapter Proteins That Modulate Specific G Protein-Coupled Receptor Trafficking by Affecting ER Cargo Capacity

PubMed Central

Ho, Vincent K.; Angelotti, Timothy

2013-01-01

Receptor expression enhancing proteins (REEPs) were identified by their ability to enhance cell surface expression of a subset of G protein-coupled receptors (GPCRs), specifically GPCRs that have proven difficult to express in heterologous cell systems. Further analysis revealed that they belong to the Yip (Ypt-interacting protein) family and that some REEP subtypes affect ER structure. Yip family comparisons have established other potential roles for REEPs, including regulation of ER-Golgi transport and processing/neuronal localization of cargo proteins. However, these other potential REEP functions and the mechanism by which they selectively enhance GPCR cell surface expression have not been clarified. By utilizing several REEP family members (REEP1, REEP2, and REEP6) and model GPCRs (α2A and α2C adrenergic receptors), we examined REEP regulation of GPCR plasma membrane expression, intracellular processing, and trafficking. Using a combination of immunolocalization and biochemical methods, we demonstrated that this REEP subset is localized primarily to ER, but not plasma membranes. Single cell analysis demonstrated that these REEPs do not specifically enhance surface expression of all GPCRs, but affect ER cargo capacity of specific GPCRs and thus their surface expression. REEP co-expression with α2 adrenergic receptors (ARs) revealed that this REEP subset interacts with and alter glycosidic processing of α2C, but not α2A ARs, demonstrating selective interaction with cargo proteins. Specifically, these REEPs enhanced expression of and interacted with minimally/non-glycosylated forms of α2C ARs. Most importantly, expression of a mutant REEP1 allele (hereditary spastic paraplegia SPG31) lacking the carboxyl terminus led to loss of this interaction. Thus specific REEP isoforms have additional intracellular functions besides altering ER structure, such as enhancing ER cargo capacity, regulating ER-Golgi processing, and interacting with select cargo proteins. Therefore, some REEPs can be further described as ER membrane shaping adapter proteins. PMID:24098485

qPIPSA: Relating enzymatic kinetic parameters and interaction fields

PubMed Central

Gabdoulline, Razif R; Stein, Matthias; Wade, Rebecca C

2007-01-01

Background The simulation of metabolic networks in quantitative systems biology requires the assignment of enzymatic kinetic parameters. Experimentally determined values are often not available and therefore computational methods to estimate these parameters are needed. It is possible to use the three-dimensional structure of an enzyme to perform simulations of a reaction and derive kinetic parameters. However, this is computationally demanding and requires detailed knowledge of the enzyme mechanism. We have therefore sought to develop a general, simple and computationally efficient procedure to relate protein structural information to enzymatic kinetic parameters that allows consistency between the kinetic and structural information to be checked and estimation of kinetic constants for structurally and mechanistically similar enzymes. Results We describe qPIPSA: quantitative Protein Interaction Property Similarity Analysis. In this analysis, molecular interaction fields, for example, electrostatic potentials, are computed from the enzyme structures. Differences in molecular interaction fields between enzymes are then related to the ratios of their kinetic parameters. This procedure can be used to estimate unknown kinetic parameters when enzyme structural information is available and kinetic parameters have been measured for related enzymes or were obtained under different conditions. The detailed interaction of the enzyme with substrate or cofactors is not modeled and is assumed to be similar for all the proteins compared. The protein structure modeling protocol employed ensures that differences between models reflect genuine differences between the protein sequences, rather than random fluctuations in protein structure. Conclusion Provided that the experimental conditions and the protein structural models refer to the same protein state or conformation, correlations between interaction fields and kinetic parameters can be established for sets of related enzymes. Outliers may arise due to variation in the importance of different contributions to the kinetic parameters, such as protein stability and conformational changes. The qPIPSA approach can assist in the validation as well as estimation of kinetic parameters, and provide insights into enzyme mechanism. PMID:17919319

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases. © The Author(s) 2015. Published by Oxford University Press.

Using circular dichroism collected as a function of temperature to determine the thermodynamics of protein unfolding and binding interactions

PubMed Central

Greenfield, Norma J.

2009-01-01

Circular dichroism (CD) is an excellent spectroscopic technique for following the unfolding and folding of proteins as a function of temperature. One of its principal applications is to determine the effects of mutations and ligands on protein and polypeptide stability If the change in CD as a function of temperature is reversible, analysis of the data may be used to determined the van't Hoff enthalpy (ΔH) and entropy (ΔS) of unfolding, the midpoint of the unfolding transition (TM) and the free energy (ΔG) of unfolding. Binding constants of protein-protein and protein-ligand interactions may also be estimated from the unfolding curves. Analysis of CD spectra obtained as a function of temperature is also useful to determine whether a protein has unfolding intermediates. Measurement of the spectra of five folded proteins and their unfolding curves at a single wavelength takes approximately eight hours. PMID:17406506

A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining.

PubMed

Barradas-Bautista, Didier; Moal, Iain H; Fernández-Recio, Juan

2017-07-01

Protein-protein interactions play fundamental roles in biological processes including signaling, metabolism, and trafficking. While the structure of a protein complex reveals crucial details about the interaction, it is often difficult to acquire this information experimentally. As the number of interactions discovered increases faster than they can be characterized, protein-protein docking calculations may be able to reduce this disparity by providing models of the interacting proteins. Rigid-body docking is a widely used docking approach, and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. Recently, more than 100 scoring functions from the CCharPPI server were evaluated for this task using decoy structures generated with SwarmDock. Here, we extend this analysis to identify the predictive success rates of the scoring functions on decoys from three rigid-body docking programs, ZDOCK, FTDock, and SDOCK, allowing us to assess the transferability of the functions. We also apply set-theoretic measure to test whether the scoring functions are capable of identifying near-native poses within different subsets of the benchmark. This information can provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts. Proteins 2017; 85:1287-1297. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Toward structure prediction of cyclic peptides.

PubMed

Yu, Hongtao; Lin, Yu-Shan

2015-02-14

Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

CAVER Analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures.

PubMed

Kozlikova, Barbora; Sebestova, Eva; Sustr, Vilem; Brezovsky, Jan; Strnad, Ondrej; Daniel, Lukas; Bednar, David; Pavelka, Antonin; Manak, Martin; Bezdeka, Martin; Benes, Petr; Kotry, Matus; Gora, Artur; Damborsky, Jiri; Sochor, Jiri

2014-09-15

The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics. CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

High-throughput profiling of nanoparticle-protein interactions by fluorescamine labeling.

PubMed

Ashby, Jonathan; Duan, Yaokai; Ligans, Erik; Tamsi, Michael; Zhong, Wenwan

2015-02-17

A rapid, high throughput fluorescence assay was designed to screen interactions between proteins and nanoparticles. The assay employs fluorescamine, a primary-amine specific fluorogenic dye, to label proteins. Because fluorescamine could specifically target the surface amines on proteins, a conformational change of the protein upon interaction with nanoparticles will result in a change in fluorescence. In the present study, the assay was applied to test the interactions between a selection of proteins and nanoparticles made of polystyrene, silica, or iron oxide. The particles were also different in their hydrodynamic diameter, synthesis procedure, or surface modification. Significant labeling differences were detected when the same protein incubated with different particles. Principal component analysis (PCA) on the collected fluorescence profiles revealed clear grouping effects of the particles based on their properties. The results prove that fluorescamine labeling is capable of detecting protein-nanoparticle interactions, and the resulting fluorescence profile is sensitive to differences in nanoparticle's physical properties. The assay can be carried out in a high-throughput manner, and is rapid with low operation cost. Thus, it is well suited for evaluating interactions between a larger number of proteins and nanoparticles. Such assessment can help to improve our understanding on the molecular basis that governs the biological behaviors of nanomaterials. It will also be useful for initial examination of the bioactivity and reproducibility of nanomaterials employed in biomedical fields.

Interactions between subunits of Saccharomyces cerevisiae RNase MRP support a conserved eukaryotic RNase P/MRP architecture.

PubMed

Aspinall, Tanya V; Gordon, James M B; Bennett, Hayley J; Karahalios, Panagiotis; Bukowski, John-Paul; Walker, Scott C; Engelke, David R; Avis, Johanna M

2007-01-01

Ribonuclease MRP is an endonuclease, related to RNase P, which functions in eukaryotic pre-rRNA processing. In Saccharomyces cerevisiae, RNase MRP comprises an RNA subunit and ten proteins. To improve our understanding of subunit roles and enzyme architecture, we have examined protein-protein and protein-RNA interactions in vitro, complementing existing yeast two-hybrid data. In total, 31 direct protein-protein interactions were identified, each protein interacting with at least three others. Furthermore, seven proteins self-interact, four strongly, pointing to subunit multiplicity in the holoenzyme. Six protein subunits interact directly with MRP RNA and four with pre-rRNA. A comparative analysis with existing data for the yeast and human RNase P/MRP systems enables confident identification of Pop1p, Pop4p and Rpp1p as subunits that lie at the enzyme core, with probable addition of Pop5p and Pop3p. Rmp1p is confirmed as an integral subunit, presumably associating preferentially with RNase MRP, rather than RNase P, via interactions with Snm1p and MRP RNA. Snm1p and Rmp1p may act together to assist enzyme specificity, though roles in substrate binding are also indicated for Pop4p and Pop6p. The results provide further evidence of a conserved eukaryotic RNase P/MRP architecture and provide a strong basis for studies of enzyme assembly and subunit function.

A force-based, parallel assay for the quantification of protein-DNA interactions.

PubMed

Limmer, Katja; Pippig, Diana A; Aschenbrenner, Daniela; Gaub, Hermann E

2014-01-01

Analysis of transcription factor binding to DNA sequences is of utmost importance to understand the intricate regulatory mechanisms that underlie gene expression. Several techniques exist that quantify DNA-protein affinity, but they are either very time-consuming or suffer from possible misinterpretation due to complicated algorithms or approximations like many high-throughput techniques. We present a more direct method to quantify DNA-protein interaction in a force-based assay. In contrast to single-molecule force spectroscopy, our technique, the Molecular Force Assay (MFA), parallelizes force measurements so that it can test one or multiple proteins against several DNA sequences in a single experiment. The interaction strength is quantified by comparison to the well-defined rupture stability of different DNA duplexes. As a proof-of-principle, we measured the interaction of the zinc finger construct Zif268/NRE against six different DNA constructs. We could show the specificity of our approach and quantify the strength of the protein-DNA interaction.

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

PubMed

Wang, Anping; Zhang, Guibin

2017-11-01

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

In Cell Footprinting Coupled with Mass Spectrometry for the Structural Analysis of Proteins in Live Cells.

PubMed

Espino, Jessica A; Mali, Vishaal S; Jones, Lisa M

2015-08-04

Protein footprinting coupled with mass spectrometry has become a widely used tool for the study of protein-protein and protein-ligand interactions and protein conformational change. These methods provide residue-level analysis on protein interaction sites and have been successful in studying proteins in vitro. The extension of these methods for in cell footprinting would open an avenue to study proteins that are not amenable for in vitro studies and would probe proteins in their native environment. Here we describe the application of an oxidative-based footprinting approach inside cells in which hydroxyl radicals are used to oxidatively modify proteins. Mass spectrometry is used to detect modification sites and to calculate modification levels. The method is probing biologically relevant proteins in live cells, and proteins in various cellular compartments can be oxdiatively modified. Several different amino acid residues are modified making the method a general labeling strategy for the study of a variety of proteins. Further, comparison of the extent of oxidative modification with solvent accessible surface area reveals the method successfully probes solvent accessibility. This marks the first time protein footprinting has been performed in live cells.

Molecular Dynamics Simulations, Challenges and Opportunities: A Biologist's Prospective.

