Protein-Protein Interaction Network Analysis for a Biomarker Panel Related to Human Esophageal Adenocarcinoma
Background: Esophageal adenocarcinoma (EAC) is one of the mostlethal cancers in the world with a very poor prognosis. Identification of molecular diagnostic methods is an important goal. Since protein-protein interaction (PPI) network analysis is a suitable method for molecular assessment, in the present research a PPI network related to EAC was targeted. Material and Method: Cytoscape software and its applications including STRING DB, Cluster ONE and ClueGO were applied to analyze the PPI network. Result: Among 182 EAC-related proteins which were identified, 129 were included in a main connected component. Proteins based on centrality analysis of characteristics such as degree, betweenness, closeness and stress were screened and key nodes were introduced. Two clusters were determined of which only one was significant statistically. Gene ontology revealed 50 terms in three groups associated with EAC. Conclusion:The findings ...http://journal.waocp.org/article_53179.html
Frontiers | Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer |...
Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ...https://www.frontiersin.org/articles/10.3389/fphys.2017.00947/full
Publications | Programa de Prevención y Control del Cáncer
Sanz-Pamplona R, Berenguer A, Sole X, Cordero D, Crous-Bou M, Serra-Musach J, et al. Tools for protein-protein interaction network analysis in cancer research. Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2012;14(1):3-14. Abstract ...http://bioinfo.iconcologia.net/es/publications/author/1584
A protein interaction map for a better insight in cancer development
Researchers from the Institut Curie and from the Paris-based biotechnology company Hybrigenics announced today that they have built a protein-protein interaction map of the fruit fly, Drosophila melanogaster. This 'simple' model organism allows them to study a 'reference set' of proteins that includes most of those known to be involved in human cancer. Since proteins function in networks, the systematic identification of the physical interactions that occur between proteins will help understanding their biological function, and improve our capacity to intervene and, ultimately, to discover novel, more specific therapeutic targets. Their results are published in the March 1st issue of Genome Research ...http://www.innovations-report.com/html/reports/life-sciences/report-41033.html
"Building protein interaction maps for Down's syndrome." by K Gardiner, M T. Davisson et al.
Now that the complete sequences for human chromosome 21 and the orthologous mouse genomic regions are known, reasonably complete, conserved, protein-coding gene catalogues are also available. The central issue now facing Down's syndrome researchers is the correlation of increased expression of specific, normal, chromosome 21 genes with the development of specific deficits in learning and memory. Because of the number of candidate genes involved, the number of alternative splice variants of individual genes and the number of pathways in which these genes function, a pathway analysis approach will be critical to success. Here, three examples, both gene specific and pathway related, that would benefit from pathway analysis are discussed: (1) the potential roles of eight chromosome 21 proteins in RNA processing pathways; (2) the chromosome 21 protein intersectin 1 and its domain composition, alternative splicing, protein ...http://mouseion.jax.org/stfb2000_2009/954/
Charting the Landscape of Tandem BRCT Domain-Mediated Protein Interactions | Science Signaling
Eukaryotic cells have evolved an intricate system to resolve DNA damage to prevent its transmission to daughter cells. This system, collectively known as the DNA damage response (DDR) network, includes many proteins that detect DNA damage, promote repair, and coordinate progression through the cell cycle. Because defects in this network can lead to cancer, this network constitutes a barrier against tumorigenesis. The modular BRCA1 carboxyl-terminal (BRCT) domain is frequently present in proteins involved in the DDR, can exist either as an individual domain or as tandem domains (tBRCT), and can bind phosphorylated peptides. We performed a systematic analysis of protein-protein interactions involving tBRCT in the DDR by combining literature curation, yeast two-hybrid screens, and tandem affinity purification coupled to mass spectrometry. We identified 23 proteins containing conserved BRCT domains and generated ...http://stke.sciencemag.org/content/5/242/rs6
visualcomplexity.com | Yeast Protein Interaction Map in 3D
VisualComplexity.com is a unified resource space for anyone interested in the visualization of complex networks. The project's main goal is to leverage a critical understanding of different visualization methods, across a series of disciplines, as diverse as Biology, Social Networks or the World Wide Web.http://www.visualcomplexity.com/vc/project_details.cfm?id=121&index=121&domain=
Quantitative analysis of protein interaction network dynamics in yeast | Molecular Systems Biology
Here, we described the multiplex in vivo measurement of 1,379 protein-protein interactions in 14 environmental conditions, to our knowledge the most extensive direct study of how protein interaction networks respond dynamically to extrinsic environmental perturbations. The most striking finding was the prevalence of dynamic binary complexes. More than half of the PPIs we considered (757 of 1,379) responded to at least one perturbation. The environmental perturbations that yielded the largest number of changes relative to our reference condition were respiratory growth in ethanol, heat shock, oxidative stress, and DNA damage. That these responses were the most profound might have been expected, as these conditions are likely to have been frequently experienced in the evolutionary history of yeast (Gasch & Werner‐Washburne, 2002; Gasch, 2007), allowing for selection and maintenance of a complex adaptive regulatory strategy. We ...http://msb.embopress.org/content/13/7/934?ijkey=7ddc8bb4c40e2d4d0e10d5fedf10294b796eb64a&keytype2=tf_ipsecsha
ARNIE | Construction of a large extracellular protein interaction network and its resolution by spatiotemporal expression...