PubMed

Kumari, Indu; Sandhu, Padmani; Ahmed, Mushtaq; Akhter, Yusuf

2017-08-30

Molecular dynamics (MD) is a computational technique which is used to study biomolecules in virtual environment. Each of the constituent atoms represents a particle and hence the biomolecule embodies a multi-particle mechanical system analyzed within a simulation box during MD analysis. The potential energies of the atoms are explained by a mathematical expression consisting of different forces and space parameters. There are various software and force fields that have been developed for MD studies of the biomolecules. MD analysis has unravelled the various biological mechanisms (protein folding/unfolding, protein-small molecule interactions, protein-protein interactions, DNA/RNA-protein interactions, proteins embedded in membrane, lipid-lipid interactions, drug transport etc.) operating at the atomic and molecular levels. However, there are still some parameters including torsions in amino acids, carbohydrates (whose structure is extended and not well defined like that of proteins) and single stranded nucleic acids for which the force fields need further improvement, although there are several workers putting in constant efforts in these directions. The existing force fields are not efficient for studying the crowded environment inside the cells, since these interactions involve multiple factors in real time. Therefore, the improved force fields may provide the opportunities for their wider applications on the complex biosystems in diverse cellular conditions. In conclusion, the intervention of MD in the basic sciences involving interdisciplinary approaches will be helpful for understanding many fundamental biological and physiological processes at the molecular levels that may be further applied in various fields including biotechnology, fisheries, sustainable agriculture and biomedical research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Quantitative interactome reveals that porcine reproductive and respiratory syndrome virus nonstructural protein 2 forms a complex with viral nucleocapsid protein and cellular vimentin.

PubMed

Song, Tao; Fang, Liurong; Wang, Dang; Zhang, Ruoxi; Zeng, Songlin; An, Kang; Chen, Huanchun; Xiao, Shaobo

2016-06-16

Porcine reproductive and respiratory syndrome virus (PRRSV) is an Arterivirus that has heavily impacted the global swine industry. The PRRSV nonstructural protein 2 (nsp2) plays crucial roles in viral replication and host immune regulation, most likely by interacting with viral or cellular proteins that have not yet been identified. In this study, a quantitative interactome approach based on immunoprecipitation and stable isotope labeling with amino acids in cell culture (SILAC) was performed to identify nsp2-interacting proteins in PRRSV-infected cells with an nsp2-specific monoclonal antibody. Nine viral proteins and 62 cellular proteins were identified as potential nsp2-interacting partners. Our data demonstrate that the PRRSV nsp1α, nsp1β, and nucleocapsid proteins all interact directly with nsp2. Nsp2-interacting cellular proteins were classified into different functional groups and an interactome network of nsp2 was generated. Interestingly, cellular vimentin, a known receptor for PRRSV, forms a complex with nsp2 by using viral nucleocapsid protein as an intermediate. Taken together, the nsp2 interactome under the condition of virus infection clarifies a role of nsp2 in PRRSV replication and immune evasion. Viral proteins must interact with other virus-encoded proteins and/or host cellular proteins to function, and interactome analysis is an ideal approach for identifying such interacting proteins. In this study, we used the quantitative interactome methodology to identify the viral and cellular proteins that potentially interact with the nonstructural protein 2 (nsp2) of porcine reproductive and respiratory syndrome virus (PRRSV) under virus infection conditions, thus providing a rich source of potential viral and cellular interaction partners for PRRSV nsp2. Based on the interactome data, we further demonstrated that PRRSV nsp2 and nucleocapsid protein together with cellular vimentin, form a complex that may be essential for viral attachment and replication, which partly explains the role of nsp2 in PRRSV replication and immune evasion. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

PubMed Central

Hsing, Michael; Byler, Kendall; Cherkasov, Artem

2009-01-01

Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

Protein associations and analytical ultracentrifugation

NASA Astrophysics Data System (ADS)

Laue, Tom

2010-03-01

Analytical ultracentrifugation (AUC) is a first principle method for characterizing the thermodynamics of macromolecules in solution. Since AUC directly assesses mass, it is particularly useful for characterizing both reversible and irreversible binding interactions between macromolecules. The principle measurement in AUC is the concentration as a function of radial position, which may be provided by either absorbance, interference or fluorescence detection. Each of these three different detectors may be used to characterize protein associations using either sedimentation equilibrium or sedimentation velocity analysis. Examples will be shown for characterizing irreversible (aggregate) formation, high-accuracy reversible association analysis, and the detection of protein interactions in complex and concentrated fluids (e.g. serum, cell cytosol).

Analysis of Paracoccidioides secreted proteins reveals fructose 1,6-bisphosphate aldolase as a plasminogen-binding protein.

PubMed

Chaves, Edilânia Gomes Araújo; Weber, Simone Schneider; Báo, Sonia Nair; Pereira, Luiz Augusto; Bailão, Alexandre Melo; Borges, Clayton Luiz; Soares, Célia Maria de Almeida

2015-02-27

Despite being important thermal dimorphic fungi causing Paracoccidioidomycosis, the pathogenic mechanisms that underlie the genus Paracoccidioides remain largely unknown. Microbial pathogens express molecules that can interact with human plasminogen, a protein from blood plasma, which presents fibrinolytic activity when activated into plasmin. Additionally, plasmin exhibits the ability of degrading extracellular matrix components, favoring the pathogen spread to deeper tissues. Previous work from our group demonstrated that Paracoccidioides presents enolase, as a protein able to bind and activate plasminogen, increasing the fibrinolytic activity of the pathogen, and the potential for adhesion and invasion of the fungus to host cells. By using proteomic analysis, we aimed to identify other proteins of Paracoccidioides with the ability of binding to plasminogen. In the present study, we employed proteomic analysis of the secretome, in order to identify plasminogen-binding proteins of Paracoccidioides, Pb01. Fifteen proteins were present in the fungal secretome, presenting the ability to bind to plasminogen. Those proteins are probable targets of the fungus interaction with the host; thus, they could contribute to the invasiveness of the fungus. For validation tests, we selected the protein fructose 1,6-bisphosphate aldolase (FBA), described in other pathogens as a plasminogen-binding protein. The protein FBA at the fungus surface and the recombinant FBA (rFBA) bound human plasminogen and promoted its conversion to plasmin, potentially increasing the fibrinolytic capacity of the fungus, as demonstrated in fibrin degradation assays. The addition of rFBA or anti-rFBA antibodies was capable of reducing the interaction between macrophages and Paracoccidioides, possibly by blocking the binding sites for FBA. These data reveal the possible participation of the FBA in the processes of cell adhesion and tissue invasion/dissemination of Paracoccidioides. These data indicate that Paracoccidioides is a pathogen that has several plasminogen-binding proteins that likely play important roles in pathogen-host interaction. In this context, FBA is a protein that might be involved somehow in the processes of invasion and spread of the fungus during infection.

A systematic analysis of atomic protein-ligand interactions in the PDB.

PubMed

Ferreira de Freitas, Renato; Schapira, Matthieu

2017-10-01

As the protein databank (PDB) recently passed the cap of 123 456 structures, it stands more than ever as an important resource not only to analyze structural features of specific biological systems, but also to study the prevalence of structural patterns observed in a large body of unrelated structures, that may reflect rules governing protein folding or molecular recognition. Here, we compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins - 6444 of which have experimental binding affinity - representing 750 873 protein-ligand atomic interactions, and analyzed the frequency, geometry and impact of each interaction type. We find that hydrophobic interactions are generally enriched in high-efficiency ligands, but polar interactions are over-represented in fragment inhibitors. While most observations extracted from the PDB will be familiar to seasoned medicinal chemists, less expected findings, such as the high number of C-H···O hydrogen bonds or the relatively frequent amide-π stacking between the backbone amide of proteins and aromatic rings of ligands, uncover underused ligand design strategies.

Analyzing Carbohydrate-Protein Interaction Based on Single Plasmonic Nanoparticle by Conventional Dark Field Microscopy.

PubMed

Jin, Hong-Ying; Li, Da-Wei; Zhang, Na; Gu, Zhen; Long, Yi-Tao

2015-06-10

We demonstrated a practical method to analyze carbohydrate-protein interaction based on single plasmonic nanoparticles by conventional dark field microscopy (DFM). Protein concanavalin A (ConA) was modified on large sized gold nanoparticles (AuNPs), and dextran was conjugated on small sized AuNPs. As the interaction between ConA and dextran resulted in two kinds of gold nanoparticles coupled together, which caused coupling of plasmonic oscillations, apparent color changes (from green to yellow) of the single AuNPs were observed through DFM. Then, the color information was instantly transformed into a statistic peak wavelength distribution in less than 1 min by a self-developed statistical program (nanoparticleAnalysis). In addition, the interaction between ConA and dextran was proved with biospecific recognition. This approach is high-throughput and real-time, and is a convenient method to analyze carbohydrate-protein interaction at the single nanoparticle level efficiently.

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Structural Determinants of Sleeping Beauty Transposase Activity

PubMed Central

Abrusán, György; Yant, Stephen R; Szilágyi, András; Marsh, Joseph A; Mátés, Lajos; Izsvák, Zsuzsanna; Barabás, Orsolya; Ivics, Zoltán

2016-01-01

Transposases are important tools in genome engineering, and there is considerable interest in engineering more efficient ones. Here, we seek to understand the factors determining their activity using the Sleeping Beauty transposase. Recent work suggests that protein coevolutionary information can be used to classify groups of physically connected, coevolving residues into elements called “sectors”, which have proven useful for understanding the folding, allosteric interactions, and enzymatic activity of proteins. Using extensive mutagenesis data, protein modeling and analysis of folding energies, we show that (i) The Sleeping Beauty transposase contains two sectors, which span across conserved domains, and are enriched in DNA-binding residues, indicating that the DNA binding and endonuclease functions of the transposase coevolve; (ii) Sector residues are highly sensitive to mutations, and most mutations of these residues strongly reduce transposition rate; (iii) Mutations with a strong effect on free energy of folding in the DDE domain of the transposase significantly reduce transposition rate. (iv) Mutations that influence DNA and protein-protein interactions generally reduce transposition rate, although most hyperactive mutants are also located on the protein surface, including residues with protein-protein interactions. This suggests that hyperactivity results from the modification of protein interactions, rather than the stabilization of protein fold. PMID:27401040

Distyrylbenzene-aldehydes: identification of proteins in water.

PubMed

Kumpf, Jan; Freudenberg, Jan; Bunz, Uwe H F

2015-05-07

Three different, water soluble, aldehyde-appended distyrylbenzene (DSB) derivatives were prepared. Their interaction with different albumin variants (human, porcine, bovine, lactalbumin, ovalbumin) was investigated (pH 11). All three fluorophores exhibit graded, protein-dependent fluorescence turn-on at slightly differing wavelengths. Linear discriminant analysis (LDA) differentiated all of the investigated albumins and was used to discern commercially available protein shakes. The three DSB derivatives barely react with the constituting amino acids but cysteine. In the proteins significant fluorescence signals are generated, probably due to a combination of imine/N,S-aminal formation and hydrophobic interactions between the DSBs and the proteins.

A decision tree-based combination of ezrin-interacting proteins to estimate the prognostic risk of patients with esophageal squamous cell carcinoma.

PubMed

He, Jian-Zhong; Wu, Zhi-Yong; Wang, Shao-Hong; Ji, Xia; Yang, Cui-Xia; Xu, Xiu-E; Liao, Lian-Di; Wu, Jian-Yi; Li, En-Min; Zhang, Kai; Xu, Li-Yan

2017-08-01

Our previous studies have highlighted the importance of ezrin in esophageal squamous cell carcinoma (ESCC). Here our objective was to explore the clinical significance of ezrin-interacting proteins, which would provide a theoretical basis for understanding the function of ezrin and potential therapeutic targets for ESCC. We used affinity purification and mass spectrometry to identify PDIA3, CNPY2, and STMN1 as potential ezrin-interacting proteins. Confocal microscopy and coimmunoprecipitation analysis further confirmed the colocalization and interaction of ezrin with PDIA3, CNPY2, and STMN1. Tissue microarray data of ESCC samples (n=263) showed that the 5-year overall survival (OS) and disease-free survival (DFS) were significantly lower for the CNPY2 (OS, P=.003; DFS, P=.011) and STMN1 (OS, P=.010; DFS, P=.002) high-expression groups compared with the low-expression groups. By contrast, overexpression of PDIA3 was significantly correlated with favorable survival (OS, P<.001; DFS, P=.001). Cox regression demonstrated the prognostic value of PDIA3, CNPY2, and STMN1 in ESCC. Furthermore, decision tree analysis revealed that the resulting classifier of both ezrin and its interacting proteins could be used to better predict OS and DFS of patients with ESCC. In conclusion, a signature of ezrin-interacting proteins accurately predicts ESCC patient survival or tumor recurrence. Copyright © 2017 Elsevier Inc. All rights reserved.

Noroviruses Co-opt the Function of Host Proteins VAPA and VAPB for Replication via a Phenylalanine-Phenylalanine-Acidic-Tract-Motif Mimic in Nonstructural Viral Protein NS1/2.

PubMed

McCune, Broc T; Tang, Wei; Lu, Jia; Eaglesham, James B; Thorne, Lucy; Mayer, Anne E; Condiff, Emily; Nice, Timothy J; Goodfellow, Ian; Krezel, Andrzej M; Virgin, Herbert W

2017-07-11

The Norovirus genus contains important human pathogens, but the role of host pathways in norovirus replication is largely unknown. Murine noroviruses provide the opportunity to study norovirus replication in cell culture and in small animals. The human norovirus nonstructural protein NS1/2 interacts with the host protein VAMP-associated protein A (VAPA), but the significance of the NS1/2-VAPA interaction is unexplored. Here we report decreased murine norovirus replication in VAPA- and VAPB-deficient cells. We characterized the role of VAPA in detail. VAPA was required for the efficiency of a step(s) in the viral replication cycle after entry of viral RNA into the cytoplasm but before the synthesis of viral minus-sense RNA. The interaction of VAPA with viral NS1/2 proteins is conserved between murine and human noroviruses. Murine norovirus NS1/2 directly bound the major sperm protein (MSP) domain of VAPA through its NS1 domain. Mutations within NS1 that disrupted interaction with VAPA inhibited viral replication. Structural analysis revealed that the viral NS1 domain contains a mimic of the phenylalanine-phenylalanine-acidic-tract (FFAT) motif that enables host proteins to bind to the VAPA MSP domain. The NS1/2-FFAT mimic region interacted with the VAPA-MSP domain in a manner similar to that seen with bona fide host FFAT motifs. Amino acids in the FFAT mimic region of the NS1 domain that are important for viral replication are highly conserved across murine norovirus strains. Thus, VAPA interaction with a norovirus protein that functionally mimics host FFAT motifs is important for murine norovirus replication. IMPORTANCE Human noroviruses are a leading cause of gastroenteritis worldwide, but host factors involved in norovirus replication are incompletely understood. Murine noroviruses have been studied to define mechanisms of norovirus replication. Here we defined the importance of the interaction between the hitherto poorly studied NS1/2 norovirus protein and the VAPA host protein. The NS1/2-VAPA interaction is conserved between murine and human noroviruses and was important for early steps in murine norovirus replication. Using structure-function analysis, we found that NS1/2 contains a short sequence that molecularly mimics the FFAT motif that is found in multiple host proteins that bind VAPA. This represents to our knowledge the first example of functionally important mimicry of a host FFAT motif by a microbial protein. Copyright © 2017 McCune et al.

Docking analysis of verteporfin with YAP WW domain

PubMed Central

Kandoussi, Ilham; Lakhlili, Wiame; Taoufik, Jamal; Ibrahimi, Azeddine

2017-01-01

The YAP oncogene is a known cancer target. Therefore, it is of interest to understand the molecular docking interaction of verteporfin (a derivative of benzo-porphyrin) with the WW domain of YAP (clinically used for photo-dynamic therapy in macular degeneration) as a potential WW domain-ligand modulator by inhibition. A homology protein SWISS MODEL of the human YAP protein was constructed to dock (using AutoDock vina) with the PubChem verteporfin structure for interaction analysis. The docking result shows the possibilities of verteporfin interaction with the oncogenic transcription cofactor YAP having WW1 and WW2 domains. Thus, the ability of verteporfin to bind with the YAP WW domain having modulator activity is implied in this analysis. PMID:28943729

Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

NASA Astrophysics Data System (ADS)

Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.

2017-07-01

Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

PubMed Central

2013-01-01

Background H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. Results We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. Conclusions The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. PMID:24564941

Reexamining protein–protein and protein–solvent interactions from Kirkwood-Buff analysis of light scattering in multi-component solutions

PubMed Central

Blanco, Marco A.; Sahin, Erinc; Li, Yi; Roberts, Christopher J.

2011-01-01

The classic analysis of Rayleigh light scattering (LS) is re-examined for multi-component protein solutions, within the context of Kirkwood-Buff (KB) theory as well as a more generalized canonical treatment. Significant differences arise when traditional treatments that approximate constant pressure and neglect concentration fluctuations in one or more (co)solvent∕co-solute species are compared with more rigorous treatments at constant volume and with all species free to fluctuate. For dilute solutions, it is shown that LS can be used to rigorously and unambiguously obtain values for the osmotic second virial coefficient (B22), in contrast with recent arguments regarding protein interactions deduced from LS experiments. For more concentrated solutions, it is shown that conventional analysis over(under)-estimates the magnitude of B22 for significantly repulsive(attractive) conditions, and that protein-protein KB integrals (G22) are the more relevant quantity obtainable from LS. Published data for α–chymotrypsinogen A and a series of monoclonal antibodies at different pH and salt concentrations are re-analyzed using traditional and new treatments. The results illustrate that while traditional analysis may be sufficient if one is interested in only the sign of B22 or G22, the quantitative values can be significantly in error. A simple approach is illustrated for determining whether protein concentration (c2) is sufficiently dilute for B22 to apply, and for correcting B22 values from traditional LS regression at higher c2 values. The apparent molecular weight M2, app obtained from LS is shown to generally not be equal to the true molecular weight, with the differences arising from a combination of protein-solute and protein-cosolute interactions that may, in principle, also be determined from LS. PMID:21682538

A STE12 homologue of the homothallic ascomycete Sordaria macrospora interacts with the MADS box protein MCM1 and is required for ascosporogenesis.

PubMed

Nolting, Nicole; Pöggeler, Stefanie

2006-11-01

The MADS box protein MCM1 controls diverse developmental processes and is essential for fruiting body formation in the homothallic ascomycete Sordaria macrospora. MADS box proteins derive their regulatory specificity from a wide range of different protein interactions. We have recently shown that the S. macrospora MCM1 is able to interact with the alpha-domain mating-type protein SMTA-1. To further evaluate the functional roles of MCM1, we used the yeast two-hybrid approach to identify MCM1-interacting proteins. From this screen, we isolated a protein with a putative N-terminal homeodomain and C-terminal C2/H2-Zn2+ finger domains. The protein is a member of the highly conserved fungal STE12 transcription factor family of proteins and was therefore termed STE12. Furthermore, we demonstrate by means of two-hybrid and far western analysis that in addition to MCM1, the S. macrospora STE12 protein is able to interact with the mating-type protein SMTA-1. Unlike the situation in the closely related heterothallic ascomycete Neurospora crassa, deletion (Delta) of the ste12 gene in S. macrospora neither affects vegetative growth nor fruiting body formation. However, ascus and ascospore development are highly impaired by the Deltaste12 mutation. Our data provide another example of the functional divergence within the fungal STE12 transcription factor family.

Development of automated high throughput single molecular microfluidic detection platform for signal transduction analysis

NASA Astrophysics Data System (ADS)

Huang, Po-Jung; Baghbani Kordmahale, Sina; Chou, Chao-Kai; Yamaguchi, Hirohito; Hung, Mien-Chie; Kameoka, Jun

2016-03-01

Signal transductions including multiple protein post-translational modifications (PTM), protein-protein interactions (PPI), and protein-nucleic acid interaction (PNI) play critical roles for cell proliferation and differentiation that are directly related to the cancer biology. Traditional methods, like mass spectrometry, immunoprecipitation, fluorescence resonance energy transfer, and fluorescence correlation spectroscopy require a large amount of sample and long processing time. "microchannel for multiple-parameter analysis of proteins in single-complex (mMAPS)"we proposed can reduce the process time and sample volume because this system is composed by microfluidic channels, fluorescence microscopy, and computerized data analysis. In this paper, we will present an automated mMAPS including integrated microfluidic device, automated stage and electrical relay for high-throughput clinical screening. Based on this result, we estimated that this automated detection system will be able to screen approximately 150 patient samples in a 24-hour period, providing a practical application to analyze tissue samples in a clinical setting.

Mixed retention mechanism of proteins in weak anion-exchange chromatography.

PubMed

Liu, Peng; Yang, Haiya; Geng, Xindu

2009-10-30

Using four commercial weak anion-exchange chromatography (WAX) columns and 11 kinds of different proteins, we experimentally examined the involvement of hydrophobic interaction chromatography (HIC) mechanism in protein retention on the WAX columns. The HIC mechanism was found to operate in all four WAX columns, and each of these columns had a better resolution in the HIC mode than in the corresponding WAX mode. Detailed analysis of the molecular interactions in a chromatographic system indicated that it is impossible to completely eliminate hydrophobic interactions from a WAX column. Based on these results, it may be possible to employ a single WAX column for protein separation by exploiting mixed modes (WAX and HIC) of retention. The stoichiometric displacement theory and two linear plots were used to show that mechanism of the mixed modes of retention in the system was a combination of two kinds of interactions, i.e., nonselective interactions in the HIC mode and selective interactions in the IEC mode. The obtained U-shaped elution curve of proteins could be distinguished into four different ranges of salt concentration, which also represent four retention regions.

Proteomic Analysis of Interactions between a Deep-Sea Thermophilic Bacteriophage and Its Host at High Temperature ▿ †

PubMed Central

Wei, Dahai; Zhang, Xiaobo

2010-01-01

The virus-host interaction is essential to understanding the role that viruses play in ecological and geochemical processes in deep-sea vent ecosystems. Virus-induced changes in cellular gene expression and host physiology have been studied extensively. However, the molecular mechanism of interaction between a bacteriophage and its host at high temperature remains poorly understood. In the present study, the virus-induced gene expression profile of Geobacillus sp. E263, a thermophile isolated from a deep-sea hydrothermal ecosystem, was characterized. Based on proteomic analysis and random arbitrarily primed PCR (RAP-PCR) of Geobacillus sp. E263 cultured under non-bacteriophage GVE2 infection and GVE2 infection conditions, there were two types of protein/gene profiles in response to GVE2 infection. Twenty differentially expressed genes and proteins were revealed that could be grouped into 3 different categories based on cellular function, suggesting a coordinated response to infection. These differentially expressed genes and proteins were further confirmed by Northern blot analysis. To characterize the host proteins in response to virus infection, aspartate aminotransferase (AST) was inactivated to construct the AST mutant of Geobacillus sp. E263. The results showed that the AST protein was essential in virus infection. Thus, transcriptional and proteomic analyses and functional analysis revealed previously unknown host responses to deep-sea thermophilic virus infection. PMID:20015994

Host cell interactome of PA protein of H5N1 influenza A virus in chicken cells.

PubMed

Wang, Qiao; Li, Qinghe; Liu, Ranran; Zheng, Maiqing; Wen, Jie; Zhao, Guiping

2016-03-16

Influenza A virus (IAV) heavily depends on viral-host protein interactions in order to replicate and spread. Identification of host factors that interact with viral proteins plays crucial roles in understanding the mechanism of IAV infection. Here we report the interaction landscape of H5N1 IAV PA protein in chicken cells through the use of affinity purification and mass spectrometry. PA protein was expressed in chicken cells and PA interacting complexes were captured by co-immunoprecipitation and analyzed by mass spectrometry. A total of 134 proteins were identified as PA-host interacting factors. Protein complexes including the minichromosome maintenance complex (MCM), 26S proteasome and the coat protein I (COPI) complex associated with PA in chicken cells, indicating the essential roles of these functional protein complexes during the course of IAV infection. Gene Ontology and pathway enrichment analysis both showed strong enrichment of PA interacting proteins in the category of DNA replication, covering genes such as PCNA, MCM2, MCM3, MCM4, MCM5 and MCM7. This study has uncovered the comprehensive interactome of H5N1 IAV PA protein in its chicken host and helps to establish the foundation for further investigation into the newly identified viral-host interactions. Influenza A virus (IAV) is a great threat to public health and avian production. However, the manner in which avian IAV recruits the host cellular machinery for replication and how the host antagonizes the IAV infection was previously poorly understood. Here we present the viral-host interactome of the H5N1 IAV PA protein and reveal the comprehensive association of host factors with PA. Copyright © 2016 Elsevier B.V. All rights reserved.

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

PubMed

Lautz, Jonathan D; Brown, Emily A; VanSchoiack, Alison A Williams; Smith, Stephen E P

2018-05-27

Cells utilize dynamic, network level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of preexisting multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The Interactomic Analysis Reveals Pathogenic Protein Networks in Phomopsis longicolla Underlying Seed Decay of Soybean.