An online database that integrates the extracellular protein interaction network. ARNIE allows users to browse the network by clicking on individual proteins, or by specifying the spatiotemporal parameters using the drop-down menus. Clicking on connector lines will allow users to compare stage-matched expression patterns for genes encoding interacting proteins. Additionally, users can rapidly search for their genes in the network using the BLAST server provided.https://omictools.com/avexis-receptor-network-with-integrated-expression-tool
Predicting Protein Functions from Protein Interaction Networks: Medicine & Healthcare Book Chapter | IGI Global
Predicting Protein Functions from Protein Interaction Networks: 10.4018/978-1-60566-398-2.ch012: Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way ofhttps://www.igi-global.com/chapter/predicting-protein-functions-protein-interaction/5566
Towards the identification of protein complexes and functional modules by integrating PPI network and gene expression data |...
Recent advances in biotechnology have resulted in a large amounts of protein-protein interaction (PPI) data. Modeling and clustering PPI networks with simple graphs makes it possible for us to understand the basic components and organization of cell machinery from the network level. One of the most important challenges in the post-genomic era is to analyze the complex networks of PPIs and detect protein complexes or functional modules from them. Over the past decade, many computational methods have been proposed for clustering PPI networks, such as G-N , MCODE, RNSC, LCMA, DPClus , MoNet , IPCA , COACH , and SPICi .. While significant progress has been made in computational methods, there are two major challenges in clustering PPI networks. One of the challenges is that the conventional clustering methods generally considered the PPI network as a static graph and overlooked the dynamics inherent within these ...https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-109
Unveiling Protein Functions through the Dynamics of the Interaction Network
Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein ...http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0017679
Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information -...
Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach fohttp://pubs.rsc.org/en/content/articlelanding/2016/mb/c6mb00599c
IJMS | Free Full-Text | Novel Strategies for Drug Discovery Based on Intrinsically Disordered Proteins (IDPs) | HTML
Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the ...http://www.mdpi.com/1422-0067/12/5/3205/htm
RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks | BMC Bioinformatics | Full Text
In recent years, protein-protein interaction (PPI) datasets have been recognized as important resources to elucidate various biological processes and cellular mechanisms. The prediction of protein complexes from PPIs (see, for example, survey papers [1-3]) is one of the most challenging inference problems from PPIs because protein complexes are essential entities in the cell. Proteins' functions are manifested in the form of a protein complex. Thus, the identification of protein complexes is necessary for the precise description of biological systems.. For protein complex prediction, many computational methods have been proposed, which were directly or indirectly designed based on the observation that densely connected subgraphs, or clusters of proteins, of a whole PPI network often overlap with known complexes. This observation is often ...https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1920-5
PT11: Domain-oriented edge-based alignment of protein interaction networks (ISMB 2009) - the mind wobbles
Xin Guo Introducing... the DOMain-oriented Alignment of Interaction Networks (DOMAIN). Previous paradigms include the node-then-edge-alignment paradigm and direct-edge-alignment paradigm. In the latter, interactions are more likely to be conserved. Many studies have suggested that direct PPIs can be mediated by interactions of their domains. Their method follows the direct-edge-alignment paradigm. In the method: try to…https://themindwobbles.wordpress.com/2009/06/29/domain-oriented-edge-based-alignment-of-protein-interaction-networks/
An automated method for finding molecular complexes in large protein interaction networks | Springer for Research & Development
Background Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial...https://rd.springer.com/article/10.1186/1471-2105-4-2
Predicting the Fission Yeast Protein Interaction Network | G3: Genes | Genomes | Genetics
SVM and RF contributed 15 and 10 unique predictions, respectively, that were confirmed experimentally. Thus, the two methods were somewhat complementary and, if used together, may provide better coverage of true predictions. On the other hand, using the overlap of predictions from both SVM and RF provides a more conservative and, hence, more reliable list of protein interactions that could be used as a starting point for further investigations.. The Cbf11 interactors predicted were significantly enriched for the experimentally determined targets, both in the case of SVM (Fisher's exact test, P = 10−14), RF (Fisher's P = 10−28), and in the overlap of the two methods (Fisher's P = 10−28). So far, there are no known genetic interactions for Cbf11 and no functional interactions due to the protein not being conserved in budding yeast. For this reason, our method for predicting its physical association partners can only be ...http://www.g3journal.org/content/2/4/453