PubMed

Li, Shuxian; Musungu, Bryan; Lightfoot, David; Ji, Pingsheng

2018-01-01

Phomopsis longicolla T. W. Hobbs (syn. Diaporthe longicolla ) is the primary cause of Phomopsis seed decay (PSD) in soybean, Glycine max (L.) Merrill. This disease results in poor seed quality and is one of the most economically important seed diseases in soybean. The objectives of this study were to infer protein-protein interactions (PPI) and to identify conserved global networks and pathogenicity subnetworks in P. longicolla including orthologous pathways for cell signaling and pathogenesis. The interlog method used in the study identified 215,255 unique PPIs among 3,868 proteins. There were 1,414 pathogenicity related genes in P. longicolla identified using the pathogen host interaction (PHI) database. Additionally, 149 plant cell wall degrading enzymes (PCWDE) were detected. The network captured five different classes of carbohydrate degrading enzymes, including the auxiliary activities, carbohydrate esterases, glycoside hydrolases, glycosyl transferases, and carbohydrate binding molecules. From the PPI analysis, novel interacting partners were determined for each of the PCWDE classes. The most predominant class of PCWDE was a group of 60 glycoside hydrolases proteins. The glycoside hydrolase subnetwork was found to be interacting with 1,442 proteins within the network and was among the largest clusters. The orthologous proteins FUS3, HOG, CYP1, SGE1, and the g5566t.1 gene identified in this study could play an important role in pathogenicity. Therefore, the P. longicolla protein interactome (PiPhom) generated in this study can lead to a better understanding of PPIs in soybean pathogens. Furthermore, the PPI may aid in targeting of genes and proteins for further studies of the pathogenicity mechanisms.

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

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

NASA Astrophysics Data System (ADS)

Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

2017-02-01

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Computational and Experimental Analysis of the Secretome of Methylococcus capsulatus (Bath)

PubMed Central

Indrelid, Stine; Mathiesen, Geir; Jacobsen, Morten; Lea, Tor; Kleiveland, Charlotte R.

2014-01-01

The Gram-negative methanotroph Methylococcus capsulatus (Bath) was recently demonstrated to abrogate inflammation in a murine model of inflammatory bowel disease, suggesting interactions with cells involved in maintaining mucosal homeostasis and emphasizing the importance of understanding the many properties of M. capsulatus. Secreted proteins determine how bacteria may interact with their environment, and a comprehensive knowledge of such proteins is therefore vital to understand bacterial physiology and behavior. The aim of this study was to systematically analyze protein secretion in M. capsulatus (Bath) by identifying the secretion systems present and the respective secreted substrates. Computational analysis revealed that in addition to previously recognized type II secretion systems and a type VII secretion system, a type Vb (two-partner) secretion system and putative type I secretion systems are present in M. capsulatus (Bath). In silico analysis suggests that the diverse secretion systems in M.capsulatus transport proteins likely to be involved in adhesion, colonization, nutrient acquisition and homeostasis maintenance. Results of the computational analysis was verified and extended by an experimental approach showing that in addition an uncharacterized protein and putative moonlighting proteins are released to the medium during exponential growth of M. capsulatus (Bath). PMID:25479164

Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

PubMed

Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K; Chowdhury, Rajdeep; Jalan, Sarika

2017-02-03

Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109

Characterization of Bufo arenarum oocyte plasma membrane proteins that interact with sperm.

PubMed

Coux, Gabriela; Cabada, Marcelo O

2006-04-28

Sperm-oocyte plasma membrane interaction is an essential step in fertilization. In amphibians, the molecules involved have not been identified. Our aim was to detect and characterize oocyte molecules with binding affinity for sperm. We isolated plasma membranes free from vitelline envelope and yolk proteins from surface-biotinylated Bufo arenarum oocytes. Using binding assays we detected a biotinylated 100 kDa plasma membrane protein that consistently bound to sperm. Chromatographic studies confirmed the 100 kDa protein and detected two additional oocyte molecules of 30 and 70 kDa with affinity for sperm. Competition studies with an integrin-interacting peptide and cross-reaction with an anti-HSP70 antibody suggested that the 100 and 70 kDa proteins are members of the integrin family and HSP70, respectively. MS/MS analysis suggested extra candidates for a role in this step of fertilization. In conclusion, we provide evidence for the involvement of several proteins, including integrins and HSP70, in B. arenarum sperm-oocyte plasma membrane interactions.

Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction.

PubMed

Uddin, Reaz; Tariq, Syeda Sumayya; Azam, Syed Sikander; Wadood, Abdul; Moin, Syed Tarique

2017-08-30

Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well. Copyright © 2017 Elsevier B.V. All rights reserved.

High throughput protein production screening

DOEpatents

Beernink, Peter T [Walnut Creek, CA; Coleman, Matthew A [Oakland, CA; Segelke, Brent W [San Ramon, CA

2009-09-08

Methods, compositions, and kits for the cell-free production and analysis of proteins are provided. The invention allows for the production of proteins from prokaryotic sequences or eukaryotic sequences, including human cDNAs using PCR and IVT methods and detecting the proteins through fluorescence or immunoblot techniques. This invention can be used to identify optimized PCR and WT conditions, codon usages and mutations. The methods are readily automated and can be used for high throughput analysis of protein expression levels, interactions, and functional states.

Network Analysis Reveals the Recognition Mechanism for Mannose-binding Lectins

NASA Astrophysics Data System (ADS)

Zhao, Yunjie; Jian, Yiren; Zeng, Chen; Computational Biophysics Lab Team

The specific carbohydrate binding of mannose-binding lectin (MBL) protein in plants makes it a very useful molecular tool for cancer cell detection and other applications. The biological states of most MBL proteins are dimeric. Using dynamics network analysis on molecular dynamics (MD) simulations on the model protein of MBL, we elucidate the short- and long-range driving forces behind the dimer formation. The results are further supported by sequence coevolution analysis. We propose a general framework for deciphering the recognition mechanism underlying protein-protein interactions that may have potential applications in signaling pathways.

"Fuzziness" in the celular interactome: a historical perspective.

PubMed

Welch, G Rickey

2012-01-01

Some historical background is given for appreciating the impact of the empirical construct known as the cellular protein-protein interactome, which is a seemingly de novo entity that has arisen of late within the context of postgenomic systems biology. The approach here builds on a generalized principle of "fuzziness" in protein behavior, proposed by Tompa and Fuxreiter.(1) Recent controversies in the analysis and interpretation of the interactome studies are rationalized historically under the auspices of this concept. There is an extensive literature on protein-protein interactions, dating to the mid-1900s, which may help clarify the "fuzziness" in the interactome picture and, also, provide a basis for understanding the physiological importance of protein-protein interactions in vivo.

"Features of two proteins of Leptospira interrogans with potential role in host-pathogen interactions"

PubMed Central

2012-01-01

Background Leptospirosis is considered a re-emerging infectious disease caused by pathogenic spirochaetes of the genus Leptospira. Pathogenic leptospires have the ability to survive and disseminate to multiple organs after penetrating the host. Leptospires were shown to express surface proteins that interact with the extracellular matrix (ECM) and to plasminogen (PLG). This study examined the interaction of two putative leptospiral proteins with laminin, collagen Type I, collagen Type IV, cellular fibronectin, plasma fibronectin, PLG, factor H and C4bp. Results We show that two leptospiral proteins encoded by LIC11834 and LIC12253 genes interact with laminin in a dose - dependent and saturable mode, with dissociation equilibrium constants (KD) of 367.5 and 415.4 nM, respectively. These proteins were named Lsa33 and Lsa25 (Leptospiral surface adhesin) for LIC11834 and LIC12253, respectively. Metaperiodate - treated laminin reduced Lsa25 - laminin interaction, suggesting that sugar moieties of this ligand participate in this interaction. The Lsa33 is also PLG - binding receptor, with a KD of 23.53 nM, capable of generating plasmin in the presence of an activator. Although in a weak manner, both proteins interact with C4bp, a regulator of complement classical route. In silico analysis together with proteinase K and immunoflorescence data suggest that these proteins might be surface exposed. Moreover, the recombinant proteins partially inhibited leptospiral adherence to immobilized laminin and PLG. Conclusions We believe that these multifunctional proteins have the potential to participate in the interaction of leptospires to hosts by mediating adhesion and by helping the bacteria to escape the immune system and to overcome tissue barriers. To our knowledge, Lsa33 is the first leptospiral protein described to date with the capability of binding laminin, PLG and C4bp in vitro. PMID:22463075

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology.

PubMed

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-03-22

A protein complex consists of two or more proteins that are linked together through protein-protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples.

Proteomic Identification and Analysis of Arginine-Methylated Proteins of Plasmodium falciparum at Asexual Blood Stages.

PubMed

Zeeshan, Mohammad; Kaur, Inderjeet; Joy, Joseph; Saini, Ekta; Paul, Gourab; Kaushik, Abhinav; Dabral, Surbhi; Mohmmed, Asif; Gupta, Dinesh; Malhotra, Pawan

2017-02-03

Plasmodium falciparum undergoes a tightly regulated developmental process in human erythrocytes, and recent studies suggest an important regulatory role of post-translational modifications (PTMs). As compared with Plasmodium phosphoproteome, little is known about other PTMs in the parasite. In the present study, we performed a global analysis of asexual blood stages of Plasmodium falciparum to identify arginine-methylated proteins. Using two different methyl arginine-specific antibodies, we immunoprecipitated the arginine-methylated proteins from the stage-specific parasite lysates and identified 843 putative arginine-methylated proteins by LC-MS/MS. Motif analysis of the protein sequences unveiled that the methylation sites are associated with the previously known methylation motifs such as GRx/RGx, RxG, GxxR, or WxxxR. We identified Plasmodium homologues of known arginine-methylated proteins in trypanosomes, yeast, and human. Hydrophilic interaction liquid chromatography (HILIC) was performed on the immunoprecipitates from the trophozoite stage to enrich arginine-methylated peptides. Mass spectrometry analysis of immunoprecipitated and HILIC fractions identified 55 arginine-methylated peptides having 62 methylated arginine sites. Functional classification revealed that the arginine-methylated proteins are involved in RNA metabolism, protein synthesis, intracellular protein trafficking, proteolysis, protein folding, chromatin organization, hemoglobin metabolic process, and several other functions. Summarily, the findings suggest that protein methylation of arginine residues is a widespread phenomenon in Plasmodium, and the PTM may play an important regulatory role in a diverse set of biological pathways, including host-pathogen interactions.

Switching assay as a novel approach for specific antigen- antibody interaction analysis using magnetic nanoparticles

NASA Astrophysics Data System (ADS)

Parr, M.; Illarionov, R.; Marchenko, Y.; Yakovleva, L.; Nikolaev, B.; Ischenko, A.; Shevtsov, M.

2016-08-01

Switching assay was applied for the detection of antigen-antibody interaction between 70-kDa heat shock protein (Hsp70) and anti-Hsp70 monoclonal antibodies in water solutions using conjugates with magnetic iron oxide nanoparticles (MNPs). Hsp70 is a ubiquitous intracellular protein that plays a crucial role in cancerogenesis and many other pathologies. Detection of the Hsp70 level in the biological fluids might have a prognostic and diagnostic value in clinic. The developed switch assay for the detection of Hsp70 demonstrated high sensitivity for antigen-antibody interaction analysis thus proving its potential for further preclinical and clinical studies.

Voroprot: an interactive tool for the analysis and visualization of complex geometric features of protein structure.

PubMed

Olechnovic, Kliment; Margelevicius, Mindaugas; Venclovas, Ceslovas

2011-03-01

We present Voroprot, an interactive cross-platform software tool that provides a unique set of capabilities for exploring geometric features of protein structure. Voroprot allows the construction and visualization of the Apollonius diagram (also known as the additively weighted Voronoi diagram), the Apollonius graph, protein alpha shapes, interatomic contact surfaces, solvent accessible surfaces, pockets and cavities inside protein structure. Voroprot is available for Windows, Linux and Mac OS X operating systems and can be downloaded from http://www.ibt.lt/bioinformatics/voroprot/.

Complex network theory for the identification and assessment of candidate protein targets.

PubMed

McGarry, Ken; McDonald, Sharon

2018-06-01

In this work we use complex network theory to provide a statistical model of the connectivity patterns of human proteins and their interaction partners. Our intention is to identify important proteins that may be predisposed to be potential candidates as drug targets for therapeutic interventions. Target proteins usually have more interaction partners than non-target proteins, but there are no hard-and-fast rules for defining the actual number of interactions. We devise a statistical measure for identifying hub proteins, we score our target proteins with gene ontology annotations. The important druggable protein targets are likely to have similar biological functions that can be assessed for their potential therapeutic value. Our system provides a statistical analysis of the local and distant neighborhood protein interactions of the potential targets using complex network measures. This approach builds a more accurate model of drug-to-target activity and therefore the likely impact on treating diseases. We integrate high quality protein interaction data from the HINT database and disease associated proteins from the DrugTarget database. Other sources include biological knowledge from Gene Ontology and drug information from DrugBank. The problem is a very challenging one since the data is highly imbalanced between target proteins and the more numerous nontargets. We use undersampling on the training data and build Random Forest classifier models which are used to identify previously unclassified target proteins. We validate and corroborate these findings from the available literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Protein-Protein Interface and Disease: Perspective from Biomolecular Networks.

PubMed

Hu, Guang; Xiao, Fei; Li, Yuqian; Li, Yuan; Vongsangnak, Wanwipa

Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.

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

Lipid vesicle-mediated affinity chromatography using magnetic activated cell sorting (LIMACS): a novel method to analyze protein-lipid interaction.

PubMed

Bieberich, Erhard

2011-04-26

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification procedures. As an alternative, lipids can be coated on flat surfaces as used for lipid ELISA and Plasmon resonance spectroscopy. However, surface coating lipids do not form microdomain structures, which may be important for the lipid binding properties. Further, these methods do not allow for the purification of larger amounts of proteins binding to their target lipids. To overcome these limitations of testing lipid protein interaction and to purify lipid binding proteins we developed a novel method termed lipid vesicle-mediated affinity chromatography using magnetic-activated cell sorting (LIMACS). In this method, lipid vesicles are prepared with the target lipid and phosphatidylserine as the anchor lipid for Annexin V MACS. Phosphatidylserine is a ubiquitous cell membrane phospholipid that shows high affinity to the protein Annexin V. Using magnetic beads conjugated to Annexin V the phosphatidylserine-containing lipid vesicles will bind to the magnetic beads. When the lipid vesicles are incubated with a cell lysate the protein binding to the target lipid will also be bound to the beads and can be co-purified using MACS. This method can also be used to test if recombinant proteins reconstitute a protein complex binding to the target lipid. We have used this method to show the interaction of atypical PKC (aPKC) with the sphingolipid ceramide and to co-purify prostate apoptosis response 4 (PAR-4), a protein binding to ceramide-associated aPKC. We have also used this method for the reconstitution of a ceramide-associated complex of recombinant aPKC with the cell polarity-related proteins Par6 and Cdc42. Since lipid vesicles can be prepared with a variety of sphingo- or phospholipids, LIMACS offers a versatile test for lipid-protein interaction in a lipid environment that resembles closely that of the cell membrane. Additional lipid protein complexes can be identified using proteomics analysis of lipid binding protein co-purified with the lipid vesicles.

3D RNA and functional interactions from evolutionary couplings

PubMed Central

Weinreb, Caleb; Riesselman, Adam; Ingraham, John B.; Gross, Torsten; Sander, Chris; Marks, Debora S.

2016-01-01

Summary Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA. PMID:27087444

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

PubMed Central

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-01-01

A protein complex consists of two or more proteins that are linked together through protein–protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples. PMID:27011181

Identification, RNAi Knockdown and Functional Analysis of an Ejaculate Protein that Mediates a Postmating, Prezgotic Phenotype in a cricket

USDA-ARS?s Scientific Manuscript database

Male ejaculate proteins, including both sperm and seminal fluid proteins, play an important role in mediating reproductive biology. The function of ejaculate proteins can include enabling sperm-egg interactions, enhancing sperm storage, mediating female attractiveness, and even regulating female lif...

Proteomic analysis of protein interactions between Eimeria maxima sporozoites and chicken jejunal epithelial cells by shotgun LC-MS/MS.

PubMed

Huang, Jingwei; Liu, Tingqi; Li, Ke; Song, Xiaokai; Yan, Ruofeng; Xu, Lixin; Li, Xiangrui

2018-04-04

Eimeria maxima initiates infection by invading the jejunal epithelial cells of chicken. However, the proteins involved in invasion remain unknown. The research of the molecules that participate in the interactions between E. maxima sporozoites and host target cells will fill a gap in our understanding of the invasion system of this parasitic pathogen. In the present study, chicken jejunal epithelial cells were isolated and cultured in vitro. Western blot was employed to analyze the soluble proteins of E. maxima sporozoites that bound to chicken jejunal epithelial cells. Co-immunoprecipitation (co-IP) assay was used to separate the E. maxima proteins that bound to chicken jejunal epithelial cells. Shotgun LC-MS/MS technique was used for proteomics identification and Gene Ontology was employed for the bioinformatics analysis. The results of Western blot analysis showed that four proteins bands from jejunal epithelial cells co-cultured with soluble proteins of E. maxima sporozoites were recognized by the positive sera, with molecular weights of 70, 90, 95 and 130 kDa. The co-IP dilutions were analyzed by shotgun LC-MS/MS. A total of 204 proteins were identified in the E. maxima protein database using the MASCOT search engine. Thirty-five proteins including microneme protein 3 and 7 had more than two unique peptide counts and were annotated using Gene Ontology for molecular function, biological process and cellular localization. The results revealed that of the 35 annotated peptides, 22 (62.86%) were associated with binding activity and 15 (42.86%) were involved in catalytic activity. Our findings provide an insight into the interaction between E. maxima and the corresponding host cells and it is important for the understanding of molecular mechanisms underlying E. maxima invasion.

3Drefine: an interactive web server for efficient protein structure refinement

PubMed Central

Bhattacharya, Debswapna; Nowotny, Jackson; Cao, Renzhi; Cheng, Jianlin

2016-01-01

3Drefine is an interactive web server for consistent and computationally efficient protein structure refinement with the capability to perform web-based statistical and visual analysis. The 3Drefine refinement protocol utilizes iterative optimization of hydrogen bonding network combined with atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields for efficient protein structure refinement. The method has been extensively evaluated on blind CASP experiments as well as on large-scale and diverse benchmark datasets and exhibits consistent improvement over the initial structure in both global and local structural quality measures. The 3Drefine web server allows for convenient protein structure refinement through a text or file input submission, email notification, provided example submission and is freely available without any registration requirement. The server also provides comprehensive analysis of submissions through various energy and statistical feedback and interactive visualization of multiple refined models through the JSmol applet that is equipped with numerous protein model analysis tools. The web server has been extensively tested and used by many users. As a result, the 3Drefine web server conveniently provides a useful tool easily accessible to the community. The 3Drefine web server has been made publicly available at the URL: http://sysbio.rnet.missouri.edu/3Drefine/. PMID:27131371

PNMA family: Protein interaction network and cell signalling pathways implicated in cancer and apoptosis.

PubMed

Pang, Siew Wai; Lahiri, Chandrajit; Poh, Chit Laa; Tan, Kuan Onn

2018-05-01

Paraneoplastic Ma Family (PNMA) comprises a growing number of family members which share relatively conserved protein sequences encoded by the human genome and is localized to several human chromosomes, including the X-chromosome. Based on sequence analysis, PNMA family members share sequence homology to the Gag protein of LTR retrotransposon, and several family members with aberrant protein expressions have been reported to be closely associated with the human Paraneoplastic Disorder (PND). In addition, gene mutations of specific members of PNMA family are known to be associated with human mental retardation or 3-M syndrome consisting of restrictive post-natal growth or dwarfism, and development of skeletal abnormalities. Other than sequence homology, the physiological function of many members in this family remains unclear. However, several members of this family have been characterized, including cell signalling events mediated by these proteins that are associated with apoptosis, and cancer in different cell types. Furthermore, while certain PNMA family members show restricted gene expression in the human brain and testis, other PNMA family members exhibit broader gene expression or preferential and selective protein interaction profiles, suggesting functional divergence within the family. Functional analysis of some members of this family have identified protein domains that are required for subcellular localization, protein-protein interactions, and cell signalling events which are the focus of this review paper. Copyright © 2018 Elsevier Inc. All rights reserved.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics.

PubMed

Labbé, Céline M; Pencheva, Tania; Jereva, Dessislava; Desvillechabrol, Dimitri; Becot, Jérôme; Villoutreix, Bruno O; Pajeva, Ilza; Miteva, Maria A

2017-07-03

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Proteins that interact with calgranulin B in the human colon cancer cell line HCT-116.

PubMed

Myung, Jae Kyung; Yeo, Seung-Gu; Kim, Kyung Hee; Baek, Kwang-Soo; Shin, Daye; Kim, Jong Heon; Cho, Jae Youl; Yoo, Byong Chul

2017-01-24

Calgranulin B is released from immune cells and can be internalized into colon cancer cells to prevent proliferation. The present study aimed to identify proteins that interact with calgranulin B to suppress the proliferation of colon cancer cells, and to obtain information on the underlying anti-tumor mechanism(s) of calgranulin B. Calgranulin B expression was induced in colon cancer cell line HCT-116 by infection with calgranulin B-FLAG expressing lentivirus, and it led to a significant suppression of cell proliferation. Proteins that interacted with calgranulin B were obtained by immunoprecipitation using whole homogenate of lentivirus-infected HCT-116 cells which expressing calgranulin B-FLAG, and identified using liquid chromatography-mass spectrometry/mass spectrometry analysis. A total of 454 proteins were identified that potentially interact with calgranulin B, and most identified proteins were associated with RNA processing, post-transcriptional modifications and the EIF2 signaling pathway. Direct interaction of calgranulin B with flotillin-1, dynein intermediate chain 1, and CD59 glycoprotein has been confirmed, and the molecules N-myc proto-oncogene protein, rapamycin-insensitive companion of mTOR, and myc proto-oncogene protein were shown to regulate calgranulin B-interacting proteins. Our results provide new insight and useful information to explain the possible mechanism(s) underlying the role of calgranulin B as an anti-tumor effector in colon cancer cells.

Proteomic analysis of the compatible interaction of wheat and powdery mildew (Blumeria graminis f. sp. tritici).

PubMed

Li, Jie; Yang, Xiwen; Liu, Xinhao; Yu, Haibo; Du, Congyang; Li, Mengda; He, Dexian

2017-02-01

Proteome characteristics of wheat leaves with the powdery mildew pathogen Blumeria graminis f. sp. tritici (Bgt) infection were investigated by two-dimensional electrophoresis and tandem MALDI-TOF/TOF-MS. We identified 46 unique proteins which were differentially expressed at 24, 48, and 72 h post-inoculation. The functional classification of these proteins showed that most of them were involved in photosynthesis, carbohydrate and nitrogen metabolism, defense responses, and signal transduction. Upregulated proteins included primary metabolism pathways and defense responses, while proteins related to photosynthesis and signal transduction were mostly downregulated. As expected, more antioxidative proteins were activated at the later infection stage than the earlier stage, suggesting that the antioxidative system of host plays a role in maintaining the compatible interaction between wheat and powdery mildew. A high accumulation of 6-phosphogluconate dehydrogenase and isocitrate dehydrogenase in infected leaves indicated the regulation of the TCA cycle and pentose phosphate pathway in parallel to the activation of host defenses. The downregulation of MAPK5 could be facilitated for the compatible interaction of wheat plants and Bgt. qRT-PCR analysis supported the data of protein expression profiles. Our results reveal the relevance of primary plant metabolism and defense responses during compatible interaction, and provide new insights into the biology of susceptible wheat in response to Bgt infection. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Functional analysis of the Hikeshi-like protein and its interaction with HSP70 in Arabidopsis

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

Koizumi, Shinya; Ohama, Naohiko; Mizoi, Junya

2014-07-18

Highlights: • HKL, a Hikeshi homologous gene is identified in Arabidopsis. • HKL interacts with two HSP70 isoforms and regulates the subcellular localization of HSC70-1. • The two HSP70 translocate into nucleus in response to heat stress. • Overexpression of HKL confers thermotolerance in transgenic plants. - Abstract: Heat shock proteins (HSPs) refold damaged proteins and are an essential component of the heat shock response. Previously, the 70 kDa heat shock protein (HSP70) has been reported to translocate into the nucleus in a heat-dependent manner in many organisms. In humans, the heat-induced translocation of HSP70 requires the nuclear carrier proteinmore » Hikeshi. In the Arabidopsis genome, only one gene encodes a protein with high homology to Hikeshi, and we named this homolog Hikeshi-like (HKL) protein. In this study, we show that two Arabidopsis HSP70 isoforms accumulate in the nucleus in response to heat shock and that HKL interacts with these HSP70s. Our histochemical analysis revealed that HKL is predominantly expressed in meristematic tissues, suggesting the potential importance of HKL during cell division in Arabidopsis. In addition, we show that HKL regulates HSP70 localization, and HKL overexpression conferred thermotolerance to transgenic Arabidopsis plants. Our results suggest that HKL plays a positive role in the thermotolerance of Arabidopsis plants and cooperatively interacts with HSP70.« less

Direct optical detection of protein-ligand interactions.