Predicting the Fission Yeast Protein Interaction Network | G3: Genes | Genomes | Genetics
SVM and RF contributed 15 and 10 unique predictions, respectively, that were confirmed experimentally. Thus, the two methods were somewhat complementary and, if used together, may provide better coverage of true predictions. On the other hand, using the overlap of predictions from both SVM and RF provides a more conservative and, hence, more reliable list of protein interactions that could be used as a starting point for further investigations.. The Cbf11 interactors predicted were significantly enriched for the experimentally determined targets, both in the case of SVM (Fisher's exact test, P = 10−14), RF (Fisher's P = 10−28), and in the overlap of the two methods (Fisher's P = 10−28). So far, there are no known genetic interactions for Cbf11 and no functional interactions due to the protein not being conserved in budding yeast. For this reason, our method for predicting its physical association partners can only be ...http://www.g3journal.org/content/2/4/453.long
A human functional protein interaction network and its application to cancer data analysis | Genome Biology | Full Text
Increasingly, human diseases and other traits are being probed by genome-wide screens. For example, several recent papers [10-14] describe genome-wide screening efforts to identify somatic mutations in several cancer types. Placing such lists of genes or proteins into a pathway context can yield information on the relationships among these genes and has the potential to generate hypotheses about the mechanism(s) linking these genes to phenotypes.. Reliable pathway databases are essential for such an analysis, but because of the effort needed to curate pathways is so human-intensive, even the largest pathway database has a SwissProt coverage of under 20% (Table 2). In this report, we describe how we have integrated several large-scale experimental data sets to build and train a machine-learning system that identifies potential 'functional interactions' among pairs of human proteins. We have combined the FIs predicted by this classifier with the curated ...https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-5-r53
Disentangling the proteome: Re-evaluations of Topological Insights from Yeast Protein Interaction Neworks | Linkalab
To understand living cells one must study them as systems rather than as a collection of individual molecules. The abstract representation of intracellular systems as 'networks' is fruitful, because it provides the ability to study these systems as a whole by ignoring details of individual components, but retaining the complexity of the interactions. This chapter will review the discoveries made through application of approaches from 'the science of complex networks' to Protein Interaction Networks, i.e. undirected networks in which the nodes represent proteins, and pairs are connected by edges if the proteins physically interact. Over the last decade the experimental techniques for measuring protein interactions has been highly improved and large numbers of new protein interactions have been elucidated. Therefore, along with the reviewed concepts and ...http://www.linkalab.it/it/publications/disentangling-proteome-re-evaluations-topological-insights-yeast-protein-interaction
Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of...
Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants: 10.4018/IJSDA.2015100103: Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found thehttps://www.igi-global.com/article/extracting-biological-significant-subnetworks-from-protein-protein-interactions-induced-by-differentially-expressed-genes-of-hiv-1-vpr-variants/136997
Dipòsit Digital de la Universitat de Barcelona: Network biology identifies novel apoptosis-related proteins and synergistic...
eng] Breast cancer is a very heterogeneous disease with a poor prognostic outcome, largely due to its resistance to current cancer therapies. The balance between cell proliferation and apoptosis plays a critical role in determining the overall growth or regression of the tumor in response to treatments. Hence, identifying treatments involved in apoptosis resistance is essential in order to find new therapeutic approaches. The heterogeneity of cancer is rarely due to abnormalities in single genes, but rather reflects the discontinuation of complex intercellular processes. Therefore, a useful way to describe and analyse this heterogeneity is the use of systems biology. This approach is based on the study of the interactions between the elements of a given system with the aim to understand its properties. Particularly, the use of protein-protein interaction networks gives a broader perspective of protein environment without ...http://diposit.ub.edu/dspace/handle/2445/104086?mode=full
A genetic algorithm for optimizing subnetwork markers for the study of breast cancer metastasis - IEEE Conference Publication
The combined use of gene expression profiles and protein-protein interaction networks has shown remarkable successes in the prediction of breast cancer methttp://ieeexplore.ieee.org/document/6022270/authors?reload=true
Towards Inter- and Intra- Cellular Protein Interaction Analysis: Applying the Betweenness Centrality Graph Measure for Node...
Towards Inter- and Intra- Cellular Protein Interaction Analysis: Applying the Betweenness Centrality Graph Measure for Node Importancehttp://nparc.nrc-cnrc.gc.ca/eng/view/object/?id=3eb6f465-0ee6-4d69-a072-aeeadc9e6598