PubMed

Gesellchen, Frank; Zimmermann, Bastian; Herberg, Friedrich W

2005-01-01

Direct optical detection provides an excellent means to investigate interactions of molecules in biological systems. The dynamic equilibria inherent to these systems can be described in greater detail by recording the kinetics of a biomolecular interaction. Optical biosensors allow direct detection of interaction patterns without the need for labeling. An overview covering several commercially available biosensors is given, with a focus on instruments based on surface plasmon resonance (SPR) and reflectometric interference spectroscopy (RIFS). Potential assay formats and experimental design, appropriate controls, and calibration procedures, especially when handling low molecular weight substances, are discussed. The single steps of an interaction analysis combined with practical tips for evaluation, data processing, and interpretation of kinetic data are described in detail. In a practical example, a step-by-step procedure for the analysis of a low molecular weight compound interaction with serum protein, determined on a commercial SPR sensor, is presented.

Characterization of Native Protein Complexes and Protein Isoform Variation Using Size-fractionation-based Quantitative Proteomics*

PubMed Central

Kirkwood, Kathryn J.; Ahmad, Yasmeen; Larance, Mark; Lamond, Angus I.

2013-01-01

Proteins form a diverse array of complexes that mediate cellular function and regulation. A largely unexplored feature of such protein complexes is the selective participation of specific protein isoforms and/or post-translationally modified forms. In this study, we combined native size-exclusion chromatography (SEC) with high-throughput proteomic analysis to characterize soluble protein complexes isolated from human osteosarcoma (U2OS) cells. Using this approach, we have identified over 71,500 peptides and 1,600 phosphosites, corresponding to over 8,000 proteins, distributed across 40 SEC fractions. This represents >50% of the predicted U2OS cell proteome, identified with a mean peptide sequence coverage of 27% per protein. Three biological replicates were performed, allowing statistical evaluation of the data and demonstrating a high degree of reproducibility in the SEC fractionation procedure. Specific proteins were detected interacting with multiple independent complexes, as typified by the separation of distinct complexes for the MRFAP1-MORF4L1-MRGBP interaction network. The data also revealed protein isoforms and post-translational modifications that selectively associated with distinct subsets of protein complexes. Surprisingly, there was clear enrichment for specific Gene Ontology terms associated with differential size classes of protein complexes. This study demonstrates that combined SEC/MS analysis can be used for the system-wide annotation of protein complexes and to predict potential isoform-specific interactions. All of these SEC data on the native separation of protein complexes have been integrated within the Encyclopedia of Proteome Dynamics, an online, multidimensional data-sharing resource available to the community. PMID:24043423

Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics.

PubMed

Kirkwood, Kathryn J; Ahmad, Yasmeen; Larance, Mark; Lamond, Angus I

2013-12-01

Proteins form a diverse array of complexes that mediate cellular function and regulation. A largely unexplored feature of such protein complexes is the selective participation of specific protein isoforms and/or post-translationally modified forms. In this study, we combined native size-exclusion chromatography (SEC) with high-throughput proteomic analysis to characterize soluble protein complexes isolated from human osteosarcoma (U2OS) cells. Using this approach, we have identified over 71,500 peptides and 1,600 phosphosites, corresponding to over 8,000 proteins, distributed across 40 SEC fractions. This represents >50% of the predicted U2OS cell proteome, identified with a mean peptide sequence coverage of 27% per protein. Three biological replicates were performed, allowing statistical evaluation of the data and demonstrating a high degree of reproducibility in the SEC fractionation procedure. Specific proteins were detected interacting with multiple independent complexes, as typified by the separation of distinct complexes for the MRFAP1-MORF4L1-MRGBP interaction network. The data also revealed protein isoforms and post-translational modifications that selectively associated with distinct subsets of protein complexes. Surprisingly, there was clear enrichment for specific Gene Ontology terms associated with differential size classes of protein complexes. This study demonstrates that combined SEC/MS analysis can be used for the system-wide annotation of protein complexes and to predict potential isoform-specific interactions. All of these SEC data on the native separation of protein complexes have been integrated within the Encyclopedia of Proteome Dynamics, an online, multidimensional data-sharing resource available to the community.

A Protein Interaction Map of the Kalimantacin Biosynthesis Assembly Line

PubMed Central

Uytterhoeven, Birgit; Lathouwers, Thomas; Voet, Marleen; Michiels, Chris W.; Lavigne, Rob

2016-01-01

The antimicrobial secondary metabolite kalimantacin (also called batumin) is produced by a hybrid polyketide/non-ribosomal peptide system in Pseudomonas fluorescens BCCM_ID9359. In this study, the kalimantacin biosynthesis gene cluster is analyzed by yeast two-hybrid analysis, creating a protein–protein interaction map of the entire assembly line. In total, 28 potential interactions were identified, of which 13 could be confirmed further. These interactions include the dimerization of ketosynthase domains, a link between assembly line modules 9 and 10, and a specific interaction between the trans-acting enoyl reductase BatK and the carrier proteins of modules 8 and 10. These interactions reveal fundamental insight into the biosynthesis of secondary metabolites. This study is the first to reveal interactions in a complete biosynthetic pathway. Similar future studies could build a strong basis for engineering strategies in such clusters. PMID:27853452

Functional Interaction Map of Lyssavirus Phosphoprotein: Identification of the Minimal Transcription Domains

PubMed Central

Jacob, Yves; Real, Eléonore; Tordo, Noël

2001-01-01

Lyssaviruses, the causative agents of rabies encephalitis, are distributed in seven genotypes. The phylogenetically distant rabies virus (PV strain, genotype 1) and Mokola virus (genotype 3) were used to develop a strategy to identify functional homologous interactive domains from two proteins (P and N) which participate in the viral ribonucleoprotein (RNP) transcription-replication complex. This strategy combined two-hybrid and green fluorescent protein–reverse two-hybrid assays in Saccharomyces cerevisiae to analyze protein-protein interactions and a reverse genetic assay in mammalian cells to study the transcriptional activity of the reconstituted RNP complex. Lyssavirus P proteins contain two N-binding domains (N-BDs), a strong one encompassing amino acid (aa) 176 to the C terminus and a weak one in the 189 N-terminal aa. The N-terminal portion of P (aa 52 to 189) also contains a homomultimerization site. Here we demonstrate that N-P interactions, although weaker, are maintained between proteins of the different genotypes. A minimal transcriptional module of the P protein was obtained by fusing the first 60 N-terminal aa containing the L protein binding site to the C-terminal strong N-BD. Random mutation of the strong N-BD on P protein identified three highly conserved K residues crucial for N-P interaction. Their mutagenesis in full-length P induced a transcriptionally defective RNP. The analysis of homologous interactive domains presented here and previously reported dissections of the P protein allowed us to propose a model of the functional interaction network of the lyssavirus P protein. This model underscores the central role of P at the interface between L protein and N-RNA template. PMID:11559793

Exploring the interactome: microfluidic isolation of proteins and interacting partners for quantitative analysis by electron microscopy.

PubMed

Giss, Dominic; Kemmerling, Simon; Dandey, Venkata; Stahlberg, Henning; Braun, Thomas

2014-05-20

Multimolecular protein complexes are important for many cellular processes. However, the stochastic nature of the cellular interactome makes the experimental detection of complex protein assemblies difficult and quantitative analysis at the single molecule level essential. Here, we present a fast and simple microfluidic method for (i) the quantitative isolation of endogenous levels of untagged protein complexes from minute volumes of cell lysates under close to physiological conditions and (ii) the labeling of specific components constituting these complexes. The method presented uses specific antibodies that are conjugated via a photocleavable linker to magnetic beads that are trapped in microcapillaries to immobilize the target proteins. Proteins are released by photocleavage, eluted, and subsequently analyzed by quantitative transmission electron microscopy at the single molecule level. Additionally, before photocleavage, immunogold can be employed to label proteins that interact with the primary target protein. Thus, the presented method provides a new way to study the interactome and, in combination with single molecule transmission electron microscopy, to structurally characterize the large, dynamic, heterogeneous multimolecular protein complexes formed.

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

Chlamydia trachomatis protein CT009 is a structural and functional homolog to the key morphogenesis component RodZ and interacts with division septal plane localized MreB

DOE PAGES

Kemege, Kyle E.; Hickey, John M.; Barta, Michael L.; ...

2014-11-10

Cell division in Chlamydiae is poorly understood as apparent homologs to most conserved bacterial cell division proteins are lacking and presence of elongation (rod shape) associated proteins indicate non-canonical mechanisms may be employed. The rod-shape determining protein MreB has been proposed as playing a unique role in chlamydial cell division. In other organisms, MreB is part of an elongation complex that requires RodZ for proper function. A recent study reported that the protein encoded by ORF CT009 interacts with MreB despite low sequence similarity to RodZ. The studies in this paper expand on those observations through protein structure, mutagenesis andmore » cellular localization analyses. Structural analysis indicated that CT009 shares high level of structural similarity to RodZ, revealing the conserved orientation of two residues critical for MreB interaction. Substitutions eliminated MreB protein interaction and partial complementation provided by CT009 in RodZ deficient Escherichia coli. Cellular localization analysis of CT009 showed uniform membrane staining in Chlamydia. This was in contrast to the localization of MreB, which was restricted to predicted septal planes. Finally, MreB localization to septal planes provides direct experimental observation for the role of MreB in cell division and supports the hypothesis that it serves as a functional replacement for FtsZ in Chlamydia.« less

Chlamydia trachomatis protein CT009 is a structural and functional homolog to the key morphogenesis component RodZ and interacts with division septal plane localized MreB

PubMed Central

Kemege, Kyle E.; Hickey, John M.; Barta, Michael L.; Wickstrum, Jason; Balwalli, Namita; Lovell, Scott; Battaile, Kevin P.; Hefty, P. Scott

2015-01-01

Summary Cell division in Chlamydiae is poorly understood as apparent homologs to most conserved bacterial cell division proteins are lacking and presence of elongation (rod shape) associated proteins indicate non-canonical mechanisms may be employed. The rod-shape determining protein MreB has been proposed as playing a unique role in chlamydial cell division. In other organisms, MreB is part of an elongation complex that requires RodZ for proper function. A recent study reported that the protein encoded by ORF CT009 interacts with MreB despite low sequence similarity to RodZ. The studies herein expand on those observations through protein structure, mutagenesis, and cellular localization analyses. Structural analysis indicated that CT009 shares high level of structural similarity to RodZ, revealing the conserved orientation of two residues critical for MreB interaction. Substitutions eliminated MreB protein interaction and partial complementation provided by CT009 in RodZ deficient E. coli. Cellular localization analysis of CT009 showed uniform membrane staining in Chlamydia. This was in contrast to the localization of MreB, which was restricted to predicted septal planes. MreB localization to septal planes provides direct experimental observation for the role of MreB in cell division and supports the hypothesis that it serves as a functional replacement for FtsZ in Chlamydia. PMID:25382739

Chlamydia trachomatis protein CT009 is a structural and functional homolog to the key morphogenesis component RodZ and interacts with division septal plane localized MreB.

PubMed

Kemege, Kyle E; Hickey, John M; Barta, Michael L; Wickstrum, Jason; Balwalli, Namita; Lovell, Scott; Battaile, Kevin P; Hefty, P Scott

2015-02-01

Cell division in Chlamydiae is poorly understood as apparent homologs to most conserved bacterial cell division proteins are lacking and presence of elongation (rod shape) associated proteins indicate non-canonical mechanisms may be employed. The rod-shape determining protein MreB has been proposed as playing a unique role in chlamydial cell division. In other organisms, MreB is part of an elongation complex that requires RodZ for proper function. A recent study reported that the protein encoded by ORF CT009 interacts with MreB despite low sequence similarity to RodZ. The studies herein expand on those observations through protein structure, mutagenesis and cellular localization analyses. Structural analysis indicated that CT009 shares high level of structural similarity to RodZ, revealing the conserved orientation of two residues critical for MreB interaction. Substitutions eliminated MreB protein interaction and partial complementation provided by CT009 in RodZ deficient Escherichia coli. Cellular localization analysis of CT009 showed uniform membrane staining in Chlamydia. This was in contrast to the localization of MreB, which was restricted to predicted septal planes. MreB localization to septal planes provides direct experimental observation for the role of MreB in cell division and supports the hypothesis that it serves as a functional replacement for FtsZ in Chlamydia. © 2014 John Wiley & Sons Ltd.

Antioxidant activity and protein-polyphenol interactions in a pomegranate (Punica granatum L.) yogurt.

PubMed

Trigueros, Lorena; Wojdyło, Aneta; Sendra, Esther

2014-07-09

Pomegranate juice (PGJ) is rich in phenolics which are potent antioxidants but also prone to interact with proteins. A yogurt rich in PGJ (40%) made from arils was elaborated (PGY) to determine the antioxidant activity and to estimate the phenolics-proteins interaction during 28 days of cold storage. Juice, yogurts, and protein-free permeates were analyzed for phenolic composition. Yogurt fermentation modified the anthocyanin profile of the initial PGJ, especially the content in cyanidin-3-O-glucoside. During storage, individual anthocyanin content in PGY decreased but it did not modify yogurt color. The analysis of permeates revealed that the degree of phenol-protein interaction depends on the type of phenolic, ellagic acid and dephinidin-3,5-O-diglucoside being the least bound phenolic compounds. The presence of PGJ in yogurt enhanced radical scavenging performance, whereas all the observed ferric reducing power ability of PGY was strictly due to the PGJ present. The 84.73% of total anthocyanins remained bound to proteins at the first day of storage and 90.06% after 28 days of cold storage, revealing the high affinity of anthocyanins for milk proteins.

Proteome-wide analysis of Anopheles culicifacies mosquito midgut: new insights into the mechanism of refractoriness.

PubMed

Vijay, Sonam; Rawal, Ritu; Kadian, Kavita; Singh, Jagbir; Adak, Tridibesh; Sharma, Arun

2018-05-08

Midgut invasion, a major bottleneck for malaria parasites transmission is considered as a potential target for vector-parasite interaction studies. New intervention strategies are required to explore the midgut proteins and their potential role in refractoriness for malaria control in Anopheles mosquitoes. To better understand the midgut functional proteins of An. culicifacies susceptible and refractory species, proteomic approaches coupled with bioinformatics analysis is an effective means in order to understand the mechanism of refractoriness. In the present study, an integrated in solution- in gel trypsin digestion approach, along with Isobaric tag for relative and absolute quantitation (iTRAQ)-Liquid chromatography/Mass spectrometry (LC/MS/MS) and data mining were performed to identify the proteomic profile and differentially expressed proteins in Anopheles culicifacies susceptible species A and refractory species B. Shot gun proteomics approaches led to the identification of 80 proteins in An. culicifacies susceptible species A and 92 in refractory species B and catalogue was prepared. iTRAQ based proteomic analysis identified 48 differentially expressed proteins from total 130 proteins. Of these, 41 were downregulated and 7 were upregulated in refractory species B in comparison to susceptible species A. We report that the altered midgut proteins identified in naturally refractory mosquitoes are involved in oxidative phosphorylation, antioxidant and proteolysis process that may suggest their role in parasite growth inhibition. Furthermore, real time polymerase chain reaction (PCR) analysis of few proteins indicated higher expression of iTRAQ upregulated protein in refractory species than susceptible species. This study elucidates the first proteome of the midguts of An. culicifacies sibling species that attempts to analyze unique proteogenomic interactions to provide insights for better understanding of the mechanism of refractoriness. Functional implications of these upregulated proteins in refractory species may reflect the phenotypic characteristics of the mosquitoes and will improve our understandings of blood meal digestion process, parasite vector interactions and proteomes of other vectors of human diseases for development of novel vector control strategies.

Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks

PubMed Central

2014-01-01

Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226

Proteomic analysis of physiological function response to hot summer in liver from lactating dairy cows.

PubMed

Wang, Qiangjun; Zhao, Xiaowei; Zhang, Zijun; Zhao, Huiling; Huang, Dongwei; Cheng, Guanglong; Yang, Yongxin

2017-04-01

Lactation performance of dairy cattle is susceptible to heat stress. The liver is one of the most crucial organs affected by high temperature in dairy cows. However, the physiological adaption by the liver to hot summer conditions has not been well elucidated in lactating dairy cows. In the present study, proteomic analysis of the liver in dairy cows in spring and hot summer was performed using a label-free method. In total, 127 differentially expressed proteins were identified; most of the upregulated proteins were involved in protein metabolic processes and responses to stimuli, whereas most of the downregulated proteins were related to oxidation-reduction. Pathway analysis indicated that 3 upregulated heat stress proteins (HSP90α, HSP90β, and endoplasmin) were enriched in the NOD-like receptor signaling pathway, whereas several downregulated NADH dehydrogenase proteins were involved in the oxidative phosphorylation pathway. The protein-protein interaction network indicated that several upregulated HSPs (HSP90α, HSP90β, and GRP78) were involved in more interactions than other proteins and were thus considered as central hub nodes. Our findings provide novel insights into the physiological adaption of liver function in lactating dairy cows to natural high temperature. Copyright © 2017. Published by Elsevier Ltd.

Impact of surface coating and food-mimicking media on nanosilver-protein interaction

NASA Astrophysics Data System (ADS)

Burcza, Anna; Gräf, Volker; Walz, Elke; Greiner, Ralf

2015-11-01

The application of silver nanoparticles (AgNPs) in food contact materials has recently become a subject of dispute due to the possible migration of silver in nanoform into foods and beverages. Therefore, the analysis of the interaction of AgNPs with food components, especially proteins, is of high importance in order to increase our knowledge of the behavior of nanoparticles in food matrices. AgPURE™ W10 (20 nm), an industrially applied nanomaterial, was compared with AgNPs of similar size frequently investigated for scientific purposes differing in the surface capping agent (spherical AgNP coated with either PVP or citrate). The interactions of the AgNPs with whey proteins (BSA, α-lactalbumin and β-lactoglobulin) at different pH values (4.2, 7 or 7.4) were investigated using surface plasmon resonance, SDS-PAGE, and asymmetric flow field-flow fractionation. The data obtained by the three different methods correlated well. Besides the nature of the protein and the nanoparticle coating, the environment was shown to affect the interaction significantly. The strongest interaction was obtained with BSA and AgNPs in an acidic environment. Neutral and slightly alkaline conditions however, seemed to prevent the AgNP-protein interaction almost completely. Furthermore, the interaction of whey proteins with AgPURE™ W10 was found to be weaker compared to the interaction with the other two AgNPs under all conditions investigated.

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.

The Application of an Emerging Technique for Protein–Protein Interaction Interface Mapping: The Combination of Photo-Initiated Cross-Linking Protein Nanoprobes with Mass Spectrometry

PubMed Central

Ptáčková, Renata; Ječmen, Tomáš; Novák, Petr; Hudeček, Jiří; Stiborová, Marie; Šulc, Miroslav

2014-01-01

Protein–protein interaction was investigated using a protein nanoprobe capable of photo-initiated cross-linking in combination with high-resolution and tandem mass spectrometry. This emerging experimental approach introduces photo-analogs of amino acids within a protein sequence during its recombinant expression, preserves native protein structure and is suitable for mapping the contact between two proteins. The contact surface regions involved in the well-characterized interaction between two molecules of human 14-3-3ζ regulatory protein were used as a model. The employed photo-initiated cross-linking techniques extend the number of residues shown to be within interaction distance in the contact surface of the 14-3-3ζ dimer (Gln8–Met78). The results of this study are in agreement with our previously published data from molecular dynamic calculations based on high-resolution chemical cross-linking data and Hydrogen/Deuterium exchange mass spectrometry. The observed contact is also in accord with the 14-3-3ζ X-ray crystal structure (PDB 3dhr). The results of the present work are relevant to the structural biology of transient interaction in the 14-3-3ζ protein, and demonstrate the ability of the chosen methodology (the combination of photo-initiated cross-linking protein nanoprobes and mass spectrometry analysis) to map the protein-protein interface or regions with a flexible structure. PMID:24865487

P-MartCancer–Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets

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

Webb-Robertson, Bobbie-Jo M.; Bramer, Lisa M.; Jensen, Jeffrey L.

P-MartCancer is a new interactive web-based software environment that enables biomedical and biological scientists to perform in-depth analyses of global proteomics data without requiring direct interaction with the data or with statistical software. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access to multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium (CPTAC) at the peptide, gene and protein levels. P-MartCancer is deployed using Azure technologies (http://pmart.labworks.org/cptac.html), the web-service is alternativelymore » available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/) and many statistical functions can be utilized directly from an R package available on GitHub (https://github.com/pmartR).« less

Comparative analysis of Leishmania exoproteomes: implication for host-pathogen interactions.

PubMed

Peysselon, Franck; Launay, Guillaume; Lisacek, Frédérique; Duclos, Bertrand; Ricard-Blum, Sylvie

2013-12-01

Leishmaniasis is a vector-borne disease caused by the protozoa Leishmania. We have analyzed and compared the sequences of three experimental exoproteomes of Leishmania promastigotes from different species to determine their specific features and to identify new candidate proteins involved in interactions of Leishmania with the host. The exoproteomes differ from the proteomes by a decrease in the average molecular weight per protein, in disordered amino acid residues and in basic proteins. The exoproteome of the visceral species is significantly enriched in sites predicted to be phosphorylated as well as in features frequently associated with molecular interactions (intrinsic disorder, number of disordered binding regions per protein, interaction and/or trafficking motifs) compared to the other species. The visceral species might thus have a larger interaction repertoire with the host than the other species. Less than 10% of the exoproteomes contain heparin-binding and RGD sequences, and ~30% the host targeting signal RXLXE/D/Q. These latter proteins might thus be exported inside the host cell during the intracellular stage of the infection. Furthermore we have identified nine protein families conserved in the three exoproteomes with specific combinations of Pfam domains and selected eleven proteins containing at least three interaction and/or trafficking motifs including two splicing factors, phosphomannomutase, 2,3-bisphosphoglycerate-independent phosphoglycerate mutase, the paraflagellar rod protein-1D and a putative helicase. Their role in host-Leishmania interactions warrants further investigation but the putative ATP-dependent DEAD/H RNA helicase, which contains numerous interaction motifs, a host targeting signal and two disordered regions, is a very promising candidate. © 2013.

A Liquid Array Platform For the Multiplexed Analysis of Synthetic Molecule-Protein Interactions

PubMed Central

Doran, Todd M.; Kodadek, Thomas

2014-01-01

Synthetic molecule microarrays, consisting of many different compounds spotted onto a planar surface such as modified glass or cellulose, have proven to be useful tools for the multiplexed analysis of small molecule- and peptide-protein interactions. However, these arrays are technically difficult to manufacture and use with high reproducibility and require specialized equipment. Here we report a more convenient alternative comprised of color-encoded beads that display a small molecule protein ligand on the surface. Quantitative, multiplexed assay of protein binding to up to 24 different ligands can be achieved using a common flow cytometer for the readout. This technology should be useful for evaluating hits from library screening efforts, the determination of structure activity relationships and for certain types of serological analyses. PMID:24245981

ProteMiner-SSM: a web server for efficient analysis of similar protein tertiary substructures.

PubMed

Chang, Darby Tien-Hau; Chen, Chien-Yu; Chung, Wen-Chin; Oyang, Yen-Jen; Juan, Hsueh-Fen; Huang, Hsuan-Cheng

2004-07-01

Analysis of protein-ligand interactions is a fundamental issue in drug design. As the detailed and accurate analysis of protein-ligand interactions involves calculation of binding free energy based on thermodynamics and even quantum mechanics, which is highly expensive in terms of computing time, conformational and structural analysis of proteins and ligands has been widely employed as a screening process in computer-aided drug design. In this paper, a web server called ProteMiner-SSM designed for efficient analysis of similar protein tertiary substructures is presented. In one experiment reported in this paper, the web server has been exploited to obtain some clues about a biochemical hypothesis. The main distinction in the software design of the web server is the filtering process incorporated to expedite the analysis. The filtering process extracts the residues located in the caves of the protein tertiary structure for analysis and operates with O(nlogn) time complexity, where n is the number of residues in the protein. In comparison, the alpha-hull algorithm, which is a widely used algorithm in computer graphics for identifying those instances that are on the contour of a three-dimensional object, features O(n2) time complexity. Experimental results show that the filtering process presented in this paper is able to speed up the analysis by a factor ranging from 3.15 to 9.37 times. The ProteMiner-SSM web server can be found at http://proteminer.csie.ntu.edu.tw/. There is a mirror site at http://p4.sbl.bc.sinica.edu.tw/proteminer/.

Identification of Protein Complex Associated with LYT1 of Trypanosoma cruzi

PubMed Central

Lugo-Caballero, C.; Ballesteros-Rodea, G.; Martínez-Calvillo, S.; Manning-Cela, Rebeca

2013-01-01

To carry out the intracellular phase of its life cycle, Trypanosoma cruzi must infect a host cell. Although a few molecules have been reported to participate in this process, one known protein is LYT1, which promotes lysis under acidic conditions and is involved in parasite infection and development. Alternative transcripts from a single LYT1 gene generate two proteins with differential functions and compartmentalization. Single-gene products targeted to more than one location can interact with disparate proteins that might affect their function and targeting properties. The aim of this work was to study the LYT1 interaction map using coimmunoprecipitation assays with transgenic parasites expressing LYT1 products fused to GFP. We detected several proteins of sizes from 8 to 150 kDa that bind to LYT1 with different binding strengths. By MS-MS analysis, we identified proteins involved in parasite infectivity (trans-sialidase), development (kDSPs and histones H2A and H2B), and motility and protein traffic (dynein and α- and β-tubulin), as well as protein-protein interactions (TPR-protein and kDSPs) and several hypothetical proteins. Our approach led us to identify the LYT1 interaction profile, thereby providing insights into the molecular mechanisms that contribute to parasite stage development and pathogenesis of T. cruzi infection. PMID:23586042

Interaction of cellular proteins with BCL-xL targeted to cytoplasmic inclusion bodies in adenovirus infected cells.

PubMed

Subramanian, T; Vijayalingam, S; Kuppuswamy, M; Chinnadurai, G

2015-09-01

Adenovirus-mediated apoptosis was suppressed when cellular anti-apoptosis proteins (BCL-2 and BCL-xL) were substituted for the viral E1B-19K. For unbiased proteomic analysis of proteins targeted by BCL-xL in adenovirus-infected cells and to visualize the interactions with target proteins, BCL-xL was targeted to cytosolic inclusion bodies utilizing the orthoreovirus µNS protein sequences. The chimeric protein was localized in non-canonical cytosolic factory-like sites and promoted survival of virus-infected cells. The BCL-xL-associated proteins were isolated from the cytosolic inclusion bodies in adenovirus-infected cells and analyzed by LC-MS. These proteins included BAX, BAK, BID, BIK and BIM as well as mitochondrial proteins such as prohibitin 2, ATP synthase and DNA-PKcs. Our studies suggested that in addition to the interaction with various pro-apoptotic proteins, the association with certain mitochondrial proteins such as DNA-PKcs and prohibitins might augment the survival function of BCL-xL in virus infected cells. Copyright © 2015 Elsevier Inc. All rights reserved.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

PubMed Central

Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

2016-01-01

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

A modified Poisson-Boltzmann equation applied to protein adsorption.

PubMed

Gama, Marlon de Souza; Santos, Mirella Simões; Lima, Eduardo Rocha de Almeida; Tavares, Frederico Wanderley; Barreto, Amaro Gomes Barreto

2018-01-05

Ion-exchange chromatography has been widely used as a standard process in purification and analysis of protein, based on the electrostatic interaction between the protein and the stationary phase. Through the years, several approaches are used to improve the thermodynamic description of colloidal particle-surface interaction systems, however there are still a lot of gaps specifically when describing the behavior of protein adsorption. Here, we present an improved methodology for predicting the adsorption equilibrium constant by solving the modified Poisson-Boltzmann (PB) equation in bispherical coordinates. By including dispersion interactions between ions and protein, and between ions and surface, the modified PB equation used can describe the Hofmeister effects. We solve the modified Poisson-Boltzmann equation to calculate the protein-surface potential of mean force, treated as spherical colloid-plate system, as a function of process variables. From the potential of mean force, the Henry constants of adsorption, for different proteins and surfaces, are calculated as a function of pH, salt concentration, salt type, and temperature. The obtained Henry constants are compared with experimental data for several isotherms showing excellent agreement. We have also performed a sensitivity analysis to verify the behavior of different kind of salts and the Hofmeister effects. Copyright © 2017 Elsevier B.V. All rights reserved.

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

PubMed Central

Beleva Guthrie, Violeta; Masica, David L; Fraser, Andrew; Federico, Joseph; Fan, Yunfan; Camps, Manel; Karchin, Rachel

2018-01-01

Abstract The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure. PMID:29522102

Developing a Multiplexed Quantitative Cross-Linking Mass Spectrometry Platform for Comparative Structural Analysis of Protein Complexes.

PubMed

Yu, Clinton; Huszagh, Alexander; Viner, Rosa; Novitsky, Eric J; Rychnovsky, Scott D; Huang, Lan

2016-10-18

Cross-linking mass spectrometry (XL-MS) represents a recently popularized hybrid methodology for defining protein-protein interactions (PPIs) and analyzing structures of large protein assemblies. In particular, XL-MS strategies have been demonstrated to be effective in elucidating molecular details of PPIs at the peptide resolution, providing a complementary set of structural data that can be utilized to refine existing complex structures or direct de novo modeling of unknown protein structures. To study structural and interaction dynamics of protein complexes, quantitative cross-linking mass spectrometry (QXL-MS) strategies based on isotope-labeled cross-linkers have been developed. Although successful, these approaches are mostly limited to pairwise comparisons. In order to establish a robust workflow enabling comparative analysis of multiple cross-linked samples simultaneously, we have developed a multiplexed QXL-MS strategy, namely, QMIX (Quantitation of Multiplexed, Isobaric-labeled cross (X)-linked peptides) by integrating MS-cleavable cross-linkers with isobaric labeling reagents. This study has established a new analytical platform for quantitative analysis of cross-linked peptides, which can be directly applied for multiplexed comparisons of the conformational dynamics of protein complexes and PPIs at the proteome scale in future studies.

A liquid diffraction analysis of sarcoplasmic reticulum. I. Compositional variation.

PubMed Central

Brady, G W; Fein, D B; Harder, M E; Spehr, R; Meissner, G

1981-01-01

Intensities of x-ray scattering from a series of fragmented rabbit muscle sarcoplasmic reticulum (SR) samples have been measured over the range x = 0.05 to s = 0.25. By varying the relative concentrations of lipid and protein (chiefly the Mg++-dependent, Ca++- stimulated ATPase) in the membranes of this series, and by employing methods of analysis appropriate to the scattering from binary liquid mixtures, we have identified the separable contributions of protein and lipid, and the protein-lipid interaction contributions to the total scattering profiles. The shape of the protein term is consistent with scattering from a cylindrical ATPase particle 142 A in length and 35 A in diameter. These data imply that the dominant ATPase species is monomeric. The protein-lipid interaction term has been analyzed by a novel treatment based on a determination of the pair correlation function between the electrons of the protein molecule with the electrons of the lipid bilayer in terms of the asymmetry of the transbilayer disposition of the protein. Applied to our results, the analysis indicates a fully asymmetric disposition of ATPase, in which one end of the molecule is contiguous with either the lumenal or cytoplasmic surface of the bilayer. PMID:6111360

A cysteine-rich plant protein potentiates Potyvirus movement through an interaction with the virus genome-linked protein VPg.

PubMed

Dunoyer, P; Thomas, C; Harrison, S; Revers, F; Maule, A

2004-03-01

We have identified a cellular factor that interacts with the virus genome-linked proteins (VPgs) of a diverse range of potyviruses. The factor, called Potyvirus VPg-interacting protein (PVIP), is a plant-specific protein with homologues in all the species examined, i.e., pea, Arabidopsis thaliana, and Nicotiana benthamiana. The sequence of PVIP does not identify a specific function, although the existence of a "PHD finger" domain may implicate the protein in transcriptional control through chromatin remodeling. Deletion analysis using the yeast two-hybrid system showed that the determinants of the interaction lay close to the N terminus of VPg; indeed, the N-terminal 16 amino acids were shown to be both necessary and sufficient for the interaction with at least one PVIP protein. From a sequence comparison of different potyvirus VPg proteins, a specific amino acid at position 12 was directly implicated in the interaction. This part of VPg is distinct from regions associated with other functional roles of VPg. Through mutation of Turnip mosaic virus (TuMV) at VPg position 12, we showed that the interaction with PVIP affected systemic symptoms in infected plants. This resulted from reduced cell-to-cell and systemic movement more than reduced virus replication, as visualized by comparing green fluorescent protein-tagged wild-type and mutant viruses. Furthermore, by using RNA interference of PVIP in Arabidopsis, we showed that reduced expression of PVIP genes reduced susceptibility to TuMV infection. We conclude that PVIP functions as an ancillary factor to support potyvirus movement in plants.

Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network.

PubMed

Saha, Sovan; Sengupta, Kaustav; Chatterjee, Piyali; Basu, Subhadip; Nasipuri, Mita

2017-09-23

Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Proteomic analysis of soybean root exposed to varying sizes of silver nanoparticles under flooding stress.

PubMed

Mustafa, Ghazala; Sakata, Katsumi; Komatsu, Setsuko

2016-10-04

Silver nanoparticles (Ag-NPs) are excessively used as antibacterial agents; however, environmental interaction specifically with the plants remain uncertain. To study the size-dependent effects of Ag-NPs on soybean under flooding, a proteomic technique was used. Morphological analysis revealed that treatment with Ag-NPs of 15nm promoted soybean growth under flooding compared to 2 and 50-80nm. A total of 228 common proteins that significantly changed in abundance under flooding without and with Ag-NPs of 2, 15, and 50-80nm. Under varying sizes of Ag-NPs, number of protein synthesis related proteins decreased compared to flooding while number of amino acid synthesis related proteins were increased under Ag-NPs of 15nm. Hierarchical clustering identified the ribosomal proteins that increased under Ag-NPs of 15nm while decreased under other sizes. In silico protein-protein interaction indicated the beta ketoacyl reducatse 1 as the most interacted protein under Ag-NPs of 15nm while least interacted under other sizes. The beta ketoacyl reductase 1 was up-regulated under Ag-NPs of 15nm while its enzyme activity was decreased. These results suggest that the different sizes of Ag-NPs might affect the soybean growth under flooding by regulating the proteins related to amino acid synthesis and wax formation. This study highlighted the response of soybean proteins towards varying sizes of Ag NPs under flooding stress using gel-free proteomic technique. The Ag NPs of 15nm improved the length of root including hypocotyl of soybean. The proteins related to protein metabolism, cell division/organization, and amino acid metabolism were differentially changed under the varying sizes of Ag NPs. The protein synthesis-related proteins were decreased while amino acid metabolism-related proteins were increased under varying sizes of Ag NPs. The ribosomal proteins were increased under Ag NPs of 15nm. The beta ketoacyl reductase 1 was identified as the most interacted protein under varying sizes of Ag NPs. The mRNA expression level of beta ketoacyl reductase was up-regulated under Ag NPs of 15nm while its activity was decreased. These results suggest that the Ag NPs of 15nm improved the soybean growth under flooding stress by increasing the proteins related to amino acid synthesis and waxes formation. Copyright © 2016 Elsevier B.V. All rights reserved.

A small stem-loop structure of the Ebola virus trailer is essential for replication and interacts with heat-shock protein A8

PubMed Central

Sztuba-Solinska, Joanna; Diaz, Larissa; Kumar, Mia R.; Kolb, Gaëlle; Wiley, Michael R.; Jozwick, Lucas; Kuhn, Jens H.; Palacios, Gustavo; Radoshitzky, Sheli R.; J. Le Grice, Stuart F.; Johnson, Reed F.

2016-01-01

Ebola virus (EBOV) is a single-stranded negative-sense RNA virus belonging to the Filoviridae family. The leader and trailer non-coding regions of the EBOV genome likely regulate its transcription, replication, and progeny genome packaging. We investigated the cis-acting RNA signals involved in RNA–RNA and RNA–protein interactions that regulate replication of eGFP-encoding EBOV minigenomic RNA and identified heat shock cognate protein family A (HSC70) member 8 (HSPA8) as an EBOV trailer-interacting host protein. Mutational analysis of the trailer HSPA8 binding motif revealed that this interaction is essential for EBOV minigenome replication. Selective 2′-hydroxyl acylation analyzed by primer extension analysis of the secondary structure of the EBOV minigenomic RNA indicates formation of a small stem-loop composed of the HSPA8 motif, a 3′ stem-loop (nucleotides 1868–1890) that is similar to a previously identified structure in the replicative intermediate (RI) RNA and a panhandle domain involving a trailer-to-leader interaction. Results of minigenome assays and an EBOV reverse genetic system rescue support a role for both the panhandle domain and HSPA8 motif 1 in virus replication. PMID:27651